When It Comes to Getting Top Data Value, Money Clearly Talks
This article is written by David Weldon and reprinted courtesy of Insurance Networking News
According to research from Accenture Analytics, so-called “high performing” organizations are several times more likely to invest a sizable portion of their IT budgets into analytics and business intelligence, to the tune of at least 25 percent of IT spending.
Equally significant, the number of organizations that plan to increase spending on data analytics and business intelligence efforts is dramatically larger among those high performers, according to Brian McCarthy, managing director of Accenture Analytics.
McCarthy spoke with Information Management about the state of data analytics investments today and what we can expect over the next two or three years. He confirmed what some other recent studies have claimed – that data analytics is the number one area of technology spending now, and the number one area where IT leaders want to increase their spending even more.
But the pace of spending on data analytics and business intelligence is clearly all over the map.
“We asked the question about spending going forward and [high performers] are actually going to double down on that investment over the next three years –five times the number of high performers, versus low performers, made that commitment,” McCarthy noted.
There are a few main areas where organizations plan to increase spending, McCarthy says. “One is in the rise of the data marketplace. The second is around the advances in machine learning. The third area is around visualization and consumption around visual environments. The fourth is more around the culture of the insight-driven enterprise, where we’re seeing much more of a shift towards scaling the impact of analytics across the enterprise.”
Asked what these organizations are most looking to do with their data analytics investments, or do better, McCarthy says “They’ve seen the benefit of investing in analytics already. Many of them have been down this journey now for a number of years with analytics or big data. They’re not just doing proof-point projects anymore.”
“They’re really looking to scale the benefit across the enterprise, either in a functional area like marketing or sales or in terms of extending the capability across multiple functional areas,” McCarthy says. “They’re investing in their operating models – how they’re structuring their analytics organizations; how they’re thinking of the technology investments, either leveraging the cloud or building their own hybrid architecture environments. They’re investing in talent in terms of building the teams within, hiring new, and partnering with third parties that have the skills to give them the capacity they need.”
In terms of what to expect for top data analytics trends in 2016, McCarthy says three take top level status.
“The first one is what I call diverse data versus big data. It’s integrating lots of different data sets from what’s out there – third party publically available data, social data, etc. to get a much richer view, either of the consumer or of the enterprise,” McCarthy explains. “It’s looking at more sources of data for driving analytics. For example, for insurance companies, it’s pulling in a lot of public data, social data, their own data, credit bureau data, etc. to get a much richer view of the customer for underwriting purposes for example.”
“The next one is what we call the rise of the data marketplace — which is the Amazon for data — where you have the ability to give your team internally access to all sorts of data sets that they can get at easily, easy to navigate, in user friendly environment, and where the right types of data that you need for your job are available. Companies are building these capabilities now internally now because they’re seeing the value in having all of these different data sets available and easy to navigate to be able to speed the business capability and business value.”
“The last one is the ability to manage your data in what we like to call different classes,” McCarthy says. “We use the analogy of first class, business class and coach class data – having a clear distinction between what is first class data that is required to be highly governed, highly maintained, and reported on a daily, weekly, monthly basis, whatever the case may be.”
“Business class data is data sets that are enabled for analytics, for marketing teams, for supply chain teams, etc. and having the analytics available not as highly governed,” McCarthy says. “Coach class data is everything else. There are nuggets of gold in there, but finding them is the challenge and you don’t really need a whole lot of governance on that data. You just need to have the ability to navigate it and know what’s where.”