How Can the C-Suite Ensure the Use of Analytics Becomes More Widespread?

December 14, 2018
Roman Stanek's picture
Founder & CEO
Roman Stanek is a passionate entrepreneur and industry thought leader with over 20 years of high-tech experience. His latest venture, GoodData, was founded in 2007 with the mission to disrupt the business intelligence space and monetize big data. Prior to GoodData, Roman was Founder and CEO of NetBeans, the leading Java development environment (acquired by Sun Microsystems in 1999) and Systinet, a leading SOA governance platform (acquired by Mercury Interactive, later Hewlett Packard, in 2006).

As I shared in a Solutions Review article,  53 percent of companies are investing in and adopting big data analytics—a move that gives employees access to contextualized, real-time data and helps forge connections between multiple parts of the business. However, I’ve found that many companies still struggle to realize these benefits. The problem often boils down to a lack of cultural support, which starts from the top with the C-suite. For a company’s analytics effort to truly be successful, they need to do their part as well.

The C-suite needs to personally use predictive analytics to make recommendations

Embedded analytics is valuable for every employee, from front-line to C-suite, but actually encouraging the use of analytics can be a challenge. A recent McKinsey study found that employees are far more likely to actually use analytics if leadership visibly commits to using it themselves. If the C-suite begins relying on analytics for decision making and leads by example when it comes to trusting the recommendations from the analytics platform, then so will the rest of the organization—resulting in more widespread use, greater success, and higher adoption rates.

The C-suite should automate decisions by using machine learning

The C-suite will introduce machine learning because they recognize the value it has for numerous departments and processes in an organization, like claims processing or fraud monitoring. However, 79 percent of executives also believe AI that will make their own jobs easier to do and more efficient, so the C-suite should make an effort to ensure that mentality reaches the rest of the organization. To do so, they should begin using machine learning to automate time-consuming and repeatable but low-value decisions fairly early on, an effort which helps drive the use of machine learning elsewhere. To further encourage the use of machine learning, the C-suite should take a hands-on approach to prove to end users that they can trust—and act on—the recommendations they’re given.

The C-suite must take responsibility for building a data-driven culture

The C-suite is the lifeblood of a company’s culture, and the failure or success of a certain initiative can often be tied back to them. Because developing the kind of data-driven culture that supports the use of pervasive analytics often starts from the top, McKinsey suggests that the executive team “coach its members in the key tenets of advanced analytics and to undo any lingering misconceptions”. Executives can then become better equipped to perform this same coaching for their own teams. Training plans will also be necessary to pull people away from older, more comfortable ways of working and toward something new such as augmented analytics as they make business decisions. The C-suite will then need to prioritize training and professional development at every layer and in every department. These kinds of efforts create a culture where analytics can thrive.

Embedded analytics and machine learning help companies get the most out of their data and confidently make critical business decisions. The success of embedded analytics for the long term will rest largely on the C-suite’s adoption. C-level executives need to lead by example by personally using predictive analytics, implementing machine learning, and promoting a data-driven culture to ensure ongoing analytics success.