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When data analytics comes up, everyone seems to focus on the quarterback of the function, the data scientist. Often they overlook the other players that can add value, and that are not as expensive.

Quarterback Matt Ryan of the Atlanta Falcons will get paid $30 million1 for the 2018 season. If you go down the list of the highest paid players in the NFL you will see a lot of quarterbacks. Missing from this list is one of the most consistent performers who I believe has single-handedly carried his team through many victories; Baltimore Ravens kicker2, Justin Tucker. Kickers are some of the lowest paid players in the NFL, but when the score is tied, they become each team’s personal messiah.

What does this have to do with data analytics?

There is much written about data analytics and the ability of data to transform finance and accounting. I do believe in the power of data to inform better decision-making and strategy. But when data analytics comes up, everyone seems to focus on the quarterback of the function, the data scientist. Often they overlook the other players that can add value, and that are not as expensive.

For a little context, data scientists3 are ranked as one of the best jobs in America by Glassdoor. These valuable, but often elusive employees, are at the center of high-performing data analytics functions. They can help move organizations from the realm of the descriptive to the predictive in analytics. They must have technical acumen (building models and programming software) as well as a strong grasp of the business.

But they are hard to find4 (companies spend an average of 51 days trying to fill this position according to IBM), expensive (median base salary is $110,000 according to Glassdoor), and prone to job hopping (according to Kaggle they spend 1-2 hours looking for a new job).5 

They are so hard to find that they are impeding companies’ implementation of analytics. A recent Deloitte Dbriefs webcast polled 3,000 finance professionals and found “only 13.3% are pursuing the full complement of descriptive, prescriptive, and predictive analytics.” What they say is holding them back the most; people.

Tom Davenport6 of Deloitte elaborated on the kinds of people and skills needed for a comprehensive analytics function:

  • Enterprise data scientists
  • Data management professionals
  • Domain/sector practitioners
  • Data storytellers
  • Technology application experts

But he also offered some great advice to those struggling with finding the right people before starting a function of this kind:

“You don’t need to immediately secure all of these capabilities to take analytics to the next level, and not all the skills need to reside within finance. Doing an audit of your accounting and finance teams’ skill set may reveal employees with statistical capabilities or employees who could be trained in statistics to complement another needed skill, i.e. that of data storyteller." 

What Davenport emphasizes is that data science sits under a big tent, with plenty of room for those who understand the business best (i.e., domain/sector practitioners), those who are able to get results in the form of analytical solutions (i.e., technology-oriented talent), and those who are able to communicate effectively about analytics (i.e., storytellers). 

IMA® (Institute of Management Accountants) recognizes that upskilling current employees, rather than trying to hire what can be difficult to find candidates, makes good business sense. Currently IMA offers a range of continuing education on data analytics including “Leveraging Excel for Data Analytics.” Management accountants do not need to be data scientists; but they should understand the fundamentals and develop some skills of their own. 

But before a finance manager can roll-out training programs and a revamped departmental structure, he/she has to make the business case for an analytics function. IMA can help there as well. Our latest whitepaper, “Fit for Purpose in a Digital Age,” finds compelling reasons for business leaders to stop and take stock of what digitization means for the future of finance.

The costs of a half-hearted approach to analytics can be high. Marc Epstein, Ph.D., Distinguished Research Professor of Management at Jones Graduate School of Business at Rice University, shared with Strategic Finance magazine what he considers the costs of ignoring digitization:

“Surveys have shown that though companies across industries are actively discussing the importance of digital technologies for their business operations, few are aggressively pursuing projects that will integrate these as quickly as needed. Having a plan isn’t enough. Effective implementation is critical, and the time to do it is now. And as was the case with previous technology and business model changes, those that don’t move quickly and boldly will likely fail.”

So to finance managers out there waiting for the Tom Brady of data science to show up at their door, I say, look to the human capital you have, afford them plenty of training opportunities, and delight when they take your team to the next level.


  1. ”NFL’s Biggest Contracts for 2018,”, February 8, 2018
  2. ” NFL's highest-paid players at every position -- and who's up next,”, March 21, 2018
  3. “Data Scientist Is the Best Job In America According Glassdoor's 2018 Rankings,” Forbes, January 29, 2018
  4. IBM Predicts Demand For Data Scientists Will Soar 28% By 2020, Forbes, March, 13, 2017
  5. “How machine learning creates new professions — and problems,” The Financial Times, November 29, 2017
  6. “Analytics: Five Skills to Help Finance Soar,” The Wall Street Journal, January 30, 2017

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