Jekyll2021-06-12T13:28:17-04:00https://mcwoods.online/industrialresearch/feed.xmlThe Industrial Research PodcastTips, tricks and techniques used by software industry researchers to attempt to predict future trends.Placing Empirical Research into the Roadmap2020-05-18T06:00:00-04:002020-05-18T06:00:00-04:00https://mcwoods.online/industrialresearch/podcast/episode/2020/05/18/Placing-Empirical-Research-into-the-Roadmap<p>It is so easy to get carried away with a new technology. It can be so much fun playing with it, learning about how it works and just trying it out that we can often forget why we are investigating it in the first place. That is why it is important to have a plan, a key set of criteria with which to assess the technology you are playing with. This is the 3rd phase of the research roadmap process.</p>
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<p>So far, we’ve covered a lot of ground. We’ve introduced the research roadmap process and described the first phase in detail, working out how to create a vision of the future and how global trends can affect and help predict that. In the second phase we showed how these visions of the future can be translated into open questions. We even saw how these open questions resolve themselves into three basic types of problems:</p>
<ol>
<li>Problems that are already solved</li>
<li>Where there are one or more solutions possible</li>
<li>Where there is no solution at all</li>
</ol>
<p>Obviously, if there is already a well-known solution to the problem we can simply move on and study the remaining questions. If you recall both Edison and Swan faced a problem with creating a vacuum for their light bulb; both used a solution which had already been produced by a German inventor, the Sprengel pump. In a similar way, if there is a workable solution then move on. The value is in addressing the more challenging questions.</p>
<h1 id="address-the-challenging-questions">Address the Challenging Questions</h1>
<p>In this blog post we are going to cover how we structure our research to address the latter two bullet points; those cases where there are multiple solutions or the rare instances when there are no solutions at all.</p>
<h1 id="general-approach-to-not-getting-distracted">General Approach to Not Getting Distracted</h1>
<p>Keeping focused on why we are doing the research is essential. This is a problem for a lot of researchers, so much so that Dr Peyton-Jones, who is a Microsoft Researcher based in Cambridge, has shared a slide deck he’s prepared for fellow researchers to help address this issue (which I’ll talk more about further down).</p>
<p>Within the academic research world, the major output of any research will be an academic paper. Academic papers are often used as a <a href="https://www.irishtimes.com/news/science/eleven-researchers-in-irish-universities-named-among-world-s-top-3-000-1.1850669">measure of success</a>. Researchers and their institutions are often evaluated on the number of papers they have published and the relevance of their research is assessed by the number of citations their papers receive from other research papers. These same metrics are also used by government agencies; for instance SFI (<a href="https://en.wikipedia.org/wiki/Science_Foundation_Ireland">Science Foundation Ireland</a>) uses these metrics to measure the success of the academic intuitions within the country.</p>
<p>Most researchers approach research in the following way:</p>
<ol>
<li>Define research direction / idea / questions to investigate</li>
<li>Do investigation</li>
<li>Write up results in a paper</li>
</ol>
<p>Dr Peyton-Jones suggests we flip the last two steps and focus on writing the paper first, then conducting the research. Why? – Well, because when we are writing the paper, we are forced to keep ourselves focused. Academic papers usually follow a standard structure; context and background research, problem presentation, experiment definition – solution, results and conclusion. By working with this structure in mind we are forced to answer these questions and keep ourselves focused.</p>
<p>However, during the investigation, it is so easy to discover something new along the way and find your time and research spinning off in a direction you hadn’t originally intended.</p>
<h1 id="evaluating-multiple-solutions">Evaluating Multiple Solutions</h1>
<p>As I mentioned in the last post, when there are multiple solutions available the empirical research becomes focused on technology selection. It is often the case that technology originally built for one purpose can be used for a different purpose. Naturally this can produce a situation where the technology is tailored for an original set of criteria and doesn’t exactly match the criteria for the problem we are currently addressing. Typically, when we see that there are multiple possible solutions to one of the problems, we’ve encountered it is because the candidate solutions have come from adjacent industries or problems.</p>
<h2 id="technical-evaluation">Technical Evaluation</h2>
<p>As always it is important to have defined the criteria for the experiment. To do this, it is necessary to identify which aspects of the technologies you are evaluating are important to the problem being addressed. If you recall in the <a href="https://mcwoods.online/industrialresearch/podcast/episode/2020/04/17/Back-to-the-research-roadmapl.html">previous post</a>, I talked about defining the questions, well here we need to refer back to the questions and refine them to produce a qualifying metric. Often this requires going back to the scenarios and looking for additional detail.</p>
<p>Let’s take an Internet of Things example. Imagine selling a smart temperature sensor to a customer. They place this sensor in their home and connect it to their home Wi-Fi. Many of us have done this with Google Home, Amazon Alexa or even Smart TVs. Typically, these devices reach out to a server in the cloud when we interact with them. But we need our smart sensor to work the other way - we reach out to it from the cloud. Typically, this type of communication is blocked so we need some way around this. There are a whole range of solutions and each has its “pros” and “cons” and, in order to evaluate them, we need to focus on the set of “pros” and “cons” we care about the most.</p>
<p>In the example above this could include factors like:</p>
<ul>
<li>The load on the cloud server</li>
<li>The amount of additional (non-application) data that is transferred</li>
<li>The latency (time from cloud requesting information from the sensor and the sensor replying)</li>
<li>The goodput – the speed of throughput of application specific data</li>
</ul>
<p>In this case we would need to go back to the original scenario and really understand what problem the technology in the scenario was addressing or what benefit it was providing. This will point us to the key metrics we need to monitor. A sensor deployed to monitor environmental temperature is unlikely to see sudden, dramatic shifts in the temperature it monitors. However, a temperature sensor deployed on say a gas furnace very much might. If the sensor is being used as part of a fire alarm then the time from the sensor observing a spike in temperature and the alarm going off is important, so latency would key. Only once we’ve really delved into the details can we determine the key metrics</p>
<h2 id="filtering-the-solutions">Filtering the Solutions</h2>
<p>The technical merits of a solution also need to be considered together with the commercial merits of a solution. This is particularly true when Open Source software is concerned. The solutions we propose, particularly those that originate outside of our organisation, need to be commercially deployable. Imagine that your livelihood and that of your family relied on the technology you’re suggesting. If you want your organisation to invest in your proposal then the following is what is going to happen.</p>
<h3 id="commercial-requirements-and-open-source-software">Commercial Requirements and Open Source Software</h3>
<p>For any organisation, taking on a commitment to use any piece of software is a risk. Imagine starting to use an open source library only to discover that the library is no longer maintained and there is a security flaw within it. These are the types of risks that organisations need to avoid and to do this we normally look for a set of qualifying points:</p>
<ul>
<li>How long has the project been running?</li>
<li>Which other organisations are using it?</li>
<li>Has it been commercially deployed already?</li>
<li>What is the frequency of updates to the project?</li>
</ul>
<p>Obviously, some emerging projects will not meet all the criteria, in which case an organisation may take on the maintenance of a project or seek to fund someone who already is. This depends on how critical the project is to the organisation.</p>
<h1 id="creating-a-solution">Creating a Solution</h1>
<p>As I mentioned in <a href="https://mcwoods.online/industrialresearch/podcast/episode/2020/04/17/Back-to-the-research-roadmapl.html">my prior post</a>, there are times when no suitable solution can be in found, in these cases it is necessary to examine the possibility of creating your own solution. Coming from a software development background this used to be my default go to response – “Yes, let’s build it!”. But the cost of software development isn’t just in the time it takes to built it, but the lifetime cost to maintain and support that software across the duration of a products lifecycle.</p>
<h2 id="search-search-and-search-again">Search, Search and Search Again</h2>
<p>For us to have arrived at the decision that there is no other viable solution available, implies that we have done our homework and looked at alternative solutions. If you can’t find an alternative, ask around - ask colleagues or on message boards. The chances are that if your organisation needs a solution someone else may need it too. I highly recommend that you double check; even if you uncover a solution that doesn’t quite match your needs, you can learn from what others have done.</p>
<h2 id="creating-a-specification">Creating a Specification</h2>
<p>Before designing the solution be a specific as you can. If you’ve been searching and comparing existing solutions you should have these details to hand; if not, now is the time to go and research them. What does the solution need to do? – Precisely. If we think back to our temperature sensor example, what should our targeted data transfer rate be? How many bytes per second? What should the goodput be? How many bytes per second could that be? How long is the acceptable latency in milliseconds?</p>
<p>It is important to dig out all these details before embarking the creation of a solution.</p>
<h1 id="looping-back-into-the-research-roadmap">Looping Back into the Research Roadmap</h1>
<p>The results of any experiment, or attempt to create a solution, provide important information which can be fed back into the research roadmap.</p>
<p><img src="https://mcwoods.online/industrialresearch/assets/images/roadmap-process-overview.png" alt="Research Roadmap Process" /></p>
<p>While we as industrial researchers are trying to find working, sellable solutions to problems, it is sometimes the case that our experiments will disprove a hypothesis, or, that attempts to create a solution result in an unsuccessful effort. That is all valuable data. Whether the experiment or solution creation effort showed a technology was a perfect fit, a close fit, or not a fit at all, then we should feed that information back into our vision of the future. This helps refine and update the vision of the future and the path it will take. While the technology we study or attempt to create may not fit our needs, other advances in the future may mean that we can revisit this at another time. But for now, we need to update the vision of the future and consider an alternative vision; a tweaked and updated one.</p>
<p>This is the last in the mini-series explaining the research roadmap process. I hope you’ve enjoyed it and found it interesting. If you’d like to read or hear more, or have questions, please don’t hesitate to reach out to me via <a href="https://twitter.com/mcwoods">twitter @mcwoods</a>, or <a href="https://www.linkedin.com/in/woodsmc/">LinkedIn</a>.</p>It is so easy to get carried away with a new technology. It can be so much fun playing with it, learning about how it works and just trying it out that we can often forget why we are investigating it in the first place. That is why it is important to have a plan, a key set of criteria with which to assess the technology you are playing with. This is the 3rd phase of the research roadmap process.Back to the Research Roadmap2020-04-17T06:00:00-04:002020-04-17T06:00:00-04:00https://mcwoods.online/industrialresearch/podcast/episode/2020/04/17/Back-to-the-research-roadmapl<p>The last three posts focused on predicting the future. That is only one part of The Research Roadmap. It is time to get back to the research and see how predicting the future fits into the Roadmap Process.</p>
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<h1 id="research-roadmap-process-recap">Research Roadmap Process Recap</h1>
<p>It seems like ages ago when I first introduced the Research Roadmap Process. The process is designed to ensure that we deliver research results of value. The roadmap has three key phases:</p>
<ul>
<li>Phase 1: What Problem Should We Research?</li>
<li>Phase 2: What solutions can we propose to these questions?</li>
<li>Phase 3: What experiments do we run?</li>
</ul>
<h1 id="phase-1-what-problem-should-we-research">Phase 1: What Problem Should We Research</h1>
<p>We’ve now covered this phase in depth. It is our exploratory research, where we discover what questions we should research. It is based on creating a vision of the future. If your organisation doesn’t already have a definition, then you can create one. We can use some of the common practices that futurists use to create a vision of the future. A good guide is the 6-step process we covered in the latest post:</p>
<ol>
<li>Trend Prediction: Gather Data</li>
<li>Trend Prediction: Data Analysis and Trend Spotting</li>
<li>Trend Prediction: Culling the unlikely Trends</li>
<li>Trend Timing: Calculate a velocity for a trend based on missing elements preventing adoption</li>
<li>Scenario Writing: Document Multiple Visions of the Future</li>
<li>Critique the Scenarios: Validate that Scenarios, combine and improve them</li>
</ol>
<h2 id="chasing-the-horse">Chasing the Horse</h2>
<p>There is one final tip I’d like to give before we leave our future prediction. The truth about any trend, is that if you study it closely enough you should be able to identify the core driver - the “need” behind any push for a solution or a technology. Once you’ve identified that you’ll be able to make more accurate predictions on what is going to happen next.</p>
<p>That sounds pretty obtuse, I know, but let me give you an example and a famous one at that. Henry Ford is often <a href="https://hbr.org/2011/08/henry-ford-never-said-the-fast">misquoted as saying</a> :</p>
<blockquote>
<p>“<em>If I had asked people what they wanted, they would have said faster horses.”</em></p>
</blockquote>
<p>This line is often used in articles about how customers cannot articulate their need for innovation. Henry Ford didn’t invent the motor car; it had been around for some time. However, what this misquote does point to is the understanding of the core need - that people wanted transportation, an ability to get themselves and their goods from point A to point B conveniently and at a low cost. Anything else that could address the core need would work. The motorised carriage, or motorcar, was one possible solution.</p>
<p>In fact, in his autobiography Henry Ford actually describes the vision of the future he created:</p>
<blockquote>
<p><em>“I will build a motor car for the great multitude. It will be large enough for the family but small enough for the individual to run and care for. It will be constructed of the best materials, by the best men to be hired, after the simplest designs that modern engineering can devise. But it will be so low in price that no man making a good salary would be unable to own one – and enjoy with his family the blessing of hours of pleasure in God’s great open spaces.” –</em> <a href="https://www.entrepreneur.com/article/282218"><em>Henry Ford</em></a></p>
</blockquote>
<p><img src="/industrialresearch/assets/images/model-t.jpg" alt="model-t" /></p>
<p><em><a href="https://www.flickr.com/photos/jacksnell707/11820638795/in/photolist-j1xS6a-LP92dx-gjs9Vu-ugWTJH-p7goBF-j1BMaS-xNhXR6-7R4VpK-cK8uZJ-ad8Uve-pUBiFY-xNbnrN-nRgVju-6wrfr-dSCDr8-eZcvLL-oSqA2w-kmy6i3-q9XSQ3-qcUPBX-pmMLQ3-yKY9CX-p7V2zU-hVo2oN-p34kxw-p9Dfer-phuhEE-84kspH-awpEUr-fNrwpv-fLVCUF-fNnn8k-brFTCr-fNJ57j-fNpDMk-uWW6Zf-6VQHnL-fLFgQH-fNFqnS-fNuHVw-GYpVbv-fNqkVV-Rok5Ld-fNDkuo-fLWL9C-fLWimN-fLDybe-GLuP5d-fNmFqz-GsCziS">Ford Model-T Photo</a> by Jack Snell, Used under <a href="https://creativecommons.org/licenses/by-nd/2.0/">creative commons</a>.</em></p>
<h2 id="the-questions">The Questions</h2>
<p>With any vision of the future, there will be a set of problems to be addressed, things that are preventing that future from becoming a reality. For Ford this wasn’t the creation of the motorcar, it already existed, the main driving question was how to manufacture the car at a cost-effective price.</p>
<p>If we look at Ford’s main problem; “How do I produce a cheaper car?”. We can break that initial question into several others:</p>
<ul>
<li>How can I reduce the bill of materials?</li>
<li>How can I reduce the time (and effort) to manufacture the car?</li>
</ul>
<h1 id="phase-2-what-solutions-can-we-propose">Phase 2: What Solutions can we Propose</h1>
<p>It is actually really unusual to need to create a completely new concept / product / product category from scratch. There is almost always a range of existing solutions to the problems we’ve identified in the creation of our vision of the future.</p>
<p>One of the first phases of any PhD, and many supervisors will ask this of their students, is to conduct a literature review. Once you’ve identified the question you want to address in your PhD, determine with clarity what work has already been conducted in this area, and what solutions, if any exist for the problem you’ve identified. This is also, probably, one of the most boring stages of a PhD. Rather than working on an exciting new idea, you are reading a lot of publications and reviewing a lot of past work. This phase resembles the literature review - taking each question in turn and then reviewing the work done in that area.</p>
<p>There is no need for us to write and publish a literature review and we don’t need to spend the year or more a PhD student may spend on this. The goal of this phase is to look more deeply at the questions we need to address, to discover if there are other solutions available and what work, if any needs to be done in that area.</p>
<h2 id="how-the-questions-get-addressed">How the Questions Get Addressed</h2>
<p>There are number of places we can investigate during our solution search:</p>
<ul>
<li>Academic publications</li>
<li>Start-up companies</li>
<li>Other adjacent industries</li>
<li>Existing products or services</li>
<li>For the software / IT industry:
<ul>
<li>Open source software and the community</li>
<li>Forms and message boards</li>
</ul>
</li>
</ul>
<h2 id="the-types-of-answers-you-get">The Types of Answers You Get</h2>
<p>Broadly speaking answers fall into one of three categories:</p>
<ol>
<li>It is already solved</li>
<li>There are one or more possible solutions</li>
<li>There is no solution at all</li>
</ol>
<h3 id="it-is-already-solved">It is already solved</h3>
<p>We are all human, and no one knows the answer to everything. Often, when we sketch out the vision of the future, we’ll see questions which look to us as if they have never been solved, but, upon closer inspection we can find a whole bucket load of solutions. Usually this is because the technology or question area we are looking at is outside our area of expertise.</p>
<p>Charlie Stross wrote a fantastic Sci-Fi Book called <a href="https://www.amazon.com/Halting-State-Charles-Stross-ebook/dp/B000W9180A/ref=sr_1_1?dchild=1&keywords=halting+state&qid=1586953816&sr=8-1">halting state</a>. In it he depicts a future in which everyone owns a headset which can connect to each other. Games and applications run across these headsets with no reliance on any central infrastructure. So, this means, no cellular network, no cloud, no AWS or Google cloud service. But, for this vision of the future to work, we’d need a technology which allows these devices to talk to each other without a cellular network. When I first considered this, I thought that would a huge issue to address. But when you look at the literature, and existing products and concepts, you can see a plethora of different solutions, including a collection of “mesh networking architectures” inspired by defence research.</p>
<p>Often what we perceive as an open question, an issue that needs a solution, actually isn’t an open question at all; occasionally there are well defined solutions which meet our specific needs. If there is a perfect fit for our problem, we can select it and move on. However, this is pretty rare. It is more likely that we will have multiple solutions each of which could be a match.</p>
<h3 id="there-are-one-or-more-possible-solutions">There are one or more possible solutions</h3>
<p>This is the most likely type of answer to get. When you dig into a problem you often see that the issue is similar to problems in other adjacent fields, or that similar, but rarely the exact same problems, have actually been addressed in the past. The solutions to these similar problems become candidate solutions for us. In this case our next step, see Phase 3 below, becomes one of technology selection. We need some way to evaluate all of these solutions and to determine the best fit.</p>
<h3 id="there-is-no-solution-at-all">There is no solution at all</h3>
<p>This is the most unlikely situation. Even in the first post introducing the research roadmap process when we covered Edison and his quest for a longer lasting filament for his lightbulb, we discovered that Edison he had many materials to choose from. He didn’t need to invent a brand-new material. However, there are rare occasions when a solution just doesn’t exist, or an existing solution is not the perfect match. It is clear that here we need to decide what to do. There are three options:</p>
<ol>
<li>Build: We invest the time and create our own new technology and solution.</li>
<li>Buy: We outsource the construction of the new technology.</li>
<li>Wait: We “keep our powder dry” and wait for the technology to be developed.</li>
</ol>
<p>The decision we make is determined by our core business need. The classic build / buy is based on return on investment and strategic importance the technology is to the business or organisation you work for. The wait option, well that’s more about understanding the dynamics of the market place.</p>
<h3 id="waiting-is-ok">Waiting is OK</h3>
<p>Generally, the hard work we’ve invested in understanding the trends, and industry we are in, and the vision of the future we want to create will have given us this deep knowledge base. But this can also show us which organisations or communities are working on what technologies. Occasionally we will find that a solution to the problems we need to address doesn’t exist right now, but we can also see that others are working on the same problems. It may be strategically important that we get access to those solutions, but we may not actually need to invent them ourselves. In this case we can wait. Alternatively, we can assist these technology creators by offering to be a use case for their solutions. Here working with other organisations via cooperative research agreements might be the way to go. I will discuss more about cooperative research in a future post.</p>
<h1 id="what-did-ford-do">What did Ford Do</h1>
<p><img src="/industrialresearch/assets/images/800px-Henry_ford_1919.jpg" alt="800px-Henry_ford_1919" /></p>
<p>Henry Ford had two big questions to answer to help reduce the cost of his car:</p>
<ul>
<li>How can I reduce the bill of materials?</li>
<li>How can I reduce the time (and effort) to manufacture the car?</li>
</ul>
<p>We most commonly hear about Ford and time and motion studies about how to reduce the labour required to make a car, but actually this was one of the last improvements. The initial improvements were on simplifying the design, removing components and reducing the complexity of the solution. He focused on one base model, the Ford Model T. Once this was achieved Ford went on to change the construction material, opting for a new steel, vanadium steel, which was stronger and lighter than the steel he had previously used. It was this simplification theme that continued in time and materials studies and how to simplify and streamline the construction process.</p>
<h2 id="from-ford-to-apple">From Ford to Apple</h2>
<p>I know that the Ford Model T is a world away from the computing and technology industry I work in today, but there are lessons here. The simplification of design is a key one. It helps, in general to reduce the cost of both software and hardware.</p>
<p>I started my career as a software developer and it is somewhat ironic that as a developer all you want to do is to create new software, but each line of code you write, each line that goes into production is an extra burden on you. It is an extra weight, an extra task, because each line of code must be maintained, tested, bug tracked and supported.</p>
<p><a href="https://en.wikipedia.org/wiki/Steve_Wozniak">Steve Wozniak</a>, the cofounder of Apple in his <a href="https://www.amazon.com/iWoz-Invented-Personal-Computer-Along/dp/B000LMPDMW/ref=sr_1_1?crid=1HLJBGTA4EFGS&dchild=1&keywords=steve+wozniak&qid=1587040917&s=books&sprefix=steve+wo%2Caps%2C148&sr=1-1">autobiography “iWos”</a> talks a lot about his passion for electronics and reducing the complexity of a circuit design. He’d work hard to reduce the chip count to make the design simpler. This leads directly to cheaper construction, and a lower price point.</p>
<p><img src="/industrialresearch/assets/images/800px-Steve_Wozniak,_November_2018_Michael_Fortsch_CC.jpg" alt="800px-Steve_Wozniak,_November_2018_Michael_Fortsch_CC" /></p>
<p>Photo of <a href="https://en.wikipedia.org/wiki/Steve_Wozniak">Steve Wozniak</a>, taken by <a href="https://commons.wikimedia.org/w/index.php?title=User:GameGuru&action=edit&redlink=1">Michael Fortsch</a> and used under <a href="https://creativecommons.org/licenses/by-sa/4.0">creative commons.</a></p>
<h1 id="next-time-phase-3">Next Time: Phase 3</h1>
<p>In the next post I’ll be looking at Phase 3 – determining the experiments we need to run. But in the meantime, I’ve got some homework for you. Is there something in your organisation or daily organisation that can be simplified, a task you undertake, or a product you use? – Let me know you! You can find me on twitter as <a href="https://twitter.com/mcwoods">@mcwoods</a>, or via <a href="https://www.linkedin.com/in/woodsmc/">linked in</a>.</p>The last three posts focused on predicting the future. That is only one part of The Research Roadmap. It is time to get back to the research and see how predicting the future fits into the Roadmap Process.Predicting the Future (Part 3): The Funnel2020-04-11T10:00:00-04:002020-04-11T10:00:00-04:00https://mcwoods.online/industrialresearch/podcast/episode/2020/04/11/Predicting-the-Future-Part-3-The-Funnel<p>In the last post we introduced the futurists and their practices, we identified the three key common practices that futurists follow:</p>
<ul>
<li>Trend Prediction</li>
<li>Multiple Futures</li>
<li>Scenario Writing</li>
</ul>
<p>That post primarily focused on trend prediction. Once the trends have been identified we need a way to combine them into multiple visions of the future, then to translate those into a scenario. That’s a lot of hard work. We need to find a set of shortcuts, some methods to make producing future trends, and writing scenarios an achievable goal.</p>
<iframe title="Predicting the Future (Part 3): The Funnel" style="border: none;" scrolling="no" data-name="pb-iframe-player" src="https://www.podbean.com/media/player/a5ch7-d8f797?from=yiiadmin&download=1&version=1&skin=1&btn-skin=107&auto=0&share=1&fonts=Helvetica&download=1&rtl=0&pbad=1" width="100%" height="122"></iframe>
<h1 id="mini-book-review">Mini Book Review</h1>
<p><a href="https://www.amazon.com/Signals-Are-Talking-Tomorrows-Mainstream/dp/1541788230/ref=tmm_pap_swatch_0?_encoding=UTF8&qid=1586620069&sr=8-1"><img align="right" style="margin:1em" src="/industrialresearch/assets/images/SignalsAreTalkingBook.png" /></a></p>
<p>In 2016 Amy Web wrote “<a href="https://www.amazon.com/Signals-Are-Talking-Tomorrows-Mainstream/dp/1541788230/ref=tmm_pap_swatch_0?_encoding=UTF8&qid=1586620069&sr=8-1">The Signals are Talking</a>”, a book which aims to introduce the reader into how to think like a futurist. Since its publication Amy has gone on to found <a href="https://futuretodayinstitute.com/about/">The Future Today Institute</a> (FTI), an organisation which offers futurist services and consulting. Luckily the FTI documents the process it uses, which is also described in Amy’s book. The FTI website describes this as the <a href="https://futuretodayinstitute.com/mu_uploads/2020/02/FTI-Funnel.pdf">FTI Funnel</a>. The funnel offers some advances, even on Rohit’s Hay Stack Approach.</p>
<p>The book is a fascinating read. Each chapter contains sets of examples and you could almost pick it up and read it like a coffee table book. But this is also, in my opinion its down fall. By going into so much detail on some of the examples I found myself lost, and wondering what the context of the story was trying to provide; how could this relate to the process the book tries to explain? - The FTI Funnel’s summary on their website provides the basis of the process, while the book, once you filter out the stories, provides some additional context. Amy suggests that once you’ve got a vision of the future you should make sure that you get support for it inside your company, if you don’t, she suggests that you drop it. If you’ve got the book, or have read it, I am referring to the final step – the “FUTURE” step. I don’t agree, since there are a bucket load of examples where an organisation has been blinded by their own structures, preventing them from looking at alternative future outcomes. Do you remember Kodak and the Digital Camera? In fact, this is such a common and reoccurring issue that there is an entire area of research dedicated to it.</p>
<p>However, the book and the six FTI steps which it describes provides a lot of what we, as industrial researchers need. I am going to use these 6 basic steps as the basis of a process to create the multiple future definitions and their corresponding scenarios.</p>
<p>The FTI process not only seeks to identify trends, but to quantify them; how big are they, how fast are they spreading. This is important as it allows us to place a timeframe on the futures and their scenarios. Additionally, the extra information on timing, and likelihood allow FTI’s clients to determine how best to react to the scenarios the FTI produces.</p>
<blockquote>
<p>“…One of the most useful features of the [FTI] report is the framework for thinking about these trends in terms of impact horizon and degree of certainty…” - <a href="https://frankdiana.net/2020/03/17/the-future-today-institute-2020-tech-trends-report/">Frank Diana</a></p>
</blockquote>
<h1 id="improving-trend-prediction">Improving Trend Prediction</h1>
<p>We can improve upon the previous approaches to trend prediction by borrowing three steps from the FTI Funnel.</p>
<p><img src="/industrialresearch/assets/images/funnel.jpg" alt="funnel" /></p>
<p><em>Funnel Photo by <a href="https://www.flickr.com/photos/maxim_mogilevskiy/36887243563/in/photolist-YcALnZ-2ifgXro-QASKi-dUaLHa-dprevH-68gkN-5fa8Lf-5fa8J7-24vmtaj-5f5L76-5f5Lca-8wxnfb-5f5LcP-5fa8Hb-5fa8JW-5fa8LG-w4DX4g-xg7wTZ-eushXN-4Xe5xu-4Hm5bT-L8hREi-cxJWuy-8QKmLr-2ankJCW-jjEEqc-RMv5N-JwPpH3-9TpTcX-2bHSFRh-7Vy6Cs-pK6f9a-7Vy6C5-9rYRNX-q5PMEz-HCJqKy-2adZvih-8tyJe3-d5uTJm-2hSMLCP-FHCsSi-8stQRB-8tg1Ft-7UrSkz-jwt5NV-8sJPVy-R4epsE-85AnRs-67hBzk-cCmrsQ">Maxim Mogilevskiy</a>, used under <a href="https://creativecommons.org/licenses/by-sa/2.0/">creative commons</a></em></p>
<h2 id="step-1-data-gathering">Step 1: Data Gathering</h2>
<p>During the data gathering step of trend prediction the FTI funnel provides tips on where to look for signs of an emerging trend. Where Rohit suggests “wondering into the unfamiliar”, the FTI process provides concrete examples of where to look:</p>
<ul>
<li>Early adopters</li>
<li>Technology evangelists</li>
<li>Patent filings</li>
<li>Published academic papers</li>
<li>Start-up companies</li>
<li>Community and groups that may start to adopt the technology before others</li>
<li>Proposed legislation</li>
</ul>
<h2 id="step-2-data-analysis-with-cipher">Step 2: Data Analysis with CIPHER</h2>
<p>Once the data has been collected it needs to be analysed to discover trends. The FTI process uses a process called CIPHER:</p>
<ul>
<li>
<p><strong>Contradictions</strong>: Where two opposing technologies or approaches gain favour at the same time, or when a reversal of approach is suddenly becoming popular.</p>
</li>
<li>
<p><strong>Inflections</strong>: This is a catalyst link, when something occurs, or a new technology appears that really helps accelerate the adoption, or development of other solutions.</p>
</li>
<li>
<p><strong>Practices</strong>: When a new technology dramatically changes an existing practice.</p>
</li>
<li>
<p><strong>Hacks</strong>: The inventions, that twist or take an existing product or technology in a new way. Many of those that gain credence do so because they are being used by early technology adopters.</p>
</li>
<li>
<p><strong>Extremes</strong>: In this we should try to identify the eccentric researchers or inventors who are proposing new, different, occasionally whacky approaches to existing or new problems. While the eccentric research itself may not yield a workable result, the fact that this type of research exists and is being funded helps to indicate that there is a possible market, or need.</p>
</li>
</ul>
<h2 id="step-3-questioning-the-trends">Step 3: Questioning the Trends</h2>
<p>The CIPHER process above is likely to result in a large set of identified trends. The chances are that not all of them are real. As humans we have a habit of seeing patterns when they don’t exist, false positives. To guard against this, we need to review and cull the list of identified changes. Consider each trend in turn and try to identify what is driving the trend, and then ask if the trend is likely to spread across multiple industries or technical fields. If the drive behind the trend is not easily identifiable or the trend is unlikely to spread across fields then shelve the trend, we would consider it again in the future, if more data points emerge.</p>
<h2 id="step-4-timing">Step 4: Timing</h2>
<p>Calculating the arrival date of a trend is really hard. The best approach is to sit down and think about what wide adoption of the trend would look like. Then work backwards, what is missing from the technology or trend that is preventing it from having the envisaged wide adoption? - Considering the roadblocks to adoption will lead to a prediction of the ETA. This is similar to thinking about the trend in terms of technology readiness level (TRL); a technology at TRL9 is a product in a store. Where would you place the trend or technology you are considering? – How far is it from a working product? What elements are missing? Creating lists of these missing elements and sketching out who might provide them helps to determine the ETA.</p>
<p>For an example, think about AI. I attended university, 20 years ago, I recall lectures in which AI was explained as an interesting concept, but unlikely to gain any traction because of the amount of compute power required. That road block was resolved with the introduction of cloud computing which made obtaining the compute power required affordable. Once the roadblock was removed the technology was adopted more widely. Identifying the steps necessary to progress to a product is the first step, the next is to determine where might that solution come from, and to keep an open mind on it. Who would have thought that an online retailer, Amazon, would have become one of the driving forces behind cloud computing?</p>
<h2 id="step-5-write-it-down">Step 5: Write it down</h2>
<p>With all the trends we’ve identified and studied so far, we will have a bunch of concepts and ideas on how they may all combine. It is now important to write these down. One way to do this is to write the scenarios like a story, detail all the events that need to occur before the envisioned future can become a reality. Just the act of writing down an envisioned future helps us to identify the more improbable ones.</p>
<p>If you are inside an organisation, think about how this future would impact them, and what they would have to do to take advantage of the future scenario or trends.</p>
<h2 id="step-6-critique-the-scenarios">Step 6: Critique the Scenarios</h2>
<p>We need some method of critically thinking about the scenarios we’ve produced so far. This in essence is similar to “<a href="https://en.wikipedia.org/wiki/Design_thinking">design thinking</a>”. The aim of design thinking is to get designers and creative people to think about a range of different ways to address a problem. Probably the best way to think about this is to consider what my old art teacher used to ask me to do. She would set a problem, design a poster. Then ask us to come up with 6 ideas for the poster, select the best idea, and do six variations of that concept, and then repeat this process 6 times. Some of the new ideas we would produce, would be combinations of ideas, others would be new. Technically this is referred to as “divergent and convergent thinking”, we think about the ways trends could develop and combine (divergent), then select one to focus on (convergent). We need to do something similar with the scenarios we’ve produced so far. Here are some ways to analyse them:</p>
<ul>
<li>Does what your suggesting offer value, and is it unique, a USP?</li>
<li>Can you identify KPIs or metrics to track against elements of the scenario?</li>
<li>What is the timeline, or ETA for the scenario, have you included enough detail to see this?</li>
<li>Drop, tweak or combine. We can drop a poor scenario, or tweak and improve it, or combine the scenario with others.</li>
<li>Stop. Have we done enough work to be happy with the resulting scenarios? - The 6 steps, 6 times example above was just a rough guide to ensure that we consider a diverse number of future possibilities. Where you stop will be up to you, and will probably simply be a direct correlation as to how much time you have to invest in this part of the roadmap process.</li>
</ul>
<p>Once we’ve got this far we will have a set of scenarios we can use. Now it is really important to work these into our roadmap process.</p>
<h1 id="next-time">Next Time</h1>
<p>In the last two blog posts we’ve covered how it is possible to predict the future. In the next blog post we will return to the research roadmap process. You may already be able to see the similarities between <a href="https://mcwoods.online/industrialresearch/podcast/episode/2020/03/18/Methods-of-Research.html">the research roadmap process</a> I introduced and the methods needed to create the vision of the future. If you spot any let me know, you can reach me on twitter as <a href="https://twitter.com/mcwoods">@mcwoods</a>.</p>In the last post we introduced the futurists and their practices, we identified the three key common practices that futurists follow: Trend Prediction Multiple Futures Scenario Writing That post primarily focused on trend prediction. Once the trends have been identified we need a way to combine them into multiple visions of the future, then to translate those into a scenario. That’s a lot of hard work. We need to find a set of shortcuts, some methods to make producing future trends, and writing scenarios an achievable goal. Mini Book Review In 2016 Amy Web wrote “The Signals are Talking”, a book which aims to introduce the reader into how to think like a futurist. Since its publication Amy has gone on to found The Future Today Institute (FTI), an organisation which offers futurist services and consulting. Luckily the FTI documents the process it uses, which is also described in Amy’s book. The FTI website describes this as the FTI Funnel. The funnel offers some advances, even on Rohit’s Hay Stack Approach. The book is a fascinating read. Each chapter contains sets of examples and you could almost pick it up and read it like a coffee table book. But this is also, in my opinion its down fall. By going into so much detail on some of the examples I found myself lost, and wondering what the context of the story was trying to provide; how could this relate to the process the book tries to explain? - The FTI Funnel’s summary on their website provides the basis of the process, while the book, once you filter out the stories, provides some additional context. Amy suggests that once you’ve got a vision of the future you should make sure that you get support for it inside your company, if you don’t, she suggests that you drop it. If you’ve got the book, or have read it, I am referring to the final step – the “FUTURE” step. I don’t agree, since there are a bucket load of examples where an organisation has been blinded by their own structures, preventing them from looking at alternative future outcomes. Do you remember Kodak and the Digital Camera? In fact, this is such a common and reoccurring issue that there is an entire area of research dedicated to it. However, the book and the six FTI steps which it describes provides a lot of what we, as industrial researchers need. I am going to use these 6 basic steps as the basis of a process to create the multiple future definitions and their corresponding scenarios. The FTI process not only seeks to identify trends, but to quantify them; how big are they, how fast are they spreading. This is important as it allows us to place a timeframe on the futures and their scenarios. Additionally, the extra information on timing, and likelihood allow FTI’s clients to determine how best to react to the scenarios the FTI produces. “…One of the most useful features of the [FTI] report is the framework for thinking about these trends in terms of impact horizon and degree of certainty…” - Frank Diana Improving Trend Prediction We can improve upon the previous approaches to trend prediction by borrowing three steps from the FTI Funnel. Funnel Photo by Maxim Mogilevskiy, used under creative commons Step 1: Data Gathering During the data gathering step of trend prediction the FTI funnel provides tips on where to look for signs of an emerging trend. Where Rohit suggests “wondering into the unfamiliar”, the FTI process provides concrete examples of where to look: Early adopters Technology evangelists Patent filings Published academic papers Start-up companies Community and groups that may start to adopt the technology before others Proposed legislation Step 2: Data Analysis with CIPHER Once the data has been collected it needs to be analysed to discover trends. The FTI process uses a process called CIPHER: Contradictions: Where two opposing technologies or approaches gain favour at the same time, or when a reversal of approach is suddenly becoming popular. Inflections: This is a catalyst link, when something occurs, or a new technology appears that really helps accelerate the adoption, or development of other solutions. Practices: When a new technology dramatically changes an existing practice. Hacks: The inventions, that twist or take an existing product or technology in a new way. Many of those that gain credence do so because they are being used by early technology adopters. Extremes: In this we should try to identify the eccentric researchers or inventors who are proposing new, different, occasionally whacky approaches to existing or new problems. While the eccentric research itself may not yield a workable result, the fact that this type of research exists and is being funded helps to indicate that there is a possible market, or need. Step 3: Questioning the Trends The CIPHER process above is likely to result in a large set of identified trends. The chances are that not all of them are real. As humans we have a habit of seeing patterns when they don’t exist, false positives. To guard against this, we need to review and cull the list of identified changes. Consider each trend in turn and try to identify what is driving the trend, and then ask if the trend is likely to spread across multiple industries or technical fields. If the drive behind the trend is not easily identifiable or the trend is unlikely to spread across fields then shelve the trend, we would consider it again in the future, if more data points emerge. Step 4: Timing Calculating the arrival date of a trend is really hard. The best approach is to sit down and think about what wide adoption of the trend would look like. Then work backwards, what is missing from the technology or trend that is preventing it from having the envisaged wide adoption? - Considering the roadblocks to adoption will lead to a prediction of the ETA. This is similar to thinking about the trend in terms of technology readiness level (TRL); a technology at TRL9 is a product in a store. Where would you place the trend or technology you are considering? – How far is it from a working product? What elements are missing? Creating lists of these missing elements and sketching out who might provide them helps to determine the ETA. For an example, think about AI. I attended university, 20 years ago, I recall lectures in which AI was explained as an interesting concept, but unlikely to gain any traction because of the amount of compute power required. That road block was resolved with the introduction of cloud computing which made obtaining the compute power required affordable. Once the roadblock was removed the technology was adopted more widely. Identifying the steps necessary to progress to a product is the first step, the next is to determine where might that solution come from, and to keep an open mind on it. Who would have thought that an online retailer, Amazon, would have become one of the driving forces behind cloud computing? Step 5: Write it down With all the trends we’ve identified and studied so far, we will have a bunch of concepts and ideas on how they may all combine. It is now important to write these down. One way to do this is to write the scenarios like a story, detail all the events that need to occur before the envisioned future can become a reality. Just the act of writing down an envisioned future helps us to identify the more improbable ones. If you are inside an organisation, think about how this future would impact them, and what they would have to do to take advantage of the future scenario or trends. Step 6: Critique the Scenarios We need some method of critically thinking about the scenarios we’ve produced so far. This in essence is similar to “design thinking”. The aim of design thinking is to get designers and creative people to think about a range of different ways to address a problem. Probably the best way to think about this is to consider what my old art teacher used to ask me to do. She would set a problem, design a poster. Then ask us to come up with 6 ideas for the poster, select the best idea, and do six variations of that concept, and then repeat this process 6 times. Some of the new ideas we would produce, would be combinations of ideas, others would be new. Technically this is referred to as “divergent and convergent thinking”, we think about the ways trends could develop and combine (divergent), then select one to focus on (convergent). We need to do something similar with the scenarios we’ve produced so far. Here are some ways to analyse them: Does what your suggesting offer value, and is it unique, a USP? Can you identify KPIs or metrics to track against elements of the scenario? What is the timeline, or ETA for the scenario, have you included enough detail to see this? Drop, tweak or combine. We can drop a poor scenario, or tweak and improve it, or combine the scenario with others. Stop. Have we done enough work to be happy with the resulting scenarios? - The 6 steps, 6 times example above was just a rough guide to ensure that we consider a diverse number of future possibilities. Where you stop will be up to you, and will probably simply be a direct correlation as to how much time you have to invest in this part of the roadmap process. Once we’ve got this far we will have a set of scenarios we can use. Now it is really important to work these into our roadmap process. Next Time In the last two blog posts we’ve covered how it is possible to predict the future. In the next blog post we will return to the research roadmap process. You may already be able to see the similarities between the research roadmap process I introduced and the methods needed to create the vision of the future. If you spot any let me know, you can reach me on twitter as @mcwoods.Predicting the Future (Part 2): Introducing the Futurist2020-04-02T10:00:00-04:002020-04-02T10:00:00-04:00https://mcwoods.online/industrialresearch/podcast/episode/2020/04/02/Predicting-the-Future-Part-2-Introducing-the-Futurologist<p>In the last post I covered how future prediction, is, to a degree, possible but I didn’t get the chance to explain how. There is, in fact, a whole industry dedicated to predicting the future. They call themselves “Futurists” or “Futurologists”.</p>
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<h1 id="introducing-the-futurologist">Introducing the Futurologist</h1>
<p>The whole topic of futurism is massive and the stories of its origins are complex and fascinating. I could take you on a journey into the depths of this emerging profession and academic practice, but that would definitely be a different series of blog posts. In this post I want to give you a very quick overview of how futurology has got to its current state and then cover the practices, tips and techniques we can use.</p>
<p><img src="/industrialresearch/assets/images/CrystalBall_Gabe_Kronisch_CC_Flickr.jpg" alt="'New Prespective' by Gabe Kronisch - via Flickr, used under creative commons" /></p>
<p><em><a href="https://www.flickr.com/photos/gkronisch/24523269215/in/photolist-Dn3ayt-4fUSc1-ayVv7C-bS8WDe-QtqgV-hLxKq-TsdJ2x-aWPLh6-dshHuu-Ep9AVm-2bFvdJJ-cPa87b-S1Avjy-25EaHn9-zPQgPS-61hPK5-PBgovn-2aJLoCo-iwmBqs-24F4riE-6EARvH-5t93LY-Tiub45-iM1FMn-Vj2WRm-A7YE3z-76jcmc-37VBrk-cCfCk-9NSvMc-koQD4-85NMwV-8HF2fo-24D84Jy-EqJ2vp-22Y81Dy-8WYhve-nQ7hK1-aJLf2B-UkEjaB-XpKicK-u6SFM-yUX1-KE8b7-25J7c1P-ArD2en-85RXjJ-xpLuUk-85RXmy-7TM7FJ">‘New Prespective’ by Gabe Kronisch - via Flickr</a>, <a href="https://creativecommons.org/licenses/by/2.0/">used under creative commons</a></em></p>
<h2 id="a-brief-history-of-foresight-futurology">A Brief History of Foresight (Futurology)</h2>
<p>In 1901 H.G Wells wrote “Anticipations of the Reaction of Mechanical and Scientific Progress Upon Human Life and Thought: An Experiment in Prophecy” – it was his attempt to predict what life would be like in the year 2000. He predicted that there would be equality between men and women, that road networks and rail links would lead to satellite towns with people commuting distances for work. He even predicted the creation of the European Union. While Wells is often referred to as the father of science fiction, during his lifetime he was also commended on his foresight into future developments. Indeed, in 1935 he proposed the study of foresight as a method of determining future events. Today we use the term “futurology” to mean the same thing and the term itself was introduced in 1945 by a German academic, <a href="https://en.wikipedia.org/wiki/Ossip_K._Flechtheim">Ossip K Fechtheim</a> when he too proposed <a href="https://en.wikipedia.org/wiki/Futurist">the study of the future</a>.</p>
<h2 id="emerging-processes">Emerging Processes</h2>
<p>Back in the late 40’s / early 50s the practice took <a href="https://en.wikipedia.org/wiki/Futures_studies,">two different and distinct paths</a> one in the USA and a second in Europe. The research in the USA was led primarily from a technology point of view and was also used to conduct studies on future outlines of technology development and defence systems. I can imagine this as a reaction to the cold war as it emerged. In contrast, Europe was, at roughly the same time, recovering from the end of the second world war with societies and their former structures destroyed. In this environment futurology was used as the start of a process to imagine the type of society that the recovering states wanted to produce. Futurology helped produce long range planning for countries and laid the foundations for social policy and consequent legislation.</p>
<p>Today futurology is used by think tanks, corporations, nations and governments as part of strategic planning, of imagining future industry disruption and trying to anticipate change. There is even a professional body, <a href="https://www.apf.org/?">The Association of Professional Futurists</a>. However, the profession’s practices are still evolving. As I mentioned in the last post there are enough data points to show that future predictions are possible and can be surprisingly accurate, unfortunately there is no concrete repeatable method of working through source data to determine a future prediction. Most of the predictions appear to be down to the skill, intuition, or imagination of the individual who is conducting the research. The result is a set of competing methods with various champions. In the midst of all this noise it is possible to determine a set of common practices.</p>
<h2 id="useful-common-practices-in-futurology">Useful Common Practices in Futurology</h2>
<p>There are a number of concepts and practices which appear to be canon across the profession and these, for us as researchers, are the little nuggets of awesomeness we need to embrace.</p>
<ol>
<li>
<p><strong>Trend Prediction is Possible</strong><br />
We can see and predict trends which sweep the world. Trends, as I mentioned in my last post, emerge like waves and can be tracked from the fringes of society to mainstream adoption.</p>
</li>
<li>
<p><strong>The “Future” is Plural</strong><br />
There is never one vision of the future - there are multiple. They are built in part upon a combination of emerging trends. We can use the concept of a “scenario” to help us test which of these alternative futures is the most likely to occur.</p>
</li>
<li>
<p><strong>Write a Scenario to Test a Future</strong><br />
Writing down the scenario helps to make it concrete and lays bare all the assumptions we’ve made and the source data points and trends we’ve used. Once written down we can critique it, test it and validate the assumptions we make.</p>
</li>
</ol>
<h1 id="watching-the-waves">Watching the Waves</h1>
<p>As I mentioned previously, there are two approaches to predicting the future - one that looks at large scale trends, the wide-ranging view; and another that considers the impact on changes based on a specific core end user need. In this post I’m going to introduce the wide-ranging view, spotting the trends as they emerge.</p>
<h2 id="todays-trends">Today’s Trends</h2>
<p>There are a number of trends that we take for granted. For instance, today it’s a widely held belief that genetically modified food may not be good for us, but a decade ago that was a fringe belief.</p>
<p>An example of an emerging trend today could be the push for privacy online. There is an emergence of VPN providers seeking to secure your internet connection, providing an encrypted link and ensuring that your internet service provider, or government is unable to observe the websites you visit. This uptick in VPN business hints at the concern many people have today. The constant trickle of news stories about data breaches is pushing people to be more cautious with their data. Just this week Marriott again announced its second <a href="https://www.reuters.com/article/us-marriott-intnl-data-breach/marriott-says-5-2-million-guests-exposed-in-new-data-breach-idUSKBN21I3DC">large scale data breach that affects 5.2 million people</a>. The events around how Cambridge Anayltica used social media data to try to swing elections have led to folks being concerned about how their data is being used. This has led some to move away from Facebook, Google and other online providers. Let’s not forget the leaks from Snowden about how GCHQ and the NSA can watch what you do on line. But will this trend continue or has it already reached a peak? – Is Google just too useful to live without? Well, the sphere of trend prediction tries to answer that, and one New York Times bestselling author has a methodology he has published all about trend prediction.</p>
<h1 id="trend-prediction-the-hay-stack-approach">Trend Prediction: The Hay Stack Approach</h1>
<p><a href="https://www.linkedin.com/in/rohitbhargava/">Rohit Bhargava</a> is a New York Times bestselling author who annually publishes “<a href="https://www.amazon.com/dp/1940858968">Non-Obvious Mega Trends</a>”. This book details the trends that Rohit has observed over the last 12 months and it includes a description of his process of spotting these trends. Rohit describes this process as the “Hay Stack” approach. Essentially Rohit tries to collect news articles and stories, press clippings, blog posts and other data sources over a defined period of time. Rohit tries to make sure the input for this collection process is as devoid of bias as possible and describes this data collection phase as “<strong>Wander[ing] into the unfamiliar</strong>”. Once he has collected all of his data points Rohit analyses them in an effort to identify emerging patterns. There are a number of techniques that he uses in his analysis. Briefly these are as follows:</p>
<ul>
<li>
<p>“<strong>Look for Similarities</strong>” Rohit refers essentially to cross-industry innovation where techniques from one industry can be transferred to another. When looking through the data set look for opportunities for this to occur. You may see that an innovation has crossed from one industry to another, but yet there are further opportunities for it to spread across other industries.</p>
<p>As an example, I’ve been amazed at how touch screens have leapt across industries. They gained popularity with the iPhone, then spread across industries. They are now used in the car industry as one of the major ways in which you can control the interior electronics of your car, everything from the radio to the air-conditioning. Indeed, the Tesla model 3 basically has what looks like an oversized iPad mounted in the middle of the dash, with almost all other controls, dials and user interface elements removed.</p>
</li>
<li>
<p>“<strong>Serendipitous Ideas</strong>” with this concept Rohit refers spotting trends in one industry that could affect another. Rohit’s example is how Starbuck’s was inspired by the plethora of Italian coffee shops. However, we could in the technology world refer to how the push for compute as a service has resulted in a whole bunch of software, such as a service (Office 365), and now products as a service, with John Deer’s tractors, Volvo’s Cars, music and TV.</p>
</li>
<li>
<p>“<strong>Be Persuadable</strong>” - Approach the data set from multiple angles / stake holder viewpoints. Everyone will see the advantages and disadvantages of both technology and industry changes in different ways.</p>
<p>Let us think about touchscreens again. They’ve seen adoption across the car industry, but is that a good thing? – Imagine the touch screen from the point of view of an overworked motorist, driving in bad weather. Do they have the time to look down and study the screen? A common observation about Tesla is that the buttons and controls on the iPad interface can move between software updates, forcing motorists to actually study the screen. Just this week Honda became the first car manufacturer to move away from touchscreens and back to physical controls, why, well, the <a href="https://www.autocar.co.uk/car-news/motor-shows-geneva-motor-show/honda-bucks-industry-trend-removing-touchscreen-controls">project leader explained</a> “<em>…it was difficult to operate intuitively. You had to look at the screen to change the heater setting…</em>”.</p>
</li>
</ul>
<p>Rohit’s approach is great and he has produced a whole collection of fantastic and quite insightful trends. However, it is also fair to say that the process is by definition subjective and perhaps owes as much to serendipity rather than a rigorous detailed, repeatable process.</p>
<p>When you review Rohit’s approach it appears remarkable similar to one used by Will Gibson. In <a href="https://www.newyorker.com/magazine/2019/12/16/how-william-gibson-keeps-his-science-fiction-real?verso=true">an interview in The New Yorker</a> Gibson outlines some of the events which influenced his book <a href="https://www.amazon.com/Neuromancer-William-Gibson/dp/0441569595/ref=sr_1_1?crid=6F0GUECPGG74&keywords=neuromancer+by+william+gibson&qid=1585707966&s=books&sprefix=neurom%2Cstripbooks%2C157&sr=1-1">Neuromancer</a>. The article is long, probably a 30-40 minute read but if you’ve the time you’ll discover that Gibson does what Rohit is suggesting - Wandering into the unfamiliar.</p>
<p>As a researcher we are often also hired because of our technical background and expertise We can be asked to predict what will happen in our domain of expertise; so perhaps having a bias or preference for data within a given domain isn’t a bad thing.</p>
<h1 id="next-time">Next Time</h1>
<p>Now we’ve a method of spotting trends, the next task is trying to combine them into a set of future scenarios but that will have to wait for the next post. In the meantime, I’ve a task for you.</p>
<p>I’ve been using the Haystack Approach. I collect interesting articles on the way and store them; then after a few months I’ll go back and look at them. I use “Pocket” the app / browser extension by Mozilla. It will store web pages and allow you to tag them in the tool with specific headings. Look around the media you consume, the headlines you read, the comments on social media. Are there emerging trends you can see? If you spot one, share it. I’d love to hear about it. You can find me on Twitter as <a href="https://twitter.com/mcwoods">@mcwoods</a>.</p>In the last post I covered how future prediction, is, to a degree, possible but I didn’t get the chance to explain how. There is, in fact, a whole industry dedicated to predicting the future. They call themselves “Futurists” or “Futurologists”.Predicting the Future (Part 1)2020-03-26T10:00:00-04:002020-03-26T10:00:00-04:00https://mcwoods.online/industrialresearch/podcast/episode/2020/03/26/Predicting-the-Future-Part-1<iframe title="Predicting the Future Part 1" style="border: none;" scrolling="no" data-name="pb-iframe-player" src="https://www.podbean.com/media/player/uf7yi-d75984?from=yiiadmin&download=1&version=1&skin=1&btn-skin=107&auto=0&share=1&fonts=Helvetica&download=1&rtl=0&pbad=1" width="100%" height="122"></iframe>
<p>In the last post I outlined the Research Roadmap Process, the first phase of the process is predicting the future. That is a huge task, in fact, some may say it is impossible, particularly in these trying times. As I write this most of the world is coping with the spread of the COVID-19 virus. The world today looks very different to what it did just 1 month ago.</p>
<h1 id="can-you-predict-the-future">Can you Predict the Future?</h1>
<p>These is a huge degree of scepticism whenever you start talking about predicting the future. Your right to initially argue that it is impossible. I mean after all, if we could predict the future we’d have predicted and coped better with COVID-19, or on a more positive note predicted the winning lottery numbers.</p>
<p>I’d like to introduce you to four people, all of whom have tried to predict the future.</p>
<h1 id="robert-fitzroy">Robert FitzRoy</h1>
<p><img align="right" style="margin:1em" src="/industrialresearch/assets/images/Robert_Fitzroy.jpg" /></p>
<p>Captain <a href="https://en.wikipedia.org/wiki/Robert_FitzRoy">Robert FitzRoy</a> was the Royal Navy Captain who sailed Charles Darwin aboard HMS Beagle on his famous voyage to The Galápagos Islands. But that wasn’t the peak of his career. FitzRoy became fascinated by the weather and, in an effort to try to save fellow sailors, he attempted to predict it. In the 1800s this idea seemed simply impossible; how could you possibly predict the future? FitzRoy faced such a huge pushback. This ridicule and scepticism from the media and the scientific community drove FitzRoy to <a href="https://www.bbc.com/news/magazine-32483678">create his own word - “Forecast”</a>. He argued that this word better portrayed the work he was doing:</p>
<blockquote>
<p>“Prophecies and predictions they are not, the term forecast is strictly applicable to such an opinion as is the result of scientific combination and calculation.”</p>
</blockquote>
<p>FitzRoy went on to found the UK’s Met Office, become Vice-Admiral and eventually Governor of New Zealand. Perhaps his lasting legacy for us in the realm of software and technology research is that he showed that predicting the future, even with uncertainty, can be done.</p>
<h1 id="william-gibson">William Gibson</h1>
<p>In 1984 William Gibson released a Science Fiction book called <a href="https://www.amazon.com/Neuromancer-William-Gibson/dp/0441569595/ref=sr_1_1?keywords=Neuromancer&qid=1585141650&sr=8-1">Neuromancer</a>. In this book Gibson outlined a future in which we access “the net”. On “the net” Gibson predicted that there would be large commercial corporations, computer servers, firewalls and indeed hackers. Just as FitzRoy invented his own word so did Gibson. He called this new place on “the net” Cyberspace.</p>
<h1 id="marc-porat">Marc Porat</h1>
<p>In 1978 <a href="https://en.wikipedia.org/wiki/Marc_Porat">Marc Porat</a> was Program Director at the Aspen Institute, an international non-profit think tank. It was while here that Porat wrote what he referred to as his “red book”. This book contained a vision of the future that Porat had come up with. In this vision of the future Porat envisioned a world where we all walked around with phones in our pockets. He even created sketches for what the device would look like. It would have a large screen covering the entire face of it and the screen would react to your touch.</p>
<p>Porat also detailed what you would do with the device. He argued that you would receive electronic messages which would just drop from a computer and land on your phone. Just as Gibson had created his own word so did Porat. In trying to describe how these servers in the sky which dropped messages to mobile phones would work. Porat referred to them as being a “cloud” of computers.</p>
<p>If you would like to see an image of the phone that Porat had in his red book, then please head over to the <a href="https://www.generalmagicthemovie.com/">General Magic Movie website</a>. Indeed if you’d like to learn more about the movie you can stream it online, it’s a <a href="https://www.rottentomatoes.com/m/general_magic">fantastic watch</a>, it details the startup company Porat ran, called <a href="https://en.wikipedia.org/wiki/General_Magic">General Magic</a>.</p>
<p>As these three figures have shown, predicting the future is possible. The complete accuracy of the prediction however, well, that can be debated. Before we start thinking about how we can predict the future we need to think about how far into the future we want to look. FitzRoy only looked 24 - 48 hours into the future. We’d need to look further ahead than that.</p>
<h1 id="how-far-ahead-do-i-have-to-look">How far ahead do I have to look?</h1>
<p>Deciding how far into the future to try to predict is difficult but there are several key drivers from your organisation which can help you try to decide what that time frame will be. We can work backwards to determine what this time frame should be.</p>
<h2 id="the-technology-transfer-latency">The Technology Transfer Latency</h2>
<p>Imagine that right now you had a fantastic detailed plan for the future and the perfect future product. You hand this future product and plan over to a development team. It will take the development team time to convert that product into something that can be sold. We can call this the Technology Transfer Latency. This latency differs depending on the organisation. For most large companies this can be about two years: the first year validating the future product can be sold and the next year spent creating it and polishing it to the standards required in the marketplace.</p>
<h2 id="the-time-spend-researching">The Time Spend Researching</h2>
<p>Once you’ve understood your company’s technology transfer latency, the next question is how long do you need to conduct your research to validate your vision of the future. This can be dependent on available budgets and organisational structures. Your internal team may have its own project length for instance. Collaborative research however often comes with its own fixed set of research timelines.</p>
<h3 id="collaborative-research">Collaborative Research</h3>
<p>Both the USA and the EU sponsor commercial collaborative research. Both organisations provide a prebuilt method of allowing both academic and commercial organisations to work together. The frameworks are so well structured that they often encourage competing organisations to collaborate, whereas perhaps in the past they wouldn’t have. I once took part in an EU sponsored research project called <a href="http://cloudwave-fp7.eu/">CloudWave</a> , it was about how to adapt software for better execution in the cloud. I represented Intel, working to make cloud applications better on Intel’s x86 processor platform. At the same time, I worked with IBM who were attempting to do the same for the PowerPC processor platform.</p>
<p>In providing the collaborative research frameworks, both the EU and the USA provide project duration guidelines. In the case of the EU and its Horizon 2020 programme this is roughly three years, broken down into three stages. These are understanding the problem, developing and evaluating possible solutions and, in the last phase, creating a working prototype which addresses the issues. In this case the research period is about three years.</p>
<p>Now imagine that you have a vision of the future, by working backwards we have determined we need this to extend about five years in the future (the Technology Transfer Latency of two years and the research period of three years). But we still need time to create the vision of the future in the first place. It can take up to a year to understand the business, the customers, the competitors, the value chain, the technical challenges and potential solutions. This gives us a period of about six years.</p>
<p>The amount of time you spend trying to predict the future, the technology realisation delay and the amount of time you are able to spend doing the research all vary. Different organisations have different views on what these should be and to a large extent this is dictated by available budget. However, as a rough rule of thumb 5 - 7 years is the most valuable. When you try to predict further into the future the number of variables you need to try to consider jumps massively and the risk of simply getting it completely wrong is very high. Conversely when you consider shorter time frames of two to three years ahead there is very little scope for new innovation as you are basically working on validating the next version of the product.</p>
<h1 id="methods-for-predicting-the-future">Methods for Predicting the Future</h1>
<p>Now we know how far into the future we need to predict the next question is “how?”. There are a number of different approaches which can be used to try to make educated guesses on the future. They appear to fall into two main categories: a wide-breadth or large trend observation and a deep-seeing, product or industry specific observation.</p>
<h1 id="watching-the-waves-at-the-beach">Watching the Waves at the Beach</h1>
<p>I am going to use a quick analogy here to explain wide-breadth and deep-seeing. Imagine that you are standing on a sandy beach. The beach has a few rocks off to the left-hand side, and some tidal pools, scooped out sand caused by the motion of the waves. You are standing watching the waves come in to the beach and you can see the patterns that they form on the shore. The strong wind from the sea is responsible for the major wave pattern, however, the local rocks bounce the waves off each other and the waves interact, causing the tidal pools, which further effect the waves. The result at the shore side is a pattern which appears random but it isn’t. It is however extremely complex.</p>
<p>By understanding the strength of the wind, we can make educated predictions on the size of the waves we might see at the shore. However, it is almost impossible to understand all the effects of the rocks and tidal pools. We might be able to predict the effect of the waves on a small area of the rocks but not for the entire stretch of the beach we can see before us.</p>
<p>The large sweeping trends, both social and technological, which cross the globe are akin to the wind whipping up waves; this is the wide-breath approach. In contrast, studying the effects of only a small number of rocks gives us a deep-seeing approach. The best predictions are of course when these are combined. But doing that is a huge and complex but not an impossible task which we will cover in the next post.</p>
<h1 id="from-the-earth-to-the-moon">From the Earth to the Moon</h1>
<p><img align="right" width="211px" height="300px" style="margin:1em" src="/industrialresearch/assets/images/422px-Jules_Verne.jpg" /></p>
<p>Remember I mentioned we were going to talk about four people? Well we’ve only covered three. In 1865 Jules Verne wrote the <a href="https://en.wikipedia.org/wiki/From_the_Earth_to_the_Moon">book</a> “<a href="https://www.gutenberg.org/ebooks/44278">From the Earth to the Moon</a>”, in which he depicted a giant cannon which could fire a projectile all the way from the Earth and land it on the moon. This is of course impossible but the length of the cannon and its location were not. You see Verne had, prior to writing the book, done a considerable amount of research. He had calculated the speed at which a projectile would need to leave earth’s atmosphere. Based on this he deduced the size of the cannon required. He also looked hard at where in the world you would want to place the cannon. This search was a combination of politics; which nation would have the funds and the drive to launch something to moon, and it was also science based; placing a launch site closer to the equator allowed for the reduced thickness of the earth’s atmosphere at that latitude and allowed the projectile to utilise the earth’s rotation. Which location did Verne pick? Florida. In fact an amazing number of his calculations were correct and informed his story so much that <a href="http://astronautix.com/j/julesvernemoongun.html">folks have been comparing the Apollo moon missions to his book</a> for a while.</p>
<p>The best Science Fiction and the best interpretations of the future are based, just as FitzRoy said, on “the result of scientific combination and calculation”. What Verne had done was identify the core mathematics and physics behind an attempt at a moon landing and then worked backward toward a story.</p>
<h1 id="the-next-blog-post">The Next Blog Post</h1>
<p>This post has already become longer than anticipated. In the next post I am going to outline how ‘breadth first’ approaches work. But in the meantime, think about your organisation and industry. Can you identify any core motivations that direct and drive the work you do? Let me know. you can reach me on <a href="https://twitter.com/mcwoods">Twitter as @mcwoods</a>.</p>In the last post I outlined the Research Roadmap Process, the first phase of the process is predicting the future. That is a huge task, in fact, some may say it is impossible, particularly in these trying times. As I write this most of the world is coping with the spread of the COVID-19 virus. The world today looks very different to what it did just 1 month ago. Can you Predict the Future? These is a huge degree of scepticism whenever you start talking about predicting the future. Your right to initially argue that it is impossible. I mean after all, if we could predict the future we’d have predicted and coped better with COVID-19, or on a more positive note predicted the winning lottery numbers. I’d like to introduce you to four people, all of whom have tried to predict the future. Robert FitzRoy Captain Robert FitzRoy was the Royal Navy Captain who sailed Charles Darwin aboard HMS Beagle on his famous voyage to The Galápagos Islands. But that wasn’t the peak of his career. FitzRoy became fascinated by the weather and, in an effort to try to save fellow sailors, he attempted to predict it. In the 1800s this idea seemed simply impossible; how could you possibly predict the future? FitzRoy faced such a huge pushback. This ridicule and scepticism from the media and the scientific community drove FitzRoy to create his own word - “Forecast”. He argued that this word better portrayed the work he was doing: “Prophecies and predictions they are not, the term forecast is strictly applicable to such an opinion as is the result of scientific combination and calculation.” FitzRoy went on to found the UK’s Met Office, become Vice-Admiral and eventually Governor of New Zealand. Perhaps his lasting legacy for us in the realm of software and technology research is that he showed that predicting the future, even with uncertainty, can be done. William Gibson In 1984 William Gibson released a Science Fiction book called Neuromancer. In this book Gibson outlined a future in which we access “the net”. On “the net” Gibson predicted that there would be large commercial corporations, computer servers, firewalls and indeed hackers. Just as FitzRoy invented his own word so did Gibson. He called this new place on “the net” Cyberspace. Marc Porat In 1978 Marc Porat was Program Director at the Aspen Institute, an international non-profit think tank. It was while here that Porat wrote what he referred to as his “red book”. This book contained a vision of the future that Porat had come up with. In this vision of the future Porat envisioned a world where we all walked around with phones in our pockets. He even created sketches for what the device would look like. It would have a large screen covering the entire face of it and the screen would react to your touch. Porat also detailed what you would do with the device. He argued that you would receive electronic messages which would just drop from a computer and land on your phone. Just as Gibson had created his own word so did Porat. In trying to describe how these servers in the sky which dropped messages to mobile phones would work. Porat referred to them as being a “cloud” of computers. If you would like to see an image of the phone that Porat had in his red book, then please head over to the General Magic Movie website. Indeed if you’d like to learn more about the movie you can stream it online, it’s a fantastic watch, it details the startup company Porat ran, called General Magic. As these three figures have shown, predicting the future is possible. The complete accuracy of the prediction however, well, that can be debated. Before we start thinking about how we can predict the future we need to think about how far into the future we want to look. FitzRoy only looked 24 - 48 hours into the future. We’d need to look further ahead than that. How far ahead do I have to look? Deciding how far into the future to try to predict is difficult but there are several key drivers from your organisation which can help you try to decide what that time frame will be. We can work backwards to determine what this time frame should be. The Technology Transfer Latency Imagine that right now you had a fantastic detailed plan for the future and the perfect future product. You hand this future product and plan over to a development team. It will take the development team time to convert that product into something that can be sold. We can call this the Technology Transfer Latency. This latency differs depending on the organisation. For most large companies this can be about two years: the first year validating the future product can be sold and the next year spent creating it and polishing it to the standards required in the marketplace. The Time Spend Researching Once you’ve understood your company’s technology transfer latency, the next question is how long do you need to conduct your research to validate your vision of the future. This can be dependent on available budgets and organisational structures. Your internal team may have its own project length for instance. Collaborative research however often comes with its own fixed set of research timelines. Collaborative Research Both the USA and the EU sponsor commercial collaborative research. Both organisations provide a prebuilt method of allowing both academic and commercial organisations to work together. The frameworks are so well structured that they often encourage competing organisations to collaborate, whereas perhaps in the past they wouldn’t have. I once took part in an EU sponsored research project called CloudWave , it was about how to adapt software for better execution in the cloud. I represented Intel, working to make cloud applications better on Intel’s x86 processor platform. At the same time, I worked with IBM who were attempting to do the same for the PowerPC processor platform. In providing the collaborative research frameworks, both the EU and the USA provide project duration guidelines. In the case of the EU and its Horizon 2020 programme this is roughly three years, broken down into three stages. These are understanding the problem, developing and evaluating possible solutions and, in the last phase, creating a working prototype which addresses the issues. In this case the research period is about three years. Now imagine that you have a vision of the future, by working backwards we have determined we need this to extend about five years in the future (the Technology Transfer Latency of two years and the research period of three years). But we still need time to create the vision of the future in the first place. It can take up to a year to understand the business, the customers, the competitors, the value chain, the technical challenges and potential solutions. This gives us a period of about six years. The amount of time you spend trying to predict the future, the technology realisation delay and the amount of time you are able to spend doing the research all vary. Different organisations have different views on what these should be and to a large extent this is dictated by available budget. However, as a rough rule of thumb 5 - 7 years is the most valuable. When you try to predict further into the future the number of variables you need to try to consider jumps massively and the risk of simply getting it completely wrong is very high. Conversely when you consider shorter time frames of two to three years ahead there is very little scope for new innovation as you are basically working on validating the next version of the product. Methods for Predicting the Future Now we know how far into the future we need to predict the next question is “how?”. There are a number of different approaches which can be used to try to make educated guesses on the future. They appear to fall into two main categories: a wide-breadth or large trend observation and a deep-seeing, product or industry specific observation. Watching the Waves at the Beach I am going to use a quick analogy here to explain wide-breadth and deep-seeing. Imagine that you are standing on a sandy beach. The beach has a few rocks off to the left-hand side, and some tidal pools, scooped out sand caused by the motion of the waves. You are standing watching the waves come in to the beach and you can see the patterns that they form on the shore. The strong wind from the sea is responsible for the major wave pattern, however, the local rocks bounce the waves off each other and the waves interact, causing the tidal pools, which further effect the waves. The result at the shore side is a pattern which appears random but it isn’t. It is however extremely complex. By understanding the strength of the wind, we can make educated predictions on the size of the waves we might see at the shore. However, it is almost impossible to understand all the effects of the rocks and tidal pools. We might be able to predict the effect of the waves on a small area of the rocks but not for the entire stretch of the beach we can see before us. The large sweeping trends, both social and technological, which cross the globe are akin to the wind whipping up waves; this is the wide-breath approach. In contrast, studying the effects of only a small number of rocks gives us a deep-seeing approach. The best predictions are of course when these are combined. But doing that is a huge and complex but not an impossible task which we will cover in the next post. From the Earth to the Moon Remember I mentioned we were going to talk about four people? Well we’ve only covered three. In 1865 Jules Verne wrote the book “From the Earth to the Moon”, in which he depicted a giant cannon which could fire a projectile all the way from the Earth and land it on the moon. This is of course impossible but the length of the cannon and its location were not. You see Verne had, prior to writing the book, done a considerable amount of research. He had calculated the speed at which a projectile would need to leave earth’s atmosphere. Based on this he deduced the size of the cannon required. He also looked hard at where in the world you would want to place the cannon. This search was a combination of politics; which nation would have the funds and the drive to launch something to moon, and it was also science based; placing a launch site closer to the equator allowed for the reduced thickness of the earth’s atmosphere at that latitude and allowed the projectile to utilise the earth’s rotation. Which location did Verne pick? Florida. In fact an amazing number of his calculations were correct and informed his story so much that folks have been comparing the Apollo moon missions to his book for a while. The best Science Fiction and the best interpretations of the future are based, just as FitzRoy said, on “the result of scientific combination and calculation”. What Verne had done was identify the core mathematics and physics behind an attempt at a moon landing and then worked backward toward a story. The Next Blog Post This post has already become longer than anticipated. In the next post I am going to outline how ‘breadth first’ approaches work. But in the meantime, think about your organisation and industry. Can you identify any core motivations that direct and drive the work you do? Let me know. you can reach me on Twitter as @mcwoods.Methods of Research2020-03-18T10:00:00-04:002020-03-18T10:00:00-04:00https://mcwoods.online/industrialresearch/podcast/episode/2020/03/18/Methods-of-Research<iframe title="Methods of Research - With help from Edison and Swan" style="border: none;" scrolling="no" data-name="pb-iframe-player" src="https://www.podbean.com/media/player/p6w7q-d6ad7e?from=yiiadmin&download=1&version=1&skin=1&btn-skin=107&auto=0&share=1&fonts=Helvetica&download=1&rtl=0&pbad=1" width="100%" height="122"></iframe>
<p>While at a previous employer I overheard the following conversation between a newly hired researcher and a local manager:</p>
<blockquote>
<p><strong>Researcher</strong>: So, what do you want to look at?<br />
<strong>Manager</strong>: I don’t know, you are the researcher.</p>
</blockquote>
<blockquote>
<p><strong>Researcher</strong>: Yes, but what do you want me to research?<br />
<strong>Manager</strong>: You’re the researcher, you figure that out.</p>
</blockquote>
<blockquote>
<p><strong>Researcher</strong>: Yes, but I’m new here, so which areas need my attention?<br />
<strong>Manager</strong>: You’re the researcher, go do research.</p>
</blockquote>
<p>This is a valid question: how do you know what to research? Any researcher, in any organisation, needs to be able to explain what they are researching and why that is of value. At the end of the day research costs money and we need to be able to explain where we are spending it and why.</p>
<h2 id="where-does-research-funding-come-from">Where does research funding come from?</h2>
<p>Research funding comes from a number of locations and each location drags the research in different ways. In general, there are two main types of research, short term and long term.</p>
<h3 id="short-term-research">Short Term Research</h3>
<p>The funding for short term research often comes from development teams who are directly creating commercial products. Often the issues they face are immediate pain points, these are typically related to new technology adoption, or the application of an unfamiliar approach to address an issue they are facing. These teams lack the resources to address the issue themselves and so they will research out to a research e team. In these situations, the research is strongly directed and it is normally a “Show me how to fix this issue now please” type of request. At best this short-term research will be 12 – 24 months ahead of the current product and is essentially exploring a future version of an existing product.</p>
<h3 id="long-term-research">Long Term Research</h3>
<p>In contrast, funding for long term research typically comes from a dedicated “research” fund. Unlike the direct need of short-term research, the longer-term research funding can support independent, non-business unit or development team directed research. That is not to say this research will be completely divorced from the development team, after all, it still has to result in something that can be sold. However, the scope of the research can be further into the future.</p>
<p>When working on long term research every researcher should be able to answer these two questions;</p>
<ol>
<li>What exactly are they researching?</li>
<li>How is this research going to be valuable to the overall organisation?</li>
</ol>
<p>I have a process which can help address these two points. In fact, it came about from overhearing that conversation mentioned at the beginning of the post. However, before we delve into the process, we need to think about what research is first.</p>
<h2 id="research-theory">Research Theory</h2>
<p>When we think about conducting research, we often think about running experiments with mad scientists in lab coats pouring multi-coloured chemicals into test tubes. But before we get to the fun part of playing with dangerous chemicals, or, in my case, as a Computer Scientist, playing with code and processors, we have to work out what we are going to do and why.
There are three key methods of research:</p>
<ul>
<li>
<p><strong>Exploratory Research</strong>; identify and define the problem or question to research</p>
</li>
<li>
<p><strong>Constructive Research</strong>; test theories and propose solutions to a problem or question</p>
</li>
<li>
<p><strong>Empirical Research</strong>; test the feasibility of the solution</p>
</li>
</ul>
<p>You can read more about <a href="https://en.wikipedia.org/wiki/Research#Research_methods">methods of research in this fascinating Wikipedia post</a>.</p>
<p>These methods can be thought of as three key questions:</p>
<ol>
<li>What problem should we research?</li>
<li>What hypothesis do we have on how we can address the problem?</li>
<li>What experiments can we design and execute to test the hypothesis?</li>
</ol>
<h3 id="theory-in-practice">Theory in Practice</h3>
<p>I am currently in New Jersey and what better example of posing the three questions above than the research that Thomas Edison conducted. In 1878 Edison started research on the creation of his lightbulb. The lightbulb itself had already been invented: in fact, the idea of electric light had been demonstrated 78 years before by Volta and more recent work on a bulb had been conducted by Joseph Swan who launched his bulb in 1875.</p>
<p><img src="/industrialresearch/assets/images/edison-and-swan-lightbulb.png" alt="Edison (left) and Swan (right) the co-inventors of the electric light bulb" /></p>
<p><em>Edison (left) and Swan (right) the co-inventors of the electric light bulb</em></p>
<p>I am currently in New Jersey and what better example of posing the three questions above than the research that Thomas Edison conducted. In 1878 Edison started research on the creation of his lightbulb. The lightbulb itself had already been invented: in fact, the idea of electric light had been demonstrated 78 years before by Volta and more recent work on a bulb had been conducted by Joseph Swan who launched his bulb in 1875.</p>
<p>Swan’s bulbs had faced issues with the vacuum seal and the filament he used was carbonised paper. <a href="https://www.wired.com/2009/12/1218joseph-swan-electric-bulb/">The bulb stayed lit for just 13 hours</a>. The low resistance of carbonised paper meant that the bulb required a lot of power which, in turn, meant it required thick, expensive, copper cables. So, Edison’s work was in essence conducting research in an already crowded marketplace. This is an example of applied research with a <a href="https://mcwoods.online/industrialresearch/podcast/episode/2020/03/12/What-is-Industrial-Research.html">high TRL level</a>. We can also use this example to illustrate the three key research questions above:</p>
<ol>
<li>
<p><strong>What problem should we research?</strong><br />
How do we create a low power, high resistance, long life lightbulb that is cost effective to manufacture?</p>
</li>
<li>
<p><strong>What hypothesis do we have on how we can address the problem?</strong><br />
Existing research showed that different filaments placed in a vacuum would glow instead of burning, therefore an alternative filament could offer higher resistance and less power. New methods of pumping out the air from a bulb need to be developed to reduce the manufacturing cost.</p>
</li>
<li>
<p><strong>What experiments can we design and execute to test hypothesis?</strong><br />
Testing a selection of filament materials in a vacuum with a lower current to understand which glowed best and longer. Search for and identify methods of creating a vacuum.</p>
</li>
</ol>
<p>The research led Edison to identify both solutions and the cheap affordable lightbulb was born.</p>
<p>Often, even as professionals, we get caught up on the experiments. But when you take these three questions in logical order you can see that conducting experiments is at the end of the process as it is the last thing we need to do. The first thing is working out what we are going to research and why. This is where the Research Roadmap Process comes in.</p>
<h1 id="research-roadmap-process">Research Roadmap Process</h1>
<p>A research roadmap is a list of all the experiments that need to be performed and the order in which they should be conducted. Creating this roadmap is a huge challenge because you have to have already answered each of the three key research questions. The Roadmap Process is a useful tool to create and implement a rolling, repeating process which allows you to address each of these questions. There are three core phases or questions that we need to answer. Each one helps to identify and refine the problems we need to address and helps to ensure that the research we are proposing or conducting remains aligned with our employers’ / company’s objectives. These three phases are as follows and they map back to the three research questions I’ve identified above:</p>
<ul>
<li>
<p><strong>Phase 1</strong>: What problem should we research? – Exploratory Research which gives us a list of questions to address.</p>
</li>
<li>
<p><strong>Phase 2</strong>: What solutions can we propose to these questions? – Constructive Research which gives is a refined list of questions, along with possible solutions to them (hypothesis) which we need to investigate.</p>
</li>
<li>
<p><strong>Phase 3</strong>: What experiments do we run? – Empirical research which gives us a list of experiments we need to run, and their priority order.</p>
</li>
</ul>
<p>I intend to cover each of these phases in greater depth in future posts / episodes but the following will give you a clear idea of how each phase works.</p>
<p><img src="/industrialresearch/assets/images/roadmap-process-overview.png" alt="Figure 2 Research Roadmap Process" /></p>
<p><em>Figure 2 Research Roadmap Process</em></p>
<h2 id="phase-1-what-problem-should-we-research">Phase 1: What problem should we research?</h2>
<p>This is a huge question and one which has rightly filled books. One way for an industrial researcher to answer this would be to think what the future might look like. Technology trends give us a trajectory on how technology is evolving but we need to combine this with observations into the business, social and policy landscape and, of course, competitor analysis. These inputs are combined to create a vision of the future which then leads to a whole bunch of open questions like:</p>
<ul>
<li>Is this vision valid? – Are there experiments I can run to test it?</li>
<li>How confident are we of the future vision?</li>
<li>How far away, in time, is the vision.</li>
</ul>
<p>No-one can predict the future precisely, if we could we’d all have won the lotto by now and it would make sports games really boring! - A researcher needs to be confident enough that the future vision is viable and addresses the needs of our company / employer. Once we’ve got our future vision, we can generate a list of technical questions which need to be solved in order for the future vision to become a reality. It is very likely that at this stage the list of questions you can produce is going to be huge.</p>
<h3 id="back-to-edison">Back to Edison</h3>
<p>In the light bulb example, Edison is quoted as envisioning a future in which he <a href="https://quoteinvestigator.com/2012/04/10/rich-burn-candles/">explained</a>:</p>
<blockquote>
<p>“After the electric light goes into general use,” said he, “none but the
extravagant will burn tallow candles.”</p>
</blockquote>
<p>We can use this as Edison’s vision of the future.</p>
<h2 id="phase-2-what-solutions-can-we-propose-to-these-questions">Phase 2: What solutions can we propose to these questions?</h2>
<p>From a researcher’s point of view, once the vision has been defined and we’ve identified the technical challenges in creating that vision, we can move on to think about how we might solve or address the technical challenges we’ve identified. This is where traditional state of the art research comes in. Many of the questions on our list of technical problems will be issues that we ourselves perceive to be unknown items. However, reviewing this list and doing a quick literature review allows you to cull many questions. I often find that issues I thought needed to be addressed have already been solved; these can be removed from our list. The remaining questions will fall into two categories; those that have one or more possible solutions and we are not sure which one is best; or problems for which we have no known solution and we may need to invent one. In my experience the vast majority of problems have a solution, or partial solution, which can be built from existing research, products, or open source software. The list of problems in which there are no current solution is very small.</p>
<p>This process also lets us refine our vision of the future. Understanding that some of these issues have already been addressed lets us update our vision. So often Phase 1 and Phase 2 will happen in small stages; first some future vision creating, then some research based on the vision which leads to an updated vision and some more research.</p>
<p>Ultimately, however, once we’ve completed this phase, we will have a clearer view on the future vision and a list of questions with proposed possible solutions, or approaches which might help. It is important to prioritize the list of questions as this allows us to ensure that the research, we are looking to conduct aligns to the business needs of our company. There are many occasions where research can deliver value both in creating the longer-term future vision, but it can also help our company to achieve nearer term goals.</p>
<h3 id="back-to-edison-1">Back to Edison</h3>
<p>Edison didn’t have to solve the vacuum pump problem. It turned out that a German/British inventor called <a href="https://en.wikipedia.org/wiki/Hermann_Sprengel">Herman Sprengel had invented the Sprengel Pump</a> which could reduce the amount of air in a chamber to one-millionth of its volume. This pump ended up being used by both Swan and Edison. Here Edison was able to refine the vision of the future and rescope it, removing the vacuum problem and reassessing the vision of the future before continuing.</p>
<h2 id="phase-3-what-experiments-do-we-run">Phase 3: What experiments do we run?</h2>
<p>Once we have our prioritized list, we can start to outline the experiments we need to perform. If we’ve already identified multiple possible solutions to a problem, we can test and validate each solution against our own internal product specific needs. If we need to create a new technology, we can identify approaches which may help and outline an architecture we would need to implement. Even then we also need to define a set of criteria in which to assess the solution we are creating and, again, this would be our own internal product specific criteria.</p>
<p>All of this enables us to design the experiments we need to run. This is important as not only does it tell us what we have to do, it gives us an outline cost for each of the experiments. That cost is made up of researcher time, the material / hardware required, etc. Along with this, we are also able to assess the impact on our research each of the proposed experiments would provide.</p>
<h3 id="back-to-edison-2">Back to Edison</h3>
<p>Edison had now refined the problem space, reducing it to just addressing the issue of filament resistance. Now the challenge became finding a workable, cheap and effective solution. This allowed Edison to define a set of experiments exploring and evaluating different filament materials.</p>
<h2 id="what-do-the-three-phases-of-the-research-roadmap-process-give-you">What do the three Phases of the Research Roadmap Process Give you?</h2>
<p>I ran through this process with my team at my current employer and I got as many of the researchers as possible involved. I work with some amazing researchers who have fantastic ideas. Creating a coherent vision is hard and many hands helped make light work. In addition the researchers go to take ownership of parts of the vision. Having this structure allowed us to speak to management in the various business units we support and ask them questions about the strategic direction the company’s products and services were going in (Phase 1 and Phase 2 iteration). Indeed, through the research conducted in Phase 2 we were able to identify trends in software and hardware which may provide clues to the best solutions to some of problems we found.</p>
<p>The first pass through this process is hard as there is no initial vision and we needed to create one. This takes a lot of work, reading the current research papers and understanding the state of the art, speaking to people from various parts of the organisation and customers where possible. However, once this hard work has been done, keeping the future vision fresh is a recurring iterative process.</p>
<p>The benefit of running through the Research Roadmap Process is that by the time you’ve completed the first pass, you will have:</p>
<ol>
<li>Created a compelling future vision which aligns to your companies needs</li>
<li>Identified problems and potential solutions for achieving this vision</li>
<li>Been able to produce a prioritized list of practical experiments, with
costs, and advantages.</li>
</ol>
<p>All of this together allows you to budget and plan the experiments you want to perform.</p>
<h1 id="conducting-the-research">Conducting the Research</h1>
<p>Of course, once we have the list of the experiments we would like to perform, the next challenge is performing them. The results of conducting the research also feedback and provide valuable input into what the vision of the future will look like. Which in turn allows us to update the questions we need to ask and the hypothesis we need to study.</p>
<h2 id="homework">Homework</h2>
<p>Take a look around your team and company. Do you have a future vision you are trying to achieve? What processes do you use to create the vision for the future? I’d love to share in your experiences so we can all learn together. Please feel free to drop me a line on <a href="https://twitter.com/mcwoods">Twitter as @mcwoods</a> and let me know your thoughts. If you haven’t seen or got a vision for the future of your company, or team, then try out this approach and let me know how you get on.</p>While at a previous employer I overheard the following conversation between a newly hired researcher and a local manager: Researcher: So, what do you want to look at? Manager: I don’t know, you are the researcher. Researcher: Yes, but what do you want me to research? Manager: You’re the researcher, you figure that out. Researcher: Yes, but I’m new here, so which areas need my attention? Manager: You’re the researcher, go do research. This is a valid question: how do you know what to research? Any researcher, in any organisation, needs to be able to explain what they are researching and why that is of value. At the end of the day research costs money and we need to be able to explain where we are spending it and why. Where does research funding come from? Research funding comes from a number of locations and each location drags the research in different ways. In general, there are two main types of research, short term and long term. Short Term Research The funding for short term research often comes from development teams who are directly creating commercial products. Often the issues they face are immediate pain points, these are typically related to new technology adoption, or the application of an unfamiliar approach to address an issue they are facing. These teams lack the resources to address the issue themselves and so they will research out to a research e team. In these situations, the research is strongly directed and it is normally a “Show me how to fix this issue now please” type of request. At best this short-term research will be 12 – 24 months ahead of the current product and is essentially exploring a future version of an existing product. Long Term Research In contrast, funding for long term research typically comes from a dedicated “research” fund. Unlike the direct need of short-term research, the longer-term research funding can support independent, non-business unit or development team directed research. That is not to say this research will be completely divorced from the development team, after all, it still has to result in something that can be sold. However, the scope of the research can be further into the future. When working on long term research every researcher should be able to answer these two questions; What exactly are they researching? How is this research going to be valuable to the overall organisation? I have a process which can help address these two points. In fact, it came about from overhearing that conversation mentioned at the beginning of the post. However, before we delve into the process, we need to think about what research is first. Research Theory When we think about conducting research, we often think about running experiments with mad scientists in lab coats pouring multi-coloured chemicals into test tubes. But before we get to the fun part of playing with dangerous chemicals, or, in my case, as a Computer Scientist, playing with code and processors, we have to work out what we are going to do and why. There are three key methods of research: Exploratory Research; identify and define the problem or question to research Constructive Research; test theories and propose solutions to a problem or question Empirical Research; test the feasibility of the solution You can read more about methods of research in this fascinating Wikipedia post. These methods can be thought of as three key questions: What problem should we research? What hypothesis do we have on how we can address the problem? What experiments can we design and execute to test the hypothesis? Theory in Practice I am currently in New Jersey and what better example of posing the three questions above than the research that Thomas Edison conducted. In 1878 Edison started research on the creation of his lightbulb. The lightbulb itself had already been invented: in fact, the idea of electric light had been demonstrated 78 years before by Volta and more recent work on a bulb had been conducted by Joseph Swan who launched his bulb in 1875. Edison (left) and Swan (right) the co-inventors of the electric light bulb I am currently in New Jersey and what better example of posing the three questions above than the research that Thomas Edison conducted. In 1878 Edison started research on the creation of his lightbulb. The lightbulb itself had already been invented: in fact, the idea of electric light had been demonstrated 78 years before by Volta and more recent work on a bulb had been conducted by Joseph Swan who launched his bulb in 1875. Swan’s bulbs had faced issues with the vacuum seal and the filament he used was carbonised paper. The bulb stayed lit for just 13 hours. The low resistance of carbonised paper meant that the bulb required a lot of power which, in turn, meant it required thick, expensive, copper cables. So, Edison’s work was in essence conducting research in an already crowded marketplace. This is an example of applied research with a high TRL level. We can also use this example to illustrate the three key research questions above: What problem should we research? How do we create a low power, high resistance, long life lightbulb that is cost effective to manufacture? What hypothesis do we have on how we can address the problem? Existing research showed that different filaments placed in a vacuum would glow instead of burning, therefore an alternative filament could offer higher resistance and less power. New methods of pumping out the air from a bulb need to be developed to reduce the manufacturing cost. What experiments can we design and execute to test hypothesis? Testing a selection of filament materials in a vacuum with a lower current to understand which glowed best and longer. Search for and identify methods of creating a vacuum. The research led Edison to identify both solutions and the cheap affordable lightbulb was born. Often, even as professionals, we get caught up on the experiments. But when you take these three questions in logical order you can see that conducting experiments is at the end of the process as it is the last thing we need to do. The first thing is working out what we are going to research and why. This is where the Research Roadmap Process comes in. Research Roadmap Process A research roadmap is a list of all the experiments that need to be performed and the order in which they should be conducted. Creating this roadmap is a huge challenge because you have to have already answered each of the three key research questions. The Roadmap Process is a useful tool to create and implement a rolling, repeating process which allows you to address each of these questions. There are three core phases or questions that we need to answer. Each one helps to identify and refine the problems we need to address and helps to ensure that the research we are proposing or conducting remains aligned with our employers’ / company’s objectives. These three phases are as follows and they map back to the three research questions I’ve identified above: Phase 1: What problem should we research? – Exploratory Research which gives us a list of questions to address. Phase 2: What solutions can we propose to these questions? – Constructive Research which gives is a refined list of questions, along with possible solutions to them (hypothesis) which we need to investigate. Phase 3: What experiments do we run? – Empirical research which gives us a list of experiments we need to run, and their priority order. I intend to cover each of these phases in greater depth in future posts / episodes but the following will give you a clear idea of how each phase works. Figure 2 Research Roadmap Process Phase 1: What problem should we research? This is a huge question and one which has rightly filled books. One way for an industrial researcher to answer this would be to think what the future might look like. Technology trends give us a trajectory on how technology is evolving but we need to combine this with observations into the business, social and policy landscape and, of course, competitor analysis. These inputs are combined to create a vision of the future which then leads to a whole bunch of open questions like: Is this vision valid? – Are there experiments I can run to test it? How confident are we of the future vision? How far away, in time, is the vision. No-one can predict the future precisely, if we could we’d all have won the lotto by now and it would make sports games really boring! - A researcher needs to be confident enough that the future vision is viable and addresses the needs of our company / employer. Once we’ve got our future vision, we can generate a list of technical questions which need to be solved in order for the future vision to become a reality. It is very likely that at this stage the list of questions you can produce is going to be huge. Back to Edison In the light bulb example, Edison is quoted as envisioning a future in which he explained: “After the electric light goes into general use,” said he, “none but the extravagant will burn tallow candles.” We can use this as Edison’s vision of the future. Phase 2: What solutions can we propose to these questions? From a researcher’s point of view, once the vision has been defined and we’ve identified the technical challenges in creating that vision, we can move on to think about how we might solve or address the technical challenges we’ve identified. This is where traditional state of the art research comes in. Many of the questions on our list of technical problems will be issues that we ourselves perceive to be unknown items. However, reviewing this list and doing a quick literature review allows you to cull many questions. I often find that issues I thought needed to be addressed have already been solved; these can be removed from our list. The remaining questions will fall into two categories; those that have one or more possible solutions and we are not sure which one is best; or problems for which we have no known solution and we may need to invent one. In my experience the vast majority of problems have a solution, or partial solution, which can be built from existing research, products, or open source software. The list of problems in which there are no current solution is very small. This process also lets us refine our vision of the future. Understanding that some of these issues have already been addressed lets us update our vision. So often Phase 1 and Phase 2 will happen in small stages; first some future vision creating, then some research based on the vision which leads to an updated vision and some more research. Ultimately, however, once we’ve completed this phase, we will have a clearer view on the future vision and a list of questions with proposed possible solutions, or approaches which might help. It is important to prioritize the list of questions as this allows us to ensure that the research, we are looking to conduct aligns to the business needs of our company. There are many occasions where research can deliver value both in creating the longer-term future vision, but it can also help our company to achieve nearer term goals. Back to Edison Edison didn’t have to solve the vacuum pump problem. It turned out that a German/British inventor called Herman Sprengel had invented the Sprengel Pump which could reduce the amount of air in a chamber to one-millionth of its volume. This pump ended up being used by both Swan and Edison. Here Edison was able to refine the vision of the future and rescope it, removing the vacuum problem and reassessing the vision of the future before continuing. Phase 3: What experiments do we run? Once we have our prioritized list, we can start to outline the experiments we need to perform. If we’ve already identified multiple possible solutions to a problem, we can test and validate each solution against our own internal product specific needs. If we need to create a new technology, we can identify approaches which may help and outline an architecture we would need to implement. Even then we also need to define a set of criteria in which to assess the solution we are creating and, again, this would be our own internal product specific criteria. All of this enables us to design the experiments we need to run. This is important as not only does it tell us what we have to do, it gives us an outline cost for each of the experiments. That cost is made up of researcher time, the material / hardware required, etc. Along with this, we are also able to assess the impact on our research each of the proposed experiments would provide. Back to Edison Edison had now refined the problem space, reducing it to just addressing the issue of filament resistance. Now the challenge became finding a workable, cheap and effective solution. This allowed Edison to define a set of experiments exploring and evaluating different filament materials. What do the three Phases of the Research Roadmap Process Give you? I ran through this process with my team at my current employer and I got as many of the researchers as possible involved. I work with some amazing researchers who have fantastic ideas. Creating a coherent vision is hard and many hands helped make light work. In addition the researchers go to take ownership of parts of the vision. Having this structure allowed us to speak to management in the various business units we support and ask them questions about the strategic direction the company’s products and services were going in (Phase 1 and Phase 2 iteration). Indeed, through the research conducted in Phase 2 we were able to identify trends in software and hardware which may provide clues to the best solutions to some of problems we found. The first pass through this process is hard as there is no initial vision and we needed to create one. This takes a lot of work, reading the current research papers and understanding the state of the art, speaking to people from various parts of the organisation and customers where possible. However, once this hard work has been done, keeping the future vision fresh is a recurring iterative process. The benefit of running through the Research Roadmap Process is that by the time you’ve completed the first pass, you will have: Created a compelling future vision which aligns to your companies needs Identified problems and potential solutions for achieving this vision Been able to produce a prioritized list of practical experiments, with costs, and advantages. All of this together allows you to budget and plan the experiments you want to perform. Conducting the Research Of course, once we have the list of the experiments we would like to perform, the next challenge is performing them. The results of conducting the research also feedback and provide valuable input into what the vision of the future will look like. Which in turn allows us to update the questions we need to ask and the hypothesis we need to study. Homework Take a look around your team and company. Do you have a future vision you are trying to achieve? What processes do you use to create the vision for the future? I’d love to share in your experiences so we can all learn together. Please feel free to drop me a line on Twitter as @mcwoods and let me know your thoughts. If you haven’t seen or got a vision for the future of your company, or team, then try out this approach and let me know how you get on.What is Industrial Research?2020-03-12T10:00:00-04:002020-03-12T10:00:00-04:00https://mcwoods.online/industrialresearch/podcast/episode/2020/03/12/What-is-Industrial-Research<iframe title="What is Industrial Research?" style="border: none;" scrolling="no" data-name="pb-iframe-player" src="https://www.podbean.com/media/player/spmj6-d61b6e?from=yiiadmin&download=1&version=1&skin=1&btn-skin=107&auto=0&share=1&fonts=Helvetica&download=1&rtl=0&pbad=1" width="100%" height="122"></iframe>
<h1 id="industrial-research">Industrial Research</h1>
<p>There is a difference between academic and industrial research. It is not the quality of the research, good research shines wherever it is conducted, but it is the research purpose that changes. The change in purpose shapes how companies conduct research and why we in industrial research often focus on different aspects than our academic colleagues.</p>
<h2 id="defining-research">Defining Research</h2>
<p>If you do a Google search for “What is research?” you’ll see the following:</p>
<blockquote>
<p><strong>Research:</strong><br />
<strong>noun</strong>: the systematic investigation into and study of materials and
sources in order to establish facts and research new conclusions.<br />
<strong>verb</strong>: investigate systematically</p>
</blockquote>
<p>Within the academic world it is possible to systematically design and run a set of experiments to validate or disprove a hypothesis. The results of the research could be a positive finding “New technology Y provides a 20% performance increase over existing methods” or it could be a negative finding “Hey method Y really sucks; it provides a 20% performance penalty. I suggest no one ever uses it”. Both outcomes are equally valid and important contributions to the body of knowledge that the world shares.</p>
<h2 id="defining-industrial-research">Defining Industrial Research</h2>
<p>Unfortunately, Google doesn’t provide a definition of “Industrial Research”, but the European Union provides its own official definition which is pretty much spot on:</p>
<blockquote>
<p>‘<strong>industrial research</strong>’ - <br />
means the planned research or critical investigation aimed at the acquisition of new knowledge and skills for developing new products, processes or services or for bringing about a significant improvement in existing products, processes or services.</p>
</blockquote>
<p>Industrial research is similar to, but distinct from, academic research. Both fields share the same rigour, although their goals are different. Within the academic world success can be defined as discovering new knowledge. But within industrial research success is new knowledge that leads to new products, processes or services. It is a subtle but important difference which affects the type of research and hence work that industrial research teams undertake.</p>
<h1 id="introducing-the-technology-readiness-level-trl">Introducing the Technology Readiness Level (TRL)</h1>
<p>Research can mean a lot of things, from understanding some fundamental law of physics through to producing a working prototype of a new product. This is a huge scope and it makes talking about research difficult. NASA had the same issue; they were speaking to researchers at a range of different organizations and they needed a way to understand how far their research was from something that could actually fly. To address this NASA invented the TRL scale (Technology Readiness Level). This approach is so useful that it has also been adopted by other US Government departments, universities around the world and the European Union. NASA’s TRL scale currently has 9 levels, with level 1 (TRL1) being basic research. Basic research is core fundamental research, like understanding the structure of an atom for instance. TRL9 is a product that is ready to purchase and use out of the box. The intermediate levels describe a process from TRL1 to TRL9, so TRL 4 is basic proof of concept of a part of an idea that works in a Lab, TRL 5 is the same proof of concept working in the real world. TRL6, 7, and 8 describe a proof of concept as it matures from tech demo to a pre-production system.</p>
<p><img src="/industrialresearch/assets/images/TRL.png" alt="TRL Scale Diagram" /></p>
<p>You can read more about <a href="https://en.wikipedia.org/wiki/Technology_readiness_level">TRL scale on Wikipedia, including details on the NASA scale</a>. You can read more about the <a href="https://ec.europa.eu/research/participants/data/ref/h2020/wp/2014_2015/annexes/h2020-wp1415-annex-g-trl_en.pdf">EU’s TRL scale on the EU website</a>.</p>
<h1 id="how-companies-structure-research">How Companies Structure Research</h1>
<p>Because of this push to deliver research which leads to new products, processes or services, it affects where companies invest into research. There is no hard and fast rule, however it is not unusual to see a large number of organizations invest in higher TRL research. This means that most organizations are not investing in developing a core technology, but are investing in how to apply core technology or research to their day to day business. This is often referred to as applied research.</p>
<p>There are a number of other organizations who take a longer-term view, organizations like Bell Labs are famous for conducting both fundamental and applied research. These types of organizations have a longer-term approach to the market place and to research. This decision basically comes down to finances and how much time (money) they will spend researching before needing to see a return on their investment. However, as a general rule, most industrial research teams will focus on TRL4 and up; some focus exclusively on bringing technology to market and will and will therefore aim for proof of concept stage and higher.</p>
<h2 id="industrial-research-and-the-business-landscape">Industrial Research and the Business Landscape</h2>
<p>The purpose of industrial research is to improve an existing product, service or to create a new product or service. As technical researchers we often have a great understanding of the underlying technology and that is important, but it is not enough by its self. An industrial researcher also needs to understand the business environment.</p>
<p>The business environment is everything from the cost of a product or service through to how it can be marketed, identifying the ultimate customer, finding out what competitors are doing, and even what the law and the policy makers are saying. These all influence the viability of a product, concept and technology.</p>
<p>The business environment is constantly changing with new technologies and the introduction of new business models. So, it is important to not only consider the business landscape today, but what the landscape of tomorrow might look like. There are a whole body of researchers dedicated to trying to predict or visualise what the future might look like.</p>
<h1 id="the-industrial-research-advantage">The Industrial Research Advantage</h1>
<p>I had the chance to meet Donald Strickland. He was giving a course I was attending. As I recall he stood at the front of lecture room and asked all of us
the same question, “<em>What is the role of a CEO?</em>”</p>
<p>“<em>To run a company?</em>” someone behind me shouted.</p>
<p>“<em>No, that’s the COO - Chief Operating Officer</em>” Donald answered, he went on to explain, that the role of a CEO was to discover how your business is going to be disrupted and destroyed by a competitor and to go ahead and do that before they do.</p>
<p>This advice stuck with me, because Donald Strickland is the guy that presented the idea of a consumer digital camera to Kodak. At the time Kodak was a large multinational which made cameras and film for everyone, from hospitals and X-Ray machines, through to movie studios and all of the available consumer cameras at the time. The Board of Kodak saw Donald’s proposal as a threat, they were worried, that they made digital cameras people would stop buying film. As a result, Kodak turned the proposal down. Donald tried three times and each time it was refused. Donald left Kodak and joined Apple.</p>
<p>The board of Kodak were right, people would stop buying film. Kodak’s competitors introduced digital cameras and in 2012 Kodak went out of business.</p>
<p>The world is constantly changing. While we can make money selling the technology of today, tomorrow it will be out of date. We need to be open to understanding both the technology and business market place, when we do that, we can be ready, as Donald put it, to disrupt our market place before a competitor does it.</p>
<p>Ultimately Industrial research provides CEOs and business owners the ability to envision new products and services but it also enables them to explore how their industry could be disrupted and how they can turn the disruption into an advantage. As a consequence, research teams in most companies will sit close to the CEO or ‘c suite’ so that they can provide insight and future vision to help shape the company’s future.</p>
<h1 id="next-time">Next time</h1>
<p>In this post I’ve talked a lot of about what research is but not how to conduct it. In the next article we will start to explore the methods of research and how it is conducted in industrial departments. In the meantime, when you look around your own workplace and the products or services your company offers, consider if there are any areas where you think disruption to production might happen.</p>Industrial Research There is a difference between academic and industrial research. It is not the quality of the research, good research shines wherever it is conducted, but it is the research purpose that changes. The change in purpose shapes how companies conduct research and why we in industrial research often focus on different aspects than our academic colleagues. Defining Research If you do a Google search for “What is research?” you’ll see the following: Research: noun: the systematic investigation into and study of materials and sources in order to establish facts and research new conclusions. verb: investigate systematically Within the academic world it is possible to systematically design and run a set of experiments to validate or disprove a hypothesis. The results of the research could be a positive finding “New technology Y provides a 20% performance increase over existing methods” or it could be a negative finding “Hey method Y really sucks; it provides a 20% performance penalty. I suggest no one ever uses it”. Both outcomes are equally valid and important contributions to the body of knowledge that the world shares. Defining Industrial Research Unfortunately, Google doesn’t provide a definition of “Industrial Research”, but the European Union provides its own official definition which is pretty much spot on: ‘industrial research’ - means the planned research or critical investigation aimed at the acquisition of new knowledge and skills for developing new products, processes or services or for bringing about a significant improvement in existing products, processes or services. Industrial research is similar to, but distinct from, academic research. Both fields share the same rigour, although their goals are different. Within the academic world success can be defined as discovering new knowledge. But within industrial research success is new knowledge that leads to new products, processes or services. It is a subtle but important difference which affects the type of research and hence work that industrial research teams undertake. Introducing the Technology Readiness Level (TRL) Research can mean a lot of things, from understanding some fundamental law of physics through to producing a working prototype of a new product. This is a huge scope and it makes talking about research difficult. NASA had the same issue; they were speaking to researchers at a range of different organizations and they needed a way to understand how far their research was from something that could actually fly. To address this NASA invented the TRL scale (Technology Readiness Level). This approach is so useful that it has also been adopted by other US Government departments, universities around the world and the European Union. NASA’s TRL scale currently has 9 levels, with level 1 (TRL1) being basic research. Basic research is core fundamental research, like understanding the structure of an atom for instance. TRL9 is a product that is ready to purchase and use out of the box. The intermediate levels describe a process from TRL1 to TRL9, so TRL 4 is basic proof of concept of a part of an idea that works in a Lab, TRL 5 is the same proof of concept working in the real world. TRL6, 7, and 8 describe a proof of concept as it matures from tech demo to a pre-production system. You can read more about TRL scale on Wikipedia, including details on the NASA scale. You can read more about the EU’s TRL scale on the EU website. How Companies Structure Research Because of this push to deliver research which leads to new products, processes or services, it affects where companies invest into research. There is no hard and fast rule, however it is not unusual to see a large number of organizations invest in higher TRL research. This means that most organizations are not investing in developing a core technology, but are investing in how to apply core technology or research to their day to day business. This is often referred to as applied research. There are a number of other organizations who take a longer-term view, organizations like Bell Labs are famous for conducting both fundamental and applied research. These types of organizations have a longer-term approach to the market place and to research. This decision basically comes down to finances and how much time (money) they will spend researching before needing to see a return on their investment. However, as a general rule, most industrial research teams will focus on TRL4 and up; some focus exclusively on bringing technology to market and will and will therefore aim for proof of concept stage and higher. Industrial Research and the Business Landscape The purpose of industrial research is to improve an existing product, service or to create a new product or service. As technical researchers we often have a great understanding of the underlying technology and that is important, but it is not enough by its self. An industrial researcher also needs to understand the business environment. The business environment is everything from the cost of a product or service through to how it can be marketed, identifying the ultimate customer, finding out what competitors are doing, and even what the law and the policy makers are saying. These all influence the viability of a product, concept and technology. The business environment is constantly changing with new technologies and the introduction of new business models. So, it is important to not only consider the business landscape today, but what the landscape of tomorrow might look like. There are a whole body of researchers dedicated to trying to predict or visualise what the future might look like. The Industrial Research Advantage I had the chance to meet Donald Strickland. He was giving a course I was attending. As I recall he stood at the front of lecture room and asked all of us the same question, “What is the role of a CEO?” “To run a company?” someone behind me shouted. “No, that’s the COO - Chief Operating Officer” Donald answered, he went on to explain, that the role of a CEO was to discover how your business is going to be disrupted and destroyed by a competitor and to go ahead and do that before they do. This advice stuck with me, because Donald Strickland is the guy that presented the idea of a consumer digital camera to Kodak. At the time Kodak was a large multinational which made cameras and film for everyone, from hospitals and X-Ray machines, through to movie studios and all of the available consumer cameras at the time. The Board of Kodak saw Donald’s proposal as a threat, they were worried, that they made digital cameras people would stop buying film. As a result, Kodak turned the proposal down. Donald tried three times and each time it was refused. Donald left Kodak and joined Apple. The board of Kodak were right, people would stop buying film. Kodak’s competitors introduced digital cameras and in 2012 Kodak went out of business. The world is constantly changing. While we can make money selling the technology of today, tomorrow it will be out of date. We need to be open to understanding both the technology and business market place, when we do that, we can be ready, as Donald put it, to disrupt our market place before a competitor does it. Ultimately Industrial research provides CEOs and business owners the ability to envision new products and services but it also enables them to explore how their industry could be disrupted and how they can turn the disruption into an advantage. As a consequence, research teams in most companies will sit close to the CEO or ‘c suite’ so that they can provide insight and future vision to help shape the company’s future. Next time In this post I’ve talked a lot of about what research is but not how to conduct it. In the next article we will start to explore the methods of research and how it is conducted in industrial departments. In the meantime, when you look around your own workplace and the products or services your company offers, consider if there are any areas where you think disruption to production might happen.What is it you do here anyway?2020-03-10T13:28:23-04:002020-03-10T13:28:23-04:00https://mcwoods.online/industrialresearch/podcast/episode/2020/03/10/What-is-it-you-do-anyway<iframe title="What is it you do here anyway?" style="border: none;" scrolling="no" data-name="pb-iframe-player" src="https://www.podbean.com/media/player/b9d6d-d5fcfc?from=yiiadmin&download=1&version=1&skin=1&btn-skin=107&auto=0&share=1&fonts=Helvetica&download=1&rtl=0&pbad=1" width="100%" height="122"></iframe>
<h1 id="what-do-you-do-here">“What do you do here?”</h1>
<p>In one of my past roles I found myself working at Microsoft. In any large organisation, especially one with such an iconic role in the computer industry, the founders become something of a legend.</p>
<p>The “old hands” would tell stories of presenting to Bill Gates. They’d talk about how he would react to a presentation he didn’t agree with. According to the legend, he’d stop a meeting, lean over and ask the presenter “What is it do you do here anyway?”.</p>
<p>I never got the chance to present to Bill Gates, but the question is a good one, particularly when you work in a role that not many people know about. Answering that question well makes you think about how your conducting your job, and what you can do to do it better.</p>
<h1 id="-whoami">$ whoami</h1>
<p>I’m <a href="https://www.linkedin.com/in/woodsmc/">Chris Woods, and I’m an Industrial Researcher</a>.</p>
<p>This mini-series of blog posts will cover Industrial Research within the software industry. It’s going to cover how we conduct research and how that helps us to anticipate changes to our industry.</p>
<p>If you’re an industrial researcher yourself, this series should help you reflect on the approaches you use during your research. For anyone else, the techniques and tricks we cover can be used as part of a wider corporate strategy, to help understand what is happening in the industry and how things may develop.</p>
<h1 id="what-prompted-me-to-write">What prompted me to write?</h1>
<p>It was my first industrial research role, and my employer asked me to update my CV with the research I had conducted during my first 12 months. One of my mum’s skills is professional proof reading, and I sent her my CV for a quick once over. She called me that night after I got home, “What is it you do anyway?”</p>