Mergeflow Teams collects and aligns information from across venture investments, markets, R&D, and news. By automating data collection and data alignment across your team, it helps you spend your time on high-value activities, such as deciding what your company should do next. Globally leading technology organizations across various industries employ Mergeflow Teams to use information, rather than managing it.

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Mergeflow Teams
Enabling collaborative discovery
for you and your team.

2. © Mergeflow AG 2020
Mergeflow Teams collects and aligns information from across venture investments,
markets, R&D, and news.
It helps you spend your time on high-value activities, such as deciding what your
company should do next, based on your team’s findings.
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Your task
Imagine you work in a technology company with activities in Electronics,
Manufacturing, Medical Devices, Robotics, and Energy. You can probably think
of many companies of varying sizes that fit this description.
Let’s say that your task is to discover, understand, and monitor relevant
developments in the field of Machine Learning, and how these developments
may affect your organization. For example, say you discover a new machine
learning company, or an interesting research paper. Now your task is to decide
what this company or research may mean for your business units. The ML
research you found may enable new products in Electronics, for example, or the
ML company you discovered may threaten one of your Medical Devices
solutions.
Eventually, your task is not just to collect findings, of course, but to help
decide what should be done next, based on your findings.
We selected Machine Learning as an example here because it has seen
tremendous momentum recently, and because it affects many other
technologies, products, and businesses.
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Your team
Your company management is aware of the importance of your task. So they let
you put together a team of the best experts from each business unit in your
company. These experts are experienced in business, they know what makes
your company tick, and they all have a very strong technical background in their
respective fields.
Here is your team, and their areas of expertise:
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Bobby (= you)
Machine Learning
Neural networks, deep
learning, machine
vision, and other
machine learning topics.
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.

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You need a variety of data sets and sources.
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Your task is to discover and monitor "relevant
developments" and their potential impact across your
organization. Because such relevant developments can
come from all kinds of different angles, you need access
to a variety of different data sets and sources.

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Mergeflow collects and
analyzes the data you
need.
Other companies or organizations may have
products or solutions that could either boost or
threaten your own products or solutions.
There could be outside research that may inspire
your own R&D, or spark collaborations with
researchers outside your organization.
Many blogs provide interesting “food for thought”
for new products and solutions, for instance.
Company information Science publications Blogs and news
Depending on your topics and your goals, other
sources such as patents, market information,
funded research, and clinical trials may be
valuable as well.
Read more about Mergeflow data sets here:
https://www.mergeflow.com/dist/files/Mergeflow-Data-Sets.pdf

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You could manage information...
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The traditional
approach.
You collect and monitor machine
learning information; Chuck does
the same for 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.
Collect information
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.
Align your findings
Based on your findings, you might
refine your topics and your
information management process.
Make sure that if you change your
information management process,
all your changes are “backward
compatible” so that you do not
destroy any of your previous
findings.
Refine and repeat
To support your efforts, you could use some kind of
information management system where you all
curate your findings in some structured way.
Just make sure that you have a good process and
structure when you do this.
Perhaps you even consider building and using an
ontology.

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The traditional information
management approach
does not work.
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:
Information overload

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The traditional information
management approach
does not work.
You could use an information management system, perhaps also an ontology,
for curating and structuring your findings. 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.
All this restructuring, reorganizing etc. will eat up your team’s time, not to
mention the actual collection and curation of your findings.
Your task is not to build an information management system or an ontology.
Your task is to help decide, based on your team’s findings, what your
company should do next.
Information management process hell

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...or you could use information.
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A new way of
collaborative
discovery.
You use Mergeflow Teams. For all
your topics, Mergeflow Teams lets
you and your team subscribe to
machine-generated weekly email
update reports (Weekly360).
Your Weekly360s tell you what
happened over the past week in
venture investments, R&D, news,
etc.. They also align your findings
with your team's topics, based on
the contents of your findings.
Automated data collection
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 (e.g. a
machine learning paper that is
relevant to both 3D printing and
tissue engineering).
Decide what to do next
Based on your findings, you might
refine your topics.
Mergeflow Teams automatically
takes your changes into account
with the next round of Weekly360s.
Refine and repeat
Rather than thinking about how to collect, structure,
and align information, you can now spend your time
on high-value activities, such as deciding what to do
next in your company’s product development or
R&D.

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Let’s look at an example.
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Real-life data from our “machine learning applications” team project.

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Mergeflow Teams gives you a 360° view across various sources.
Venture Capital Fundings
This enables you to discover companies that are
relevant to your team’s topics but that you didn’t
even know might exist.
Market News
New market estimates (market segments, size and
growth estimates) within and adjacent to your
team’s topics. This helps you discover new
markets for your products and solutions.
Scientific Publications
New scientific publications from journals,
conferences, and preprint databases. New R&D
findings can stimulate your team’s thought
process, and thus lead to new approaches and
collaborations.
News & Blogs
General news and blog posts from tech journalists
around the world. Often, such posts can help you
bridge the gap between R&D and general interest
topics.

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An ‘R&D’ Weekly360 from our “machine learning applications” project.
Weekly360s are delivered via email. This ensures
that you can use a communication channel that you
already have, rather than worrying about yet another
app.
This paper discusses 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.
This paper explores 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.

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A ‘Venture Capital Investments’ Weekly360 from our example project.
Weekly360s focus on findings that are at the
intersection of your team’s interests. This makes it
easier for you to quickly get relevant information
(e.g. a new company) to people in your organization
that can act on this information (e.g. product,
solution, or business unit managers).
CLEW (https://clewmed.com/) is a healthcare
analytics company that uses machine learning to
predict the best course of action for patient care,
e.g. in intensive care medicine.
Nanox (https://www.nanox.vision/) provides a
machine-learning-powered, integrated solution for
medical imaging (X-ray). Nanox’s solution helps
drastically reduce per-scan costs.

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Managing Information vs. Using Information
Each team member manually collects and monitors
information relevant to them.
Subscribe to Mergeflow Weekly360s in order to get
automated updates from across R&D and business.
Use an information management system and an
ontology to curate your findings.
Mergeflow automatically aligns 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
next, based on the most relevant findings. "Most
relevant" could be "aligns with most team topics", for
example.

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‘Using information’ automates the boring stuff.
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The combinatorics of manually collecting and
aligning your findings, every week, are brutal. Just
consider the large number of new findings every
week that I showed you above. Also, 'collecting
and aligning' 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 your company should do next,
based on your findings.
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.
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 Mergeflow
Teams is transparent, and you are in control of
what is or is not relevant. Always.
The software, not you, collects
and aligns the findings across
your team.
Use your team's distributed
expertise, without the pains of
coordination.
Transparent relevance criteria,
rather than some mysterious
black box metrics.

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A simple process for deciding what to do
next.
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The information handling process we use at Mergeflow.
scan
relevant to project,
product, or
solution?
YES assign to person
NO
generally interesting?
YES NO
“everything folder”
This simple process requires no tags, no folder
structures, no discussion forums, no complex
workflows.
Does this mean that you might sometimes miss or
misjudge something? Yes. But if you keep your
team and yourself busy with tags, folders,
workflows, etc., this will happen even more often.

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Let’s have lunch!
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Location
Mergeflow AG
Effnerstr. 39a
81925 Muenchen
Germany
Contact
team@mergeflow.com
WWW
www.mergeflow.com
scope.mergeflow.com
Get in touch to discuss how Mergeflow Teams can
enable collaborative discovery for you.