CIOs Must Lead the Data Science Charge

The CIO’s role has changed dramatically in the past five years. Becoming an enabler of business success is now expected.

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The CIO’s role has changed dramatically in the past five years. Becoming an enabler of business success is now expected. What’s emerging, and is just as important, is that the CIO must take the lead in enabling data analytics within the organization. This includes supporting data scientists as they integrate data sets within the enterprise and establish themselves as the center of expertise for all things having to do with data. Above all, it’s about providing the leadership to ensure that the organization develops the critical business insights that can only come from the effective use of data.

As Ben Gaines, Director of Product Management for Adobe Analytics, has noted, “CIOs are uniquely positioned to deliver an organizational platform that provides the depth and breadth of data and analytics tools that meet a range of demands, from the casual user to the highly skilled data scientist.”

To do this, IT and the CIO must develop a strong understanding of use cases (and how they interact) and ensure that all data is available for analysis. As Gaines says, “The CIO has to take on the imprimatur of the ‘broad perspective’ and ensure that the data is comprehensive and supports the organization as a whole.”

Most CIOs intrinsically understand the need to take on the mantle of chief data scientist, so the question then becomes: What are the first steps to take? Here’s what Gaines suggests: “Many data science activities are done in isolation, so the CIO must become the ‘glue’ for a cohesive approach. For example, ensuring that sales, support, and marketing data is used holistically across all these functions is a critical step. The CIO must also line up business unit and executive management support for specific data science initiatives, such as utilizing customer experience data to inform both sales and product management.” 

This interaction will identify how data science is currently used, and what the most important use cases are. Armed with this knowledge, the CIO can build solutions that enhance the most important current use cases or new ones that provide competitive advantage.

It’s also important to realize that, even though every enterprise wants to be data-driven, there are no shortcuts, and it will take hard work to add value to—and enhance the use of—data. With use cases in hand, the CIO should pick a small number of high-impact projects. Focusing on revenue-generating or customer-facing data science initiatives can be a good place to start. 

Adobe Analytics supports the CIO’s efforts in many ways. Among the most important is moving from just creating insights to identifying specific actions that drive success utilizing AI and machine learning. This predictive analytics functionality enables data scientists to explore the future rather than just evaluate the past. In addition, Adobe Analytics can integrate CRM, web, mobile, and even connected car data into a single comprehensive data source that makes this information easily available for use by data scientists working for other business units.

For more information on Adobe Analytics or how Adobe can support the CIO’s commitment to data science, please click here.

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