Phase 1 Innovation Analytics – Hypothesis Generation

Phase 1 Innovation Analytics: Hypothesis GenerationIn this series I have introduced the concept of a Data Analytics lifecycle and began to explain how it guides the analysis of innovation at my corporation (EMC).

As with any lifecycle, the first phase (Discovery) lays the foundation for the rest of the steps. Two of the key activities in the Discovery phase are (a) the creation of hypotheses, and (b) the creation of an analytic plan. In this post I will introduce relevant hypotheses; my next post will dive into with the analytic plan.

I found it useful to ask myself the following question:

Given a global repository representing employee ideas, discussions, minutes, and notes about innovation and research activities, what can I measure?  What measurements can accelerate corporate innovation world-wide?

I found the exercise below to be the most difficult part of the curriculum. It is clearly the most relevant, however. Business theories about data must turn into statements that can be proved or disproved.

In this phase of the analytics lifecycle, the curriculum encourages business users to generate as many relevant hypotheses as possible. All of these can help guide data scientists in further phases. Each statement below starts with a “stream of consciousness” on the business problem, and ends with a specific hypothesis that data scientists can either prove or disprove.

Hypothesis #1: Local Measurement of Innovation Activity

I believe that innovation can be measured for a given geography. This measurement can take a number of forms, including number of participants, percentage of time dedicated to innovation, local idea-to-implementation timeframes, and geographic reach of innovators (how far outside the workplace does innovation activity extend)?  As the Director of a Global Innovation Network, I’d like to know how these activities map to corporate strategy.

IH1: Innovation activity in different geographic regions can be mapped to corporate strategic directions.

Hypothesis #2: Geographic Innovators

In every locale world-wide there are typically a set of people who pursue innovation with passion and consistency.  Their contributions may be hidden from the corporate eye. They also may be focused on particular activities, such as idea contests, visits to customers/partners, frequent visitors to a university, or the generation of intellectual property.  If they have not yet taken the initiative to extend their visibility to the global stage, I believe that they can be found via analytics and connected to relevant knowledge sources.  I believe their skill in the delivery of ideas would be improved. For this topic there are two hypotheses to prove:

IH2a: The length of time it takes to deliver ideas decreases when global knowledge transfer occurs as part of the idea delivery process.

IH2b: Innovators that participate in global knowledge transfer deliver ideas more quickly than those that do not.

Hypothesis #3: Effective Ideators

When it comes to idea generation, some employees have an advanced ability to suggest ideas that resonate with the decision makers. Rarely are their ideas dismissed outright. They often (but not always) have a track record of idea delivery as well.  I believe that analytics can help me find these people. I also believe that the form of their ideas can be analyzed to reveal clues as to why their ideas are likely to be funded. In turn, the format of any idea submission can be evaluated for its value.

IH3: An idea submission can be analyzed and evaluated for the likelihood of receiving funding.

Hypothesis #4: Geographic Knowledge

Very often certain geographies have a reputation for excellence in a certain area of knowledge.  I believe that analytics will reveal that this knowledge can be found in other locales as well (or conversely it may not be found where it was assumed to be).  In general, different geographies will likely reveal themselves to be hubs of expertise in any number of areas. Knowing this fact would not only facilitate the matching of problems to local innovators in a certain region, it also may provide the opportunity to join different locales together for problem-solving exercises.

IH4: Knowledge discovery and growth for a particular topic can be measured and compared across geographic regions.

Hypothesis #5: Knowledge transfer facilitation via boundary spanners

There are certain employees that have arisen within a geography and made connections with other geographies for the purpose of collaboration. They may not have high visibility within a corporation aside from the direct connections that they have made on their own.  I believe that not only can analytics identify these people, but it can also classify the type of knowledge that these individuals are transferring. These “boundary spanners” can be targeted and trained as “innovation facilitators” and united at a corporate level.

IH5: Knowledge transfer activity can identify research-specific boundary spanners in disparate regions.

Hypothesis #6 Corporate research gaps and assignments

A corporate research roadmap needs a portfolio of initiatives to go along with it.  I believe analytics can enable a dashboard view of particular strategic initiatives (e.g. cloud computing) and determine how much research activity (if any) is occurring across the corporation. This view can also be extended to profile funding activity on particular themes. I also believe that analytics can recommend the best place to perform research as new items are added to corporate research roadmaps.

IH6: Strategic corporate themes can be mapped to geographic regions.

Hypothesis #7 Incubation Lineage and Asset Generation

I believe that the path that knowledge takes, from a local innovator, to a corporate boundary spanner, to an implementation team, to a delivered asset, can be traced and measured. I also believe that this measurement, once studied, can reveal ways to accelerate innovation and point out areas of knowledge that are yet to be converted. I’ve long been a fan of provenance, and I love the concept of “idea lineage”. The lineage can be studied to reduce asset delivery time.

IH7a: Frequent knowledge expansion and transfer events reduce the amount of time it takes to generate a corporate asset from an idea.

IH7b: Lineage maps can reveal when knowledge expansion and transfer did not (or has not) result(ed) in a corporate asset.

Hypothesis #8 New areas of innovation and research

Finally, I believe that predictive analytics can reveal areas of focus for future innovation, research, and investment. What knowledge should be expanded? Who should collaborate on that theme? What kind of assets could result?

IH8: Emerging research topics can be classified and mapped to specific ideators, innovators, boundary spanners and assets.

If I were to sum up the list above into one hypothesis, it would look something like this:

An increase in geographic knowledge transfer improves the speed of idea delivery.

In my next post I will describe how this hypothesis can be integrated into an analytic plan.

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Steve ToddSteve Todd is Director at EMC Innovation Network, and a high-tech inventor and book author “Innovate With Global Influence“. An EMC Intrapreneur with over 180 patent applications and billions in product revenue, he writes about innovation on his personal blog, the Information Playground. Twitter: @SteveTodd

Steve Todd




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