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Why Finance Should Set Data Science Free

June 1, 2016 Mitchell Buchanan  
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Analytics may be key to success, but CFOs and their teams need to work collaboratively to leverage it.

If you have kids, ask them what they want to be when they grow up. Odds are few of them say something like “data scientist.” And even though that may be a relatively new job title, the same holds true for those working in finance departments today. 

Despite the rise of unstructured information within organizations and the need to offer better insight into what it all means, many CFOs and their teams are still struggling to keep up. A recent survey from recruiting firm Robert Half and the Institute of Management Accountants proves this with some statistics on how important they few analytics skills, and to what extent they actually feel their finance department possesses them.

According to the research, for example, there is an 18% gap between the 87% of finance leaders who say “analytics” is important to their success vs. the 59% who feel their staff knows how to perform the kind of analysis expected from them. You might expect that, since many of the tools to assist with this are still emerging, but the gap is even worse for more traditional functions. Budgeting, planning and forecasting were cited as critical by 85% but only 63% feel their group can handle it, a 22% gap. Operational analysis? A 28% gap.

Break Down the Silos

Some of this may improve over time, of course, but progress may depend in part on making sure analytics isn’t put into a silo within the enterprise. In fact, Information Management published an article not long ago that made the case for decentralizing analytics beyond a core group of data scientists.

Companies with centralized analytics functions can be entangled in this type of command-control arrangement which forces business partners to queue up “analytics questions” that are better answered by members of their own team,” the article said.

“When faced with this dilemma, centralized analytic teams have a choice. They can be stewards for their internal partners and answer sets of questions that tend to deliver incremental innovation or they can focus on larger strategic initiatives that don’t always align with partner needs and can cause unnecessary friction.”

The good news is, analytics is now within closer reach than ever before, thanks to cloud-based technologies can help organizations dissect data on the fly. The next step is figuring out how to establish a more collaborative framework to make sure the right stakeholders are doing the right things — in other words, to determine where the data science ends and the strategic thinking that spans finance, operations and the CEO’s office begins.

At the end of the day, having access to the data, and to the right tools to analyze it and act on it shouldn’t be rocket data science.

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