Meet Your New Data Gurus

In the age of the cloud, don’t assume the owner of your company’s most valuable information has to work in IT.

Remember when calling your company “data-driven” was a way to differentiate yourself? It seems almost quaint today, what with two-thirds of all companies describing themselves in just those terms.

So many businesses see themselves as data-driven because there is now so much data to capture, process and analyze. It comes from every department and function: sensor data on the manufacturing floor; point of purchase data on RFID devices; Waves of marketing data stored in CRM and marketing automation systems; promotion response rates from social networks, and so on and so forth.

When companies overwhelmingly relied on premise-based applications and ERP systems, they naturally assigned ownership of all that data to IT. While this made sense from a technical perspective – there was plenty of heavy lifting required to consume and integrate structured and unstructured data – IT isn’t the only function that needs to derive meaning from data in modern businesses.

Looking Beyond IT

Now that more applications and data have moved to the cloud – giving more users direct and easy access to decision-shaping information – businesses no longer need to see data as an only-IT problem. In fact, IT complexity itself has become a problem for businesses, with legacy systems ranking at the top of the list of concerns for executives like CFOs, according to a 2014 Accenture survey.

It’s a new world, and ingenious new systems offer ever more useful ways to view and analyze data. Features like LinkedIn’s University Outcome Rankings, for instance, make it easy for college-bound high school students to identify the schools most likely to catapult them into their chosen careers. LinkedIn’s analysis engine studies millions of LinkedIn profiles to identify where alumni of thousands of universities end up. Add that to another tool – University Finder – and prospective college students can even narrow their search by geographic location. Want a high-tech marketing career in Portland? LinkedIn makes utilizing its data-driven insights as easy as searching for the MLB World Series television schedule on Google. The technology will tell you which schools offer you the best shot at that future in a matter of seconds.

Where the Data Comes Together

Businesses are anxious to gain similar simplicity to utilizing insights that can help them plan better, because many of them are waking up and realizing the benefits of empowering non-technical staff with the ability to make data-driven decision-making. Modern organizations want collaboration among departments to better identify the factors that drive demand for their products or services, the conditions that reduce employee churn, and the efficiencies that will boost customer satisfaction rates or reduce service costs.

The only place capable of bringing all this information together, and making it truly useful to the business, isn’t solely IT anymore. The new data center can revolve around finance.

This is why businesses are looking for their finance organizations to move away from the world of green eyeshades and bean counting and into the real-time age of advanced analytics. They want them to do more than interpret and report results, close the books every quarter, and establish guardrails for spending. As Jim Frankola, CFO of Cloudera, told Wall Street Journal, “…I want my analysts to have access to literally every single transaction out there. I want them to be able to find not just correlation, but causation.”

Today’s finance executives are expected to work across the organization to model the business for growth, develop potential responses to likely scenarios (good and bad), align new initiatives with monetization, and deploy resources and investments where they’ll do the most good. CFO Magazine wrote they are, “…demanding that their finance and accounting personnel have the ability to interpret trends from big data and tell a compelling story." In the age of the cloud and accessible analytics, it’s finance’s job to turn that relentless, ever-growing stream of information into something that’s useful not just for highly trained data analysts, but also to the front-line managers whose activities and decisions directly impact revenues and expenses. They now enable the hard but helpful questions to be asked and answered:

• When my so-called superior servers go down (like we know all do) and my social networking site is offline, how much money is our company losing?
• What kind of impact on sales did that viral video campaign have on the business, and how many more videos should we produce if it did help close deals?
• How can we predict the impact of severe weather on food quantity for the college football game on Saturday?
• Which investments in recruiting will be most effective in reducing employee churn in our Midwest Region?
• What factors are causing stores in Austin and Madison to sell out of our new seasonal ale, and how can we exploit those drivers in other markets?

As CFOs and their staffs take on more strategic roles, they’ll start looking more like Frankola or Anthony Noto, Twitter’s finance chief who took the helm at the end of June, who has embraced his expanded role with gusto, now even announcing strategic product news.

It Doesn’t Take a Quant from MIT

Most executives assume that only data scientists with advanced educations in analytics are capable of understanding a company’s complex set of data. So pervasive is this perception that it’s resulted in an extreme shortage of data scientists, and especially those trained to deliver analytics for humans. According to McKinsey & Co., companies can’t hire data analytics experts fast enough, with as many as 190,000 unfilled positions in the United States alone.

After years spent working with finance folks, it’s easy for me to see why. The platforms most companies use are based on architectures that were designed before social media, before mobile, even before the Internet. This software is notoriously difficult to use, which discourages adoption among business users – on average, only 11 percent of employees are engaged in financial planning and analysis (FP&A) processes – who instead find it easier to leave it to The Office of the Few. But as reliance on data has grown, the bottlenecks resulting from this model have become more evident. Requests from management take weeks to fulfill; and once reports are issued, they sometimes contain data that’s several months old.

Fortunately, it doesn’t take a quant from MIT to move an organization’s budgeting, planning and forecasting (BP&F) processes beyond Excel spreadsheets and beyond finance itself. Armed with the right technology, finance can easily turn non-technically trained personnel into data gurus who can help make finance a participation sport where front-line managers have the data and analytics needed to understand both correlation and causation (what we call “what-if” scenarios) and utilize predictive analytics and forecasting – the kinds of things that historically were stuck in The Office of the Few.

It’s possible today to anoint a new kind of data guru from within the ranks of finance – someone who, as Frankola said can understand data and, “…be able to analyze it and find relationships that you’ve never seen before because it is those relationships that create…actionable data.”

So what does the future hold for these new data gurus and the organizations they serve? Quite a lot. According to the same report from Accenture I referenced earlier, 81% of CEOs partner with their CFOs to drive transformational change across their organization instead of simply focusing on cost control.

Every day, I see companies achieve this marriage of data and vision without a quant in sight. They trust in the person who understands where data and individuals connect. They help build trust and empower their entire organization through their new data gurus.