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KDnuggets Home » News » 2014 » May » Opinions, Interviews » Interview: Richard Wendell, VP, Data Science, TE Connectivity on the Role of Analytics in Organizations ( 14:n13 )

Interview: Richard Wendell, VP, Data Science, TE Connectivity on the Role of Analytics in Organizations

We discuss organizational structure of data science team, making Analytics an integral component of all projects, future of Big Data and crucial soft-skills for aspiring practitioners.

Richard WendellRichard Wendell is the Vice President, Data Science and Strategic Analytics for TE Connectivity (TEL), the $13.5B global electronics manufacturer. In this role, Mr. Wendell leads the global team responsible for data science and analytics across the company. Mr. Wendell was brought into the company to construct the data science team from scratch and to pioneer the company’s move into advanced analytics.

Prior to joining TE Connectivity, Mr. Wendell was Vice President, Global Strategy & Business Development at American Express, where he led the company’s move into analytics-driven business models. Before American Express, Mr. Wendell's experience include co-founding several technology start-up companies, driving the successful turnaround of an IT services firm, and management consulting to Fortune 100 telecommunications clients. Mr. Wendell holds an MBA in Decision Technologies from the Tepper School at Carnegie Mellon and an undergraduate degree in Philosophy and Mathematics, Summa Cum Laude, from Brandeis University. Mr. Wendell is a regular speaker at events on big data and innovation.

First part of interview: Richard on Strategy for Analytics Projects

Here is second part of my interview with him:

Anmol Rajpurohit: Q4. How does the Data Science and Strategic Analytics unit fit within the overall organizational structure of TE Connectivity? While you were setting up the team (and thus, your company's analytic capabilities) what were your strategic priorities?

Richard Wendell: We decided to build my team as part of our Corporate Strategy, Business Development and Analytics team, reporting directly to our CEO. This gives us the tremendous advantage of seeing which challenges are the largest ones facing the company. TE Logo In addition, this approach helps with the critical executive engagement required in the early days of building up a new data science capability. With this approach, I do need to work hard to ensure my team stays highly connected with our lines of business. Our internal customers are ultimately the business units and TE’s end customers, so it is critical that we don’ fall into the trap of becoming stuck inside of corporate. I can’t disclose our strategic priorities. However, our CEO, Tom, has publically mentioned many times that one of TE’s key assets is our deep customer relationships. My team spends a good deal of time visiting with end customers.

AR: Q5. Should a firm focus on strategizing and executing the "Analytics projects" really well, or rather on making Analytics an integral component of all projects across the firm? Or both? In other words, should Analytics capability in a firm be designed as a Center of Excellence or rather as a decentralized, distributed capability?

RW: I think the answer to this question depends on where an organization is in its journey with data science and analytics. For organizations at an early stage in this journey, which is where most companies are, establishing a center of excellence can be a good move. Centralized vs Decentralized
A COE can help give the rest of the organization a “taste” of the power of analytics. Once an organization gets a taste, they will want more and more.
Pretty soon thereafter, the demand for analytics starts exceeding the COE’s ability to supply it. Around that time, the COE needs to begin pushing analytics out into the organization. A strong COE can be instrumental in this push by offering training and rotation programs. Ultimately, if you look at the Googles or Harrah’s, who are analytics masters, they have achieved a distributed analytics capability. For those of us not as far along as Google, we need to see distributed analytics as a destination. I like the way Davenport describes this journey in “Competing on Analytics: The New Science of Winning.

AR: Q6. What do you personally think about the future of Big Data? Your predictions?

RW: I agree with those who say Big Data is more about Data than it is about Big. I really like the Hadoop ecosystem of technologies, and the power of open-source distributed processing. Future of Big Data This technology means that we no longer have to bet on whether Moore’s law continues to keeps pace with the rate of data generation. We can elastically scale our ability to analyze larger sets of data. In addition, the cost of storing data has decreased dramatically. The cost for storing 1 GB of data in 1980 was $300,000. Now the cost is 5 cents. We have crossed a crucial inflection point in history:
it is now more expensive for a business to spend the time deciding whether to keep data than it is to just keep the data. As more organizations realize this, there will be more and more data to analyze.

The net net of having distributed computation and more data is that there will be more opportunity to deliver data-driven insights. I like to believe that better insights will drive better decisions, which, in turn, translate into economic efficiencies. Organizations that aren’t strategically accelerating towards this future could find themselves in a tough spot. But, to succeed, we also need to be focusing on the softer “EQ” skills I mentioned above.

AR: Q7. What soft skills do you think are the most important for practitioners in the field of Data Science?

RW: I think that practitioners need to focus on their all-around Analytics EQ.
Two Emotional Quotientkey soft skills are being able to actively listen to your audience and to help them frame their issues in a way that can solved with Advanced Analytics.
The critical element to framing is knowing which analytics techniques are best at solving which sort of business problems. Combining active listening with analytic framing enables a practitioner to have a conversation with a non-technical person that is both engaging and arrives at the right approach.

Jiro Dreams of SushiAR: Q8. What book (or article) did you recently read and liked?

RW: May I mention a movie? Jiro Dreams of Sushi. I was entranced by the way Jiro approaches making sushi with such passion, always striving to be better. I found it really inspiring. What if those of us in Data Science and Advanced Analytics approached our profession with even a fraction of this dedication? I imagine a Shokunin of Data Science being able to walk into any company, make sense of all of the data and transform the entire organization.


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