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How to enable collaborative tech discovery for your team

Collaborative tech discovery is when your team discovers and tracks new tech together. Each of you does their own investigation and tracks new tech in their area of interest. Then you compare notes and get suggestions from each other. And this is where you may realize that your team is collecting more data than you can effectively sort through. That’s why we built Collaborative Discovery, so you can worry less about information management and processes, and instead get back to actually using the insights that you discover.

How does collaborative tech discovery work?

For example, let’s say that your team wants to do tech discovery for machine learning applications in electronics, manufacturing, medical devices, robotics, and energy. In this scenario, the electronics expert on your team could take care of the “machine learning for electronics tech discovery” part; the manufacturing expert could do the “ML for manufacturing” part; and so on.

Collaborative discovery is particularly useful for complex, multi-layered tech fields. Rather than one person trying to do it all, you divide the big, complex tech field into sub-topics. Generally, but even more importantly for such complex scenarios, we recommend such a divide-and-conquer approach to tech discovery.

The challenge: How can you align findings across your team?

Let’s say your team does this for a while, you (= the team leader) plus five colleagues (= one each for electronics, manufacturing, medical devices, robotics, and energy). Everybody collects and tracks information from their respective vantage point.

How do you avoid information silos?

In other words, how do you notice that there is a new finding (a company, a paper, etc.) that’s relevant to both your manufacturing and your energy colleague? For example, a method that uses machine learning to do power management for manufacturing systems, for example? By the way, I’m not making this up. See, for example, this paper.

Here is one way you could deal with this: You could buy an information management system. Then, everybody on your team has to label their findings in that system. Applied to the example from just now, if your energy colleague discovers the paper, they’d have to tag the paper with “energy” and “manufacturing systems”, for example. Perhaps even “power management”?

At Mergeflow, we think that such information management and labeling puts too much of a burden on your team. A bit like labeling your photos. Whereas what you want is a software that just labels the photos for you, right?

So we built Collaborative Discovery.

Collaborative Discovery allows you to focus less on information management and more on taking advantage of the insights you discover.

Let me explain how it works.

A collaborative discovery example

Let’s get back to our scenario from above. Your team is tasked with finding machine learning applications across various domains. Eventually, of course, your task is not just to discover and track findings. What really counts is that you help develop new products faster and better, for example.

By the way, I selected machine learning applications as an example here because this has seen tremendous momentum over the past few years, and because it affects many other technologies, products, and businesses.

Let’s get started.

Your collaborative tech discovery team

As mentioned above, your team is you plus experts from each of the application fields:

  • Bobby (= you) — Machine Learning and AI (neural networks, deep learning, machine vision, etc.). You are the project leader.
  • Chuck — Energy. This includes energy generation (by wind, solar, gas, etc.), storage, and distribution (e.g. smart grids).
  • Wendy — Electronics. Currently, Wendy focuses on bioelectronics, intelligent networks, and wearables.
  • Kate — Manufacturing. Kate is interested in 3D printing, digital twins, and maintenance.
  • Bryan — Medical Devices. Bryan cares about topics such as CRISPR, lab automation, medical imaging, microbiome, and tissue engineering.
  • Nina — Robotics. Nina currently looks at collaborative robots, swarm robotics, and unmanned vehicles in general.

Your team members might involve additional experts as well, given the enormous sizes of their technology fields. But let’s keep things simple here and assume that our team only consists of the six people mentioned above.

You need a variety of data and sources

We said that your task is to discover and monitor “relevant developments” and their potential impact across applications that are relevant your organization.

Because relevant developments can come from any direction, you need a 360° view across a variety of data and sources.

Information about companies

For example, there could be other companies or organizations whose products or solutions may either boost or threaten your own products or solutions.

Science publications

There could be outside research that may inspire your own R&D, or spark collaborations with researchers outside your organization.

Blogs and news

Many blogs provide interesting “food for thought” and future scenarios for new products and solutions, for instance.

Other data sets

Not to mention other sources such as patents, market information, funded research, and clinical trials. Depending on the topic and your goals, each of these sources may hold valuable information about relevant outside activity.

Pulling together and analyzing such data and sources is at the core of what we do here at Mergeflow. You can see the data sets available in Mergeflow by clicking here.

The traditional approach to collaborative tech discovery has limits

Here is the traditional way of collaborative tech discovery, the one where you use an information management system and manual data labeling:

(1) Collect and label information: You collect and monitor machine learning information generally; Chuck does the same for machine learning in energy technologies; Wendy for electronics; and so on. You should be as non-redundant as possible across your team, so that you don’t end up collecting and monitoring the same information multiple times. And don’t forget to manually label your findings.

(2) Align your findings: Your team meets once a week, to compare notes and align findings. For example, you may have discovered a new machine learning company that is relevant to Chuck’s “smart grid” interests. Or Kate may have spotted a paper that uses machine learning to improve 3D printing processes, and this could also be relevant to Bryan’s “tissue engineering” interests.

(3) Go to (1), perhaps refine your topics, based on (2).

Again, make sure to avoid redundancy in your collection and monitoring process as much as possible.

Now, here is why this process will most likely not work:

Information overload

For each of your technology fields, you collect quite a number of new findings each week (papers, companies, news, patents, etc.). In order to quantify how much, we checked how many company news, R&D, blogs, patents etc. each of your team members would likely have to wade through during an average week:

Number of documents per week, per team member. Each week you would have to manually go through and label all these documents, if you did collaborative tech discovery manually.
<strong>Number of documents per week per team member<strong> Each week you would have to manually go through and label all these documents if you did collaborative tech discovery manually

Of course, these numbers depend on how broadly or narrowly one scopes the topics. But from our experience, these numbers are fairly typical.

In total, you and your team would have to go through and label more than 5,500 documents. Every week. This is clearly not sustainable.

But you won’t just have a volume problem. You will also end up in information management process hell. We talk about this in another article as well, “5 ways to escape from innovation theater”.

Information management process hell

So you use an information management system, and collect and label your findings there. Perhaps you even build and use an ontology. An ontology is a formal system for representing concepts, categories, entities, and other data, and their relations among each other. For example, an ontology for our scenario here could state that “3d printing is a manufacturing technology”, or that “robotics is a technology for manufacturing and surgery”.

But the problem is that you are dealing with a moving target. As you go along, your knowledge and your goals will evolve. This means that you will have to keep modifying your process and your information structures. Including your ontology.

All this restructuring, reorganizing etc. will eat up your team’s time. Not to mention the actual collection and labeling of your findings will get slower as well because your system changes all the time.

With manual collaborative tech discovery, you will end up spending your time on defining processes and managing information, not on using information. We call this ‘information management process hell‘.

By the way, information management process hell also contributes to analysis paralysis. So, time to get out of information management process hell! So, next, I will describe how you could do collaborative tech discovery with Mergeflow.

How Collaborative Discovery helps you automate the boring stuff in collaborative tech discovery

We built Collaborative Discovery to help you automate as much of the boring stuff as possible when you do collaborative tech discovery. Here is how it works:

(1) Follow your topics in Mergeflow: Each of your team members follows their topics in Mergeflow. Wendy follows the “machine learning in electronics” topics; Chuck those in “energy”; and so on. When you follow a topic in Mergeflow, you receive machine-generated, weekly email update reports. We call these reports Weekly360. Your Weekly360s tell you what happened over the past week in venture investments, R&D, news, etc.. Weekly360s also align your findings with your team’s topics, based on the contents of your findings. Below I will describe two examples.

(2) Decide what to do next: Once a week, get together, go over the most relevant findings, and decide what do do, based on these findings. “Most relevant findings” are those that are relevant to most topics across your team. For example, a machine learning paper that is relevant to both 3D printing and tissue engineering is probably more relevant to your team overall than a paper that is just relevant to one of your team’s topics.

(3) Go to (1), perhaps refine your topics, based on (2).

Let’s make this concrete, and look at some actual data.

Mergeflow’s Weekly360 email update reports enable continuous tech discovery for your team

With Mergeflow’s Weekly360 email update reports, your team can do collaborative tech discovery on a continuous basis. Each week, you and each of your team members will receive four emails:

Venture Capital Fundings

Venture funding rounds relevant to your topics.

Market News

New market estimates (market segments, size and growth estimates) within and adjacent to the scope of your topics.


New scientific publications from journals, conferences, and preprint databases.

News & Blogs

General news and blog posts from tech journalists around the world.

Here, for example, is a screenshot of an actual R&D Weekly360, just like you would receive it (this one is from 10 January 2020):

 A Mergeflow R&D Weekly360. Mergeflow Teams labels the findings with your team's interests. This enables continuous tech discovery for your team.
<strong>A Mergeflow <em>RD Weekly360<em><strong> Collaborative Discovery labels the findings with your teams interests This enables continuous tech discovery for your team

In the screenshot above you can see that for each finding, the Weekly360 indicates relevant topics and team members. For example, the first paper…

Advanced Methods for Photovoltaic Output Power Forecasting

…talks about how various machine learning methods may be used to make better forecasts of photovoltaic output power. Based on this content, Mergeflow assigns the paper to your “Machine Learning” topic, as well as to Chuck’s “Solar Energy” topic.

The second paper…

Improved 1D-CNNs for behavior recognition using wearable sensor network

…discusses using a machine learning method (convolutional neural networks) to better recognize various types of human activity from wearable acceleration sensor data. This is why Mergeflow assigns this paper to your “Machine Learning” and Wendy’s “Wearables” topic.

Other scenarios for collaborative tech discovery

In this article, I use a scenario where a fictitious team explores machine learning applications across a number of technology fields. But in our blog, we have other articles, on other topics, where the same collaborative tech discovery approach would work. These other articles use another Mergeflow feature, Grid Search, instead. The articles include:

If you find any of these topics interesting, look at the articles, and think about how you could set up Co to explore these topics. Basically, instead of spreading the topics across the rows and columns of the Grid Search matrix, as we do in these articles, you’d spread the topics across your team.

Here is the difference between Grid Search and Collaborative Discovery: In Grid Search, you get as a result one big matrix that shows you how your findings relate to your topics. By contrast, in Collaborative Discovery, you get Weekly360 email reports, and the findings are labeled with those of your team’s topics that apply.

‘Managing information’ vs. ‘using information’, revisited

Let’s recap, and compare our two approaches, ‘managing information’ vs. ‘using information’:

Managing information Using information
Each team member manually collects and monitors information relevant to them.Follow topics in Mergeflow in order to get updates from across R&D and business.
Use an information management system and an ontology to label your findings. Mergeflow automatically labels the findings with your team’s interests, based on the contents of your findings.
On a weekly basis, your team manually aligns the findings from the past week with your fields of interest. On a weekly basis, your team discusses what to do with the findings.

Collaborative tech discovery, revisited

Helping you automate the boring stuff is the central idea behind collaborative tech discovery in Mergeflow.

The software, not you, collects and labels the findings across your team

The combinatorics of manually collecting and labeling your findings, every week, are brutal. Just consider the large number of new findings every week that I showed you above. Also, ‘collecting and labeling’ is really quite boring. Plus, this is not where you and your team can really shine, and put your hard-won and valuable expertise to good use. So let the software do this, and spend your time on high-value activities instead, such as deciding what you should do with your findings.

Use your team’s distributed expertise, without the pains of coordination

In your team, you know best what is interesting in Machine Learning, and how to search for interesting findings. Chuck has the sense of judgment and knows the terminology you need to graze the Energy space; Wendy is your Electronics champion; and so on. It is much more effective if each team member can deploy their expertise on their own schedule, rather than in a centralized approach. For example, if one of Wendy’s findings makes her explore a new avenue, she can do so whenever it fits into her schedule. The next round of Weekly360s from Mergeflow will then automatically consider Wendy’s changes. There is no need for Wendy to explicitly coordinate her efforts with those of her team. This leaves her and the team time and energy to do more important things.

Transparent relevance criteria, rather than some mysterious black box metrics

For example, when Mergeflow flags a finding as relevant to three of your team’s topics, this is because the contents of the finding match all three queries that your team uses to monitor these topics. This means that relevance in Collaborative Discovery is transparent. At all times, you are in control of what is relevant or not.