Debating About Data Science Literacy

By March 22, 2018 No Comments


Do we need to teach data scientists to be better communicators, or improve the data science literacy of their audience, or both?

In recent years, advanced analytics has been making a lot of Operations functions more efficient at utility companies. As a result, a portion of Operations personnel have been migrating into more data-centric jobs.

Even though such job shifts over a short period of time may involve a seemingly small percentage of employees, over longer periods of time this trend appears to be translating into a big cultural change.

Regarding leaders making a job change from Operations to Analytics areas, a March 15, 2018 Information Week article, “Reshaping Skillsets in the Age of Analytics” suggests changes within the analytics arena itself are another key driver.

The article points to a growing trend, whereby non-analytics executives assume responsibility over areas traditionally managed by data scientists. Easy-to-use tools which automate advanced analytics functions (e.g. data mining) are enabling leaders who lack data science expertise to move from Sales or Operations areas into Advanced Analytics areas. This shift is propelled by “two trends…the emergence of citizen data scientists…and [efficiency], freeing data scientists to focus on insights.”

Data Snapshot

It comes as no surprise that utilities are seeing shifts of personnel from Operations areas to data-centric areas, as improvements in Advanced Analytics capabilities drive greater operational efficiency.  But what about leadership-level changes?

In this regard, it is of interest to look beyond staff-level job changes to learn more about advanced analytics-related shifts for utility executives and managers.

Comparison of Representative IT, Finance, and Advanced Analytics Executive-level Jobs at 28 Large U.S. Electric Utilities


(Source:  Analysis of March 2018 queries from database, Peter Manos)

Job types at the 28 utilities analyzed  Oldest quartile average job longevity, years  Median Job Longevity, Months  Number of jobs analyzed
CFO and VP Finance  4.2 18  102
CIO and VP IT  3.0 13 82
CAO and VP or Director of Analytics 2.1  12 50

There are now Chief Analytics Officer or VP-level or Director-level Analytics jobs, at a significant portion of large U.S. electric utilities.

This conclusion is based on a March 2018 review executive-level job titles on the DataConnect database showed 58 analytics-related director or higher roles at a representative sample of 28 large U.S. electric utilities.  Looking at the oldest quartile of the 50 such analytics jobs, an average of 2.1 years was found, while the median time the personnel in question were at the 50 jobs was 12 months.  As a rough benchmark, similar level position in Information Technology and Finance respectively showed 3 year and 4.2 year oldest quartile longevities respectively, and medians of 13 and 18 months respectively.

I discussed the Information Week article with Raiford Smith, Vice President of Energy Technology and Analytics at Entergy. Smith said he sees “a pretty huge gap between aspiration and capability,” even though he “appreciates and agrees with the article’s point that everyone in the future will need to be well-versed with analytics tools and insights developments.”

“Analytics are the latest email/cellphone/workplace gadget we can’t live without, but like a lot of new tools, the clock is still flashing 12:00 in the hopes someone will read the instruction manual and figure out how to set the time,” Smith said.

To fill the gap between the ideal future and our current state, Smith suggests companies “bring on staff that are well-versed in demystifying analytics, developing insights, and helping the enterprise begin the massive change management exercise that empowers every employee to use data and to create new analytics that will provide additional value.”

Smith also suggests companies set aside a training budget for existing employees to help staff and leaders understand the lingo and capabilities of analytics so they can communicate with the data scientists effectively and lead the organization through this new digital, data-driven frontier.

Information Week included a great emphasis on communication skills: “Communication is an increasingly critical skill. Future data scientists will be empowered to spend their time communicating insights and driving value, unencumbered by the tedious tasks that previously consumed their time.”

If you combine the takeaways from the Information Week article with Smith’s point of view, you can see that well-roundedness cuts both ways. A utility will do well to foster data science literacy throughout the non-technical ranks of the organization so that the greater depth of ideas can be exchanged on the path to more data-driven insights.

Raiford Smith will be speaking this April 17th at Utility Analytics Summit in Irvine, California. Register today for the event!