The role and attributes of the chief data officer 4.0
With the increased use of data and analytics across the enterprise, the chief data officer’s mindset needs to shift from focusing on D&A projects and programs to driving a product-centric organization, according to the Gartner Research Board.
“We believe this is the rise of a new type of leader — the CDO 4.0,” says Mario Faria, vice president and program director at the Gartner Research Board.
“CDO 1.0 was focused exclusively in data management. CDO 2.0 started to embrace analytics. CDO 3.0 led and participated quite heavily in digital transformation. This fourth version of the CDO is focused on products, and on managing profit and loss instead of just being responsible for driving D&A projects and programs,” Faria explains.
The Gartner Research Board’s Global Chief Data and Analytics Officer (Global CDAO) offering provides additional insight on the CDO 4.0 and a product-centric organization through a community of Global CDAOs engaged with their true peers at comparably sized organizations.
Traditionally, technology investment has been structured as a pool of ongoing “run the business” costs and a separate portfolio of discrete capital projects that have a clearly defined beginning and end, Faria explains. However, organizations are now beginning to align funding, development resources and ongoing management support around a set of enduring product lines.
Gartner expects 72 percent of organizations to be using the product model this year.
“The change to a product-centric organization must focus on business areas where there is room to innovate, such as supporting a new business model,” Faria says. “Product-centric approaches make it easier to rapidly innovate and iterate because they focus on user experience, evolving requirements, and the strategic differentiation for what you are delivering.”
A product-centric D&A organization requires new skill sets, roles, investment models and the right culture. Gartner Research Board has identified three steps to becoming a product-centric CDO.
Think Platform First
When tasked with creating a product, the CDO should plan the D&A platform first, then define the release plan and roadmap. Following those crucial steps, a team can be built to make the delivery cycle happen. Faria said CDOs must think about the data first and the use cases later, following the lead of leading D&A companies, such as Facebook, Amazon, Google and Apple. He said these companies have built successful platforms that allow data capture and analytics usage, with faster life cycles between releases.
“Most D&A leaders believe D&A platforms play a meaningful role in their D&A future, though scale and scope differ by company, maturity and investment levels,” Faria says.
Change Investment Models
CDOs should work with their organizations’ leaders to adapt or change the investment models their businesses currently have in place, Faria says. Serving one area or one team on an individual basis is not a scalable model anymore. CDOs should look to scale the usage of the platform across the enterprise.
In the product line management model, product lines are funded based on the business capabilities they support. Common or shared capabilities — such as infrastructure, technology, D&A — are funded based on the anticipated and aggregated needs of the product lines they support.
Use a Proven Product Management Methodology
CIOs and other cross-functional executives, as well as business unit leaders, must set the tone for the importance of product management in driving successful growth and scale, Faria explains. It is critical that the role definition is clear to all stakeholders. Fundamental to the role definition are its vision and objectives, goals, and scope and metrics — specifically, the metrics for transitioning toward a customer-centric approach for innovation.
“Not all CDOs will be able to make the transition from a project or programs mindset to a product mentality,” Faria says. “CDOs who are able to deliver results using an operating model that hides details and is focused on the better, more scalable usage of the data assets, will succeed.”