Baseball great Yogi Berra is fondly remembered for famously miscalculating that, “Baseball is 90 percent mental. The other half is physical.” Unfortunately, making similar miscalculations on data in the business world is neither funny nor fondly remembered – such errors can damage reputations and create conflicts with customers, investors and regulators. As we increase our reliance on data for automated decision making, the impacts of these errors will grow exponentially and have real impacts on customers and the bottom line.
Better data and analytics ranked among the top CFO priorities in Protiviti’s 2018 Finance Trends Survey Report. New technologies like machine learning, predictive analytics and robotic process automation (RPA) have made the need for a reliable data platform even more keen and have stoked a growing fear of missing out on transformative innovations in finance.
So why, after all this time, do 89 percent of C-level executives in the U.S. still say inaccurate data is undermining their ability to serve their customers? A lot has to do with the increasing volume and variety of data, compounded by errors associated with manual entry and manipulation of data and increasing pressure to produce more reports faster.
Too often in the past, enterprise data initiatives have been solution driven, and failed to deliver desired results due to data access or data quality problems. It has only been recently, as companies have tried to move beyond historical reporting, or “How much money we made,” to “How can we make more money?” that there has been a better understanding of what good data governance means and why it is necessary.
Good data governance assumes higher data maturity. It allows finance executives to transition from asking “How can we access the data we need?” to “How can we use this data to make our predictions more accurate?”
This tipping point occurs when an organization establishes a stable and trusted data platform with clean, organized and accessible data. Once a company has developed a trusted data platform, it can begin to analyze that information and make predictive assumptions that can be relied on in a meaningful way. A reliable data platform is the launch pad from which executives can make effective use of enhanced data analytics, machine learning, RPA and other tools.
One result we were happy to see in the survey is that CFOs ranked data accuracy near the top of their priorities but data visualization closer the bottom. Though this may look like a surprise to some, to us it isn’t. Finance departments tend to be obsessive about getting the numbers right, for obvious reasons. This differs from, say, marketing, which is more interested in broader trends and is eager to deploy visualization tools to highlight these trends. Making data pretty is not a high priority for finance at this time – but access to accurate, reliable, well-governed data is. In this sense, we feel that our responders got their priorities straight.
Finally, as data analysis matures – from using manually keyed data, standard reporting and the occasional drill down to using automated data gathering, machine learning-enabled forecasting and predictive analytics – relying on data as a lever for improving market share and driving revenue becomes a real prospect and a performance expectation.
This discussion only scratches the surface of the survey’s findings. In separate discussions, we will cover data security and privacy, the state of RPA in finance, and other trends. For a more in-depth look at all the findings, download the survey report from our website.