Information Playground

Connecting University Researchers

In previous posts I explained how EMC leverages an analytic process to manage university research globally. Truth be told, the analytic process actually measures global innovation; university research is a subset of corporate innovation activities.  The context of university research is worth exploring on its own, however, and I took the opportunity to share our approach with partner faculty and students at EMC’s 2^nd Annual University Day.

Analyzing university research side-by-side with other corporate innovation activities has its advantages. In my last post, I shared specific data about a list of Chinese researchers that are actively involved in local university research.  The pie chart below highlights the set of researchers that collaborated with Chinese universities for a specific time period.

In addition to participating in local university research, the engineers at EMC Labs China are also actively involved in the yearly EMC idea contest known as the Innovation Roadmap. Historical idea submissions from global employees (8,000+ ideas and growing) are also stored in this innovation database.

Employees are encouraged to submit their ideas as “teams”. In fact, diversity of team members, whether it be geographic or by function, is highly encouraged. This diversity is a leading cause of increased idea quality, as I’ve discussed in a recent post.  As a result of storing many years’ worth of historical idea submissions, we can begin to visualize team submissions using social network analysis.  For example, the social graph below focuses on Chinese employees that continually surface as “strong collaborators” in the area of idea submission.

In the graph above it is apparent that Jidong Chen, for example, has a network of collaborators with which he submits ideas.  Chances are good that Jidong, during discussions with his peers, is sharing the work of his university counterparts either directly or indirectly.

A more important question, in my mind, is whether Jidong is sharing knowledge across boundaries. These boundaries could be geographic, they could be technology related (e.g. security researchers, compression researchers, etc), or they could be by function (collaborating with marketing, HR, finance, etc).

In order to validate that any given EMC employee is indeed crossing boundaries in the transfer of university research knowledge, the analysis was run again with color-coded values representing country of origin. The graph below is a zoomed-out picture of the same chart, with color coding of each individual by geography.

The yellow dots represent Chinese employees. The yellow circles with red boundaries correspond to the red circles in the previous chart. One can readily see that Chinese idea submitters not only cluster together, but they bridge to other geographies as well. The colors of these circles represent employees in France, Israel, Australia, and the United States.  As a result, certain Chinese employees have extremely high betweenness ranks, which means that they are strong candidates to transfer their knowledge to other countries.

Whether or not they actually DO transfer that knowledge is a different thing altogether. Is it enough to know that the potential is there?  The answer is no. However, the knowledge of betweenness allows a centralized innovation program to guide good behaviors when it comes to global knowledge transfer. The analytic results give us all the pieces to put a plan into action.

This post has highlighted the ability to explore connectedness and collaboration emanating from an EMC employee conducting local university research. Knowledge expands locally, and then it has the potential to be transferred globally.

What about the reverse of this process? Can any global researcher correlate their research efforts to a remote employee like Jidong?

In a future post I will examine analytic techniques to enable this behavior.

Steve

http://stevetodd.typepad.com

Twitter: @SteveTodd

EMC Fellow

Finding the Research Needle

In my last post I used the diagram below to highlight the locations and activity level with EMC's global university research partners:

Additionally, I included another graphic which highlighted that the large purple dot hovering over Saint Petersburg Russia can be further analyzed to discover examples of research that is occurring in that locale.

Given the capabilities to track and analyze “what” types of research EMC does, and “where” the research is being conducted, in this post I’d like to focus on “who” is doing the research.  In particular, I’d like to add two regions that also have university research programs: Europe and China.

If I wanted to plan a conference call with any and all employees that conduct research in Europe or Russia, who would I invite to the meeting?  If I wanted to pinpoint the employee(s) in China that researches Hadoop with a local university, how could I use analytics to arrive at an automated answer?

EMC's current approach allows the company to answer these questions. In my last post I described the analytic sandbox that is used to aggregate research and innovation activities across EMC. I mentioned that location information (e.g. the geography where the activity occurred) is retained. In addition, the employees involved with that activity are imported as well. This allows reports to be generated that summarize the total number of employees that are active in university research across multiple locales:

Note that the analytic repository accurately lists myself (Todd) as a participant in Russian University Research (as evidenced by a previous blog post describing one such visit).

Certainly this capability is useful, but can we dive a bit deeper?  For example, can I find the Chinese researcher that is most active in researching Big Data Analytics algorithms with local Chinese universities? 

The answer is yes, and the algorithm is as follows:

Jidong Chen is listed as the most active Chinese employee when it comes to university engagement. The database can be queried to find the specific entries containing Jidong as a participant. Each of these engagements, in turn, can be compared against the topic model described in the visualization blog post. Each activity can be mapped into one of the 25 buckets. For example, Jidong’s last five activities are mapped as follows:

This graph shows that 80% of Jidong’s activity map to “Big Data”, and one of them maps to “Cloud”.  A deeper historical analysis of all of Jidong’s activity would reinforce his research leanings.  

This approach proves that the “voice” or “research interests” of any EMC employee can be discovered using analytic techniques.

The next question to ask would be: “does this employee collaborate outside of their geography”?

I will answer this question in my next post.

Steve

http://stevetodd.typepad.com

Twitter: @SteveTodd

EMC Fellow

Where In The World Of University Research

I’ve been writing several blog posts recently that describe EMC’s use of analytics to accelerate the innovation that is coming from global university research partners world-wide.

The geographic locations of many (but not all) of our university research partners are depicted below:

In addition, in my last post I described how EMC tries to keep track of “what” research activities our global partners are working on through the use of Stanford’s Topic Modeling Toolbox:

In this post I’d like to shed a little more light on how to take these research themes and map them to specific geographic regions. In order to do this, it helps to describe the inner workings of EMC's innovation analytics framework in general.

The framework is founded upon activities that are closely tied to innovation, including:

• University Research
• Publications
• Conferences
• Customers/Partner engagements
• Knowledge Transfer/Brown Bag sessions
• Employee ideas
• Intellectual property
• Etc.

Visually these activities can be depicted as follows:

The analytic framework is essentially a data gathering process that can record these activities via a variety of methods, including (a) manually, (b) via email, (c) via crawling of a file system, or (d) as part of Outlook calendar invites. No matter what the source of ingest, all of the innovation activities (including university research), are funneled into an analytic sandbox, which stores both structured and unstructured content (the graphic below describes this approach, and was previously described in a series of posts on the data analytic lifecycle).

The beauty of this approach is that the geographic location of the structured and unstructured data is preserved during the ingest phase, and thus available for analytic queries. For example, the diagram below highlights an answer to the question: “What types of research has EMC funded recently in Russia”?

The resulting map and word cloud depicts that compression research is occurring in Saint Petersburg, Russia.  This is due in large part to the strong mathematical skills of the EMC employees and universities in that region.

I recently contributed an article that described my own personal empirical data about innovation at our global R&D locations. The approach described above is an alternative, data-driven approach to classifying a company's global innovation activities.

Who are the EMC employees conducting the research? Can the analytic framework drill down to the employee level and discover which EMC mathematicians are involved with this compression collaboration, and/or which Russian employees participate in university research in general?

The answer is yes, and more detail will be described in an upcoming post.

Steve

http://stevetodd.typepad.com

Twitter: @SteveTodd

EMC Fellow

 

 

Visualizing University Research Activities

During a lecture at EMC’s 2^nd Annual University Day on Monday, I held a dialogue with faculty and students gathered at our Santa Clara campus. I described how EMC uses an analytics framework (Pivotal/Greenplum) to accelerate the innovation that emerges from our global academic research partners. In particular, I highlighted the following capabilities of our innovation analytics platform:

• A visualization of the “types” of research currently active in our portfolio (e.g. solid state storage, analytics, etc).
• A visualization of the “types” of research by region (e.g. where in the world do we research compression technology?)
• Who are EMC’s key researchers in any given region?
• Which researchers are the best at transferring knowledge out of their region?
• For any given EMC researcher, what type(s) of research do they conduct?
• What is the complete list of EMC employees, per region, that are involved in any form of university research?
• How can global EMC employees advance their own ideas by locating relevant university research?
• How do we augment university research with other external employee connections (e.g. programmatically leverage their Twitter connections)

In this post I’d like to focus on the first bullet. EMC has dozens of university research partnerships worldwide. How do we dynamically visualize the current areas of exploration that are occurring across the globe at any given point in time? How can we determine which strategic research areas have strong coverage and which areas may have no coverage at all?

These questions are currently answered through our use of the functionality provided within the Stanford Topic Modeling Toolbox. The diagram below helps explain our use of this tool:

The Topic Modeling Toolbox analyzes the analytic repository containing university research activity.  Data Scientists within EMC (working as part of our EMC Labs China team) categorize these research activities by providing the toolbox with a number (e.g. N = 25). The toolbox runs algorithms that classify each research activity into one of a number of different buckets using the toolbox algorithm.

Once the toolkit has taken a pass at every document, the bar graph above shows the level of activity for each “class” of research initiative. I asked our Data Science team to provide a simple word cloud algorithm across each category, and it is fairly easy to see at a high level that Topic 01 has a cloud focus, while Topic 12 has a Big Data focus.

Furthermore, if the data above was analyzed in a given time frame (e.g. the first half of 2013), Topic 22 would be viewed as “most active”, while Topics 02 and 23 would qualify as “least active”.  This may or may not be cause for concern depending on the nature of the work.  In fact, given topics can be broken down into the “nature of the engagement”, which is highlighted below:

While this type of data gives EMC a great idea about “what” we are researching, it doesn’t provide any data at all about “where”.

I will cover EMC’s approach to solving this problem in a future post.

Steve

http://stevetodd.typepad.com

Twitter: @SteveTodd

EMC Fellow

 

Analyzing University Research

Today I attended EMC’s 2^nd Annual University Day in Santa Clara, California.  A large number of schools were represented from all over the United States, including:

• UC San Diego
• UC Irvine
• UC Santa Cruz
• Northeastern University
• Minnesota
• Carnegie Mellon University
• University of Wisconsin
• Case Western
• Florida International University
• University of Utah
• Harvard
• University of Rochester
• Stony Brook University
• Princeton University

The agenda for the day included discussions on challenging high-tech issues in next generation data centers, including new developments in solid state storage. EMC Distinguished Engineer Jeroen VanRotterdam led an interesting dialogue examining the current state of relationships between Industry and Academia.

Greg Ganger, CMU Professor and Director of the Parallel Data Lab, gave the Academic Keynote during the afternoon session. His keynote was followed by the annual poster session, in which nine students competed for first prize.

For this post, however, I’d like to summarize a discussion I led just before lunch, in which I asked the students the following question:

“How would you manage EMC’s global university research portfolio?”

Their answer was loud and clear: "We don't know!". I responded that the answer was a fair one; it's a hard problem to solve. I then shared our company's approach of using EMC’s own analytic products (e.g. Pivotal/Greenplum) to perform global analytics across all academic research partners.  In order to highlight the global span and scope of our research initiatives, I shared the following map:

This map is dynamically generated. While it doesn’t represent every university research partnership EMC has across the globe, it’s pretty close.  The map is the result of nearly two years of collaboration across all of the countries that register their research engagements. The larger the circle, the more activity is being reported from the region.

What types of analysis can be run against a database containing research activities? During my talk I described the current reports enabled by our analytics framework:

• A visualization of the “types” of research currently active in our portfolio (e.g. solid state storage, analytics, etc).
• A visualization of the “types” of research by region (e.g. where in the world do we research compression technology?)
• Who are EMC’s key researchers in any given region?
• Which researchers are the best at transferring knowledge out of their region?
• For any given EMC researcher, what type(s) of research do they conduct?
• What is the complete list of EMC employees, per region, that are involved in any form of university research?
• How can global EMC employees advance their own ideas by locating relevant university research?
• How do we augment university research with other external employee connections (e.g. programmatically leverage their Twitter connections)

The talk was well-received. The faculty and students that attended got a good feel for the framework that EMC uses to impact our own business by expanding our knowledge with local university partners. 

In future posts I will dive in many of the items above in more detail to specifically describe how analytics are leveraged to improve EMC’s university research results.

Steve

http://stevetodd.typepad.com

Twitter: @SteveTodd

EMC Fellow

Collaboration with the Push of a Button

Last week I described the Improve Idea button that we have added to our 2013 Innovation Roadmap activity at EMC.

I'd like to describe in a bit more detail what happens when this button is pushed. The goal is to connect EMC employees with the relevant people and resources that can improve their idea before they submit it.

In a previous post I described an analytic experiment that allowed us to visualize the evolution of employee idea submission themes over a period of three years. The experiment essentially categorized employee ideas into 25 buckets using the Stanford Topic Modeling toolkit. The results, gathered in 2012, are shown below.

Each topic typically has a theme that is based on employee interest, such as "Big Data", "Cloud", or "Trust/Security".

This year, when an employee comes up with a new idea, they enter the idea into the Innovation Roadmap portal. They then have the option to push the "Improve Button". The pushing of this button causes the idea to be analyzed by the Stanford topic modeling toolkit in the context of all previous employee idea submissions. This categorizes the idea into one of the buckets.

Once the bucket has been identified, a further analysis can be run against the idea to determine "which previous idea is most closely related to yours". For example, if the employee submits an idea using terms like OpenStack technology, their idea will likely map to the "Cloud" bucket. Within this bucket a mathematical approximation will identify other documents that either (a) mention OpenStack or (b) espouse approaches that are quite similar to the inventor's idea.  The more thorough the idea submission (e.g. the longer the idea), the better chance that the idea can be correlated against something similar (we have already observed that longer ideas tend to be higher quality).

For example, Topic 09 maps to Cloud Computing, and the following term evolution in that category can be seen below:

As a result of pushing the improve button, the inventor receives a list of the 10 most relevant ideas that the toolkit identifies. They can browse these ideas, view the list of inventors, and connect/collaborate as part of the idea improvement process.

What's Next?

The Improve Button has been implemented as part of an Innovation Analytics framework that goes beyond idea submission. Employees around the world can also submit other innovation activities that they are involved with (university research, conference attendance, publications, visits to start-up incubators, etc). The analytic sandbox (structured and unstructured data), and the approach of gathering innovation activities is depicted below.

Collecting this large amount of data allows our internal data scientists to create additional buckets and categories for university research, for example. The Improve Idea Button can then be augmented to analyze their idea and also refer the inventor to "most similar university research" happening within the company, reveal who is the EMC employee involved with the research, and name the professor that studies the topic.

The same approach can be used for technical conferences, visits to startups, and any innovation activity that you can think of.

The real fun starts when all of this data is analyzed together and one can begin to see which employees tend to innovate the most for a particular topic. Often times these innovators are identified as global boundary spanners that communicate their knowledge frequently. The Improve Idea button can, in theory, conclusively point to the person that is most likely to be an expert in regards to the idea being submitted.

Making helpful idea improvement suggestions relies on strong contributions to the innovation gathering framework. Variety and volume are two key parts of analytic correctness. 

The more data gathered, the better suggestions can be made to improve the idea.

Steve

http://stevetodd.typepad.com

Twitter: @SteveTodd

EMC Fellow

 

Related articles

Phase 4 Innovation Analytics: Finding Boundary Spanners

Innovation Roadmap 2013: Push the Improve Button

Phase 1 Innovation Analytics

Understanding your Innovation Culture - A Case-Study from Swisslog

Phase 1 Innovation Analytics: Hypothesis Generation

Innovation Roadmap 2013: Push the Improve Button

 Earlier this week the Chief Technology Officer of EMC, John Roese, launched his own version of what used to be called the Innovation Showcase:  The 2013 Innovation Roadmap.

The Innovation Showcase was launched in 2007, and for six consecutive years the ideation growth across EMC's global employee base has been consistent.  In 2007 employees submitted just over 400 ideas; in 2012 they submitted a grand total of 2200 ideas. Each idea was targeted at a specific challenge that EMC is facing as a company. In many cases these challenges come directly from EMC Executives that wish to extend beyond their organization for a broader set of suggestions to the problems that they face. Every year this process has been celebrated as an "idea showcase" at EMC's annual (and global) Innovation Conference.

This year John has renamed the program to build upon the ideation success of the previous six years and focus on the continual delivery of employee ideas as a scheduled, predictable roadmap. As part of this effort the Office of the CTO has asked all business units to also financially sponsor the Innovation Roadmap at a Bronze, Silver, Gold, or Platinum level. This fund can be leveraged during the incubation stage.

Big Data for Innovation Insight

One of the benefits of being a Big Data company is the leverage of our own analytic technologies.  For several years I have been describing our use of Greenplum technology for the purpose of creating a global innovation analytics sandbox. Several graduates of EMC's Data Scientist course (including myself) have taken our learnings and applied analytics on this global innovation data.

The visualizations have influenced our corporate innovation decisions, and perhaps the best example of this is the "Improve Idea" button that our employees can press before submitting their idea to EMC's 60,000+ person employee base.

Below are some of the innovation analytics that led to the creation of the Improve Idea button:

• We have been running a longitudinal study of idea incubation progress. This study allows us to measure collaboration (or lack of collaboration) that moves (or doesn't move) ideas forward.
• We have measured the evolution of idea submission trends over time, and recognized that our employees do not have an easy way to explore previous ideas and how they relate to new ideas.
• We've created global social network graphs that highlight idea collaboration and idea quality. We've also identified boundary spanners (ideators whose influence spans countries).
• We identified clusters of innovators and understood the culture that made them successful.
• We analyzed word count on idea submission and informed our employees that more well-thought out and articulated ideas have a higher success ratio.
• We discovered that successful ideation programs in a given region quite often have a robust innovation framework in place.
• We studied clique size and the impact on idea effectiveness.

EMC's Data Scientist curriculum teaches that the last two phases of the Data Analytic Life Cycle (Communicate Results and Operationalize) should have a goal of driving behavioral change. Our analytics supplied a wealth of evidence that idea collaboration strengthens idea quality. We therefore put a plan in place to encourage collaboration by connecting inventors before they actually submit their ideas. 

The button shown below is the result of that effort. I will dive into our implementation of the Improve Button in a future post.

In the meantime, I encourage my EMC co-workers to "push it". The Innovation Central submission server is open until July 12!

Steve

http://stevetodd.typepad.com

Twitter: @SteveTodd

EMC Fellow

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Phase 1 Innovation Analytics

A Strategy for Innovation Analytics

Phase 1 Innovation Analytics: Hypothesis Generation

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Innovation: predicting or creating the future?

Innovation Lineage Threads: A Longitudinal Update

I continue to lead a Data Science project at EMC that focuses on Innovation Analytics. My approach has been to create a set of hypotheses  and leverage a task force of volunteer data scientists to help me prove or disprove them.

During this process I ran into a roadblock on one hypothesis:

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.

The roadblock occurred because my data sets did not contain sufficient data to measure “elapsed time” between events. My mentor in this initiative, David Dietrich, suggested that I begin a longitudinal study. In a previous blog post this year I described his advice as follows:

Our ability to prove hypothesis #7 was in jeopardy. This realization was not the end of the world. In data scientist terms, it was time to begin a longitudinal study (making a series of observations over a long period of time).The team began to design a method whereby TRL (Technology Readiness) levels would be gathered and recorded as a regular part of the reporting and gathering of global innovation activities. Over time, we would eventually have enough data to take a good, hard look at our hypothesis

After writing the blog post I commissioned the longitudinal study by collaborating with EMC Labs China researcher Diego Wu. Diego and I added two steps to our data gathering process:

1. We introduced innovation lineage threads whenever new, significant research initiatives started (e.g. a university research collaboration was funded).
2. We gathered innovation activities in the same way, but added these activities to a lineage thread when appropriate.

Diego implemented this framework, and two key insights emerged.

Asset Generation (or lack thereof) is Measurable

The diagram below is an actual record of an interaction that EMC had with the University of Florida. An innovation lineage thread was created (the U Florida lineage thread). As multiple innovation events were entered into the innovation analytics framework, they were also associated with the U Florida thread.

 

In this example, several assets emerged from the initial visit of a professor to EMC’s campus. These assets include the publication of a paper (good press for EMC), the hiring of a student (who was already up to speed on the EMC domain to which they were assigned), and the introduction of a new software feature into an existing product line.  Each event occurred at a discrete point in time that is a measurable distance away from any other event in the lineage thread. In this case, three assets were generated within a six month period. The time lapse becomes a data point that can be compared with other lineage threads to evaluate the “time it takes to generate an asset from an idea” (the original hypothesis).

Not all ideas experience this level of success. Often times no significant corporate asset is produced. The use of innovation lineage threads allows us to calculate “asset generation success ratios” across all ideas registered in the system. Success can be correlated against many different factors, including geography, the people associated with the idea, the business unit they are part of, amount of investment, etc.

“Engaging” Insights

While studying any given lineage thread the level of engagement becomes obvious. For example, with the University of Florida I could see clear examples of employee and faculty engagement. They visited each other. They worked on a paper together. They attended a conference together. I could “count” the number of such entries in the database and calculate such metrics as “average number of engagements per month”.

This enables me to draw “heat maps” by asking, for example, which universities are sufficiently “engaged” (or which are not).

As the Director of Global Innovation I struggle to keep track of the health of global innovation initiatives at EMC. For example:

• EMC has dozens of university research partners around the world.  How engaged are we with each one?
• EMC has committed to incubate nearly 30 winning ideas from the 2012 Innovation Showcase process. Which ones are still alive? Which ones have stalled? What kind of corporate assets result from EMC’s yearly investment in the Showcase?
• How can we fail fast? Which funded innovation initiatives are not likely to succeed and should experience “de-investment”?

The decision to begin a longitudinal study was the right one. I’m also convinced that using the Data Analytics Lifecycle (shown below, and taught in EMC’s Data Scientist Course) was the right idea. The project will most certainly progress to Step 6, causing the EMC Executive team to make operational changes to our innovation frameworks.

If you have experience with a concept similar to innovation lineage threads, I'd be interested to hear more about your approach. 

Steve

http://stevetodd.typepad.com

Twitter: @SteveTodd

EMC Fellow

 

Related articles

Phase 2 Innovation Analytics: ELT

Phase 1 Innovation Analytics: Creating the Analytics Plan

Phase 3 Innovation Analytics: Hypothesis Exploration

Phase 3 Innovation Analytics: Longitudinal Studies

Phase 1 Innovation Analytics: Hypothesis Generation

Data Science and Innovation

Earlier this year I participated in a 5-day Data Science and Big Data Analytics course. I was hoping that by the end of the week I would have a greater sense of how to grow the skill sets that would turn me into a more effective data scientist.

An interesting thing happened on day 1 of the course: I became distracted. The instructor (David Dietrich) put up a chart depicting the Data Science Lifecycle (shown below), and I was drawn so much into the diagram that I realized that the role of data scientist was not for me:

I attended the course because I knew that Data Science would become intrinsically linked to my role as Director of Global Innovation at EMC. The course has been wildly popular inside the company (1200 employees have taken the course). Hundreds of EMC partners and customers have also signed up, and the curriculum is now available at various universities. Over 100 faculty have ordered the readiness training from 20+ countries so far.

For my own data science project, I was collecting innovation data from EMC's R&D locations around the world. I had structured data. I had unstructured data. But I lacked the general knowledge of which analytical techniques would provide me the best insight into EMC's innovation ecosystem. Would it be clustering? Or would regression make the most sense?

Eventually I was taught the basics of clustering and regression algorithms, and I was also taught the circumstances in which these algorithms were most commonly used. Clearly these techniques would assist me in my quest to gain innovation insight.

However, the Lifecycle chart informed me that traditional data science projects have many moving parts and a variety of important roles; the Data Scientist alone could never provide the insight that I am looking for.

In fact, the course defined my role as a Project Sponsor, which is quite distinct from a Data Scientist. Here are the definitions for both:

• Project Sponsor: Responsible for the genesis of the project, providing the impetus for the project and core business problem, generally provides the funding and will gauge the degree of value from the final outputs of the working team.
• Data Scientist: Provide subject matter expertise for analytical techniques, data modeling, applying valid analytical techniques to given business problems and ensuring overall analytical objectives are met.

Indeed, as the months moved forward and I began implementing the six phases of the Analytic Lifecycle, I found myself building a team that not only contained data scientists, but various other roles as well:

• Data Engineers: deep technical skills to assist with tuning SQL queries for data management, extraction, and support data ingest to analytic sandbox (this included IT personnel on the East Coast  and EMC Labs researchers in China).
• Database Administrators: Provisions and configures database environment to support the analytical needs of the working team (my co-workers at the CTO Office Lab in Santa Clara).
• Business User: Someone who benefits from the end results and can consult and advise project team on value of end results and how these will be operationalized (the CTO of EMC, who runs the Innovation programs).

The fact that I was drawn into the "business end" of building a data science team for innovation is actually consistent with industry trends.  While it is estimated (in the United States) that employers will be short some 140,000-190,000 data scientists in the years to come, there will be an even bigger gap of some 1.5 million "data savvy professionals". These professionals need to know how to build teams, engage with executives, and guide the data scientists, engineers, DBAs, and business intelligence analysts.

In hindsight, I would have valued a dedicated course on the topic of managing data science projects.

Fortunately, I am in luck: within the next several months EMC Education Services will release a new course on this very topic. The course will be geared to teaching data science to business leaders. I will attend a pre-release version of the course and will be sure to share my thoughts in advance of the official release.

Steve

http://stevetodd.typepad.com

Twitter: @SteveTodd

Director, EMC Innovation Network

 

Idea Contests: An Exhaustive Look

After six years worth of idea submissions by EMC employees, the EMC Innovation Showcase has yielded a fairly robust set of data over which we can run analyses.  In recent posts I have shared analytic discoveries about clusters of innovators, geographic boundary spanners, verbosity of idea submissions, and topic trends and analysis.

During this year, EMC Distinguished Engineer and Data Scientist John Cardente has taken an exhaustive look at a variety of parameters related to the submission of employee ideas. I have looked through his results and considered how to operationalize some of the insights that I've gained.

Here are some of the parameters that are important to analyze in regards to idea contests:

• Team Size: how many people are submitting an idea together?
• Length of problem description: how many words does it take to describe the problem?
• Length of idea: how many words does it take to describe the idea?
• How many people commented on the idea?
• What was the time span (in days) during which people commented?
• How many "sponsor tags" were on the idea (sponsors at EMC create specific challenges for employees to solve)?
• How long before the deadline was the idea submitted?

All of these parameters can be analyzed and mapped against "the results" of the idea. Was it promoted as a "good idea" (i.e. selected as a finalist)? Was it ultimately selected as a winner and resources applied to advancement?

John produced the kernel density plots depicted below:

Here are some of John's own conclusions (word for word):

• Good contest ideas are characterized by detailed problem descriptions.
• Good contest ideas attract a greater number of comments.
• Large teams aren't necessary to generate winning ideas.
• Early contest submissions appear to have a disadvantage.  Perhaps this is because early submissions inspire others to submit higher quality ideas. 

How can I operationalize these findings?

Firstly, I can market these results, especially on our idea portal (for example as part of the idea submission workflow).

Secondly, I can actually run analytics against individual submissions and make recommendations on the fly, e.g.: "Good ideas, on average, contain 500 words or more. Your submission of 200 words falls well below this length. You have 30 days until this contest closes, do you wish to add additional detail to your idea"?

My team is currently closing out the 2012 Showcase and ramping up the creation of new tools and processes for 2013.  John's innovation analytics data has been (and will continue to be) tremendously helpful in this regard.

Steve

http://stevetodd.typepad.com

Twitter: @SteveTodd

Director, EMC Innovation Network