Our team of data scientists have not reached Phase 5 of the Data Analytics LifeCycle for measuring innovation at EMC. We will likely be in Phase 4 (running analytic models) for several months. In the interest of finishing up this series, however, I’d like to share my thoughts about preparing for Phase 5.
Phase 5 is labeled as “Communicate Results”. Presumably a business user received permission (and resources) to create and execute an Analytic Plan. Before the creation of this plan there was a vague notion, an idea, or a hunch that needed to be proven. Phase 1 represented the beginning stages of scoping the problem, assessing the risks, and defining success. The business value was fuzzy and hard to quantify.
In Phase 5 the results will be communicated. The vague, fuzzy notions of earlier phases should be replaced by quantifiable conclusions. This may seem like a straightforward step (and it can be if the steps are followed diligently). The main takeaway that I learned about Phase 5 can be summarized as follows:
Hypotheses about unlocking value from corporate data were either proven or not. Did we succeed? Did we fail? In Phase 5, the business value imagined before Phase 1 is quantified and presented back to the corporation.
One way to prepare for Phase 5 is to start thinking about this question:
What are the three most significant findings in the observation of the data?
Here are three significant experiences our team has had during this project:
- Early visualizations yield enormous (and actionable) insight. In Phase 3 our global data scientists began exploring the data. Social network analysis visualizations identified Irish Butterflies and Chinese Boundary Spanners. The at-a-glance insight that these graphs revealed led to the immediate operationalization of new processes (which I will describe in Phase 6). Visualizations help immensely (as opposed to staring at the raw data).
- Measuring innovation delivery is difficult. Correlating ideas with successful (or unsuccessful) delivery could not be accomplished with our data sets. As a result we have launched a Longitudinal Study.
- “Knowledge Flight Patterns” can identify global gaps in communication.
I have direct responsibility for the output of dozens of top-notch researchers at our EMC Labs China location. In 2011 some of my Chinese co-workers came up with a graphical visualization of “Knowledge Flight Patterns”.
The capture of meeting minutes within my corporation identified boundary spanner Jidong Chen. His lecture on SIGMOD findings (June 2011) was shared with six different countries. The EMC Labs China team wrote software that placed red dots on a map to represent participating innovators (hovering over the red dots reveals the names). The animated yellow dots represent the knowledge being transferred across geographic boundaries (from Jidong to the team). In the above example, the yellow dots represents “Big Data” knowledge.
Jidong’s talk is just one entry in our database (which contains records of hundreds of such conversations). If each entry is “animated” in the same fashion, eventually a trans-global grid of knowledge transfer “flights” will be displayed. Different colors can be used for different types of knowledge. When looking at this type of grid, a few insights can emerge:
- Some global locations are the source of a particular kind or class of knowledge
- Some global locations are on the receiving end for a particular kind of knowledge, while some are left out in certain conversations
- Some global locations are more active than others when it comes to knowledge transfer participation.
- Some flight patterns regularly leave (and land) at defined intervals, while some flights are more sporadic.
Communicating this learning in Phase 5 is important. The course that taught me these steps recommends a template to use when entering Phase 5. The template is an excellent stimulus for communication, and ties back directly to the “plea for resources” made before Phase 1.
My mentor and teacher on this topic, EMC’s David Dietrich, points out that Data Scientists often don’t enjoy going to the effort of messaging their results to different audiences in different ways. To quote Dave:
“Many people who are great at the analytics do not enjoy this story telling or evangelization portion of the project. As a result, they may give it short shrift. Instead, I have come to view these opportunities as a way to (a) fine tune my message and (b) drive change. Sharing a strong message means you have a chance to reach multiple groups and influence behavior if what you are embarking on is worth driving change.”
I’m nearing the end of this journey through the six steps. My last post will describe Step 6 (Operationalize). Our team is far from arriving at Step 6, but I will share some changes that we have already introduced as a result of executing the first four steps.
image credits: steve todd
Steve 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