| | APRIL 20248CIOReviewBy Laércio Queiroz, Global Data & Analytics Director, AB InBevIN MY OPINIONYou are the Chief Data Officer of a successful business and have been assigned a mission to define and execute an effective data strategy. You have taken some time assessing where the organization is at in its overall business strategy, its drivers, main objectives, constraints, and reservations. Then you focus on the data side of things and start to shape a data strategy that will help your organization achieve its goals with data. Most contemporary businesses are project-oriented, so once the data strategy is defined you move ahead on setting up a plan with a programmatic view of data projects required to deliver it. Usually, those projects will create new or improve the maturity of existing data management capabilities. They will target strengths, opportunities, weaknesses, and threats identified in your data strategy. The projects are executed according to the plan, your teams (and the business) are happy with their results and they eventually come to a closure, freeing up resources for the next big initiatives. This short novel can be seen as a successful journey, and it is indeed, but then reality comes to hit us. No matter how well you execute your data projects, as soon as you close them, there will be a new business or IT requirements surfacing and they can reduce the effectiveness of your data strategy quickly. One may argue that the project deliverables are subject to a plan-do-check-act cycle and new requirements could be addressed by new projects. Now it is time for our friend reality hit us again as it is often quite difficult to win a project selection battle when what you are proposing are improvements to capabilities already in place. Put simply, new projects that create "new things" look more interesting than the ones that keep things running. How do you deal with technical debts of closed projects? How do you keep your talents engaged and up-to-date in the ever-changing data space? A Product-Oriented OrganizationI have joined AB InBev in 2021 to lead the Data and Analytics teams in our major digital transformation program. The program itself is focused on the standardization of core business processes and the modernization of the underlying technology platform. In that context, we have deployed product teams to take over entire knowledge areas and process groups of the business. We follow the same approach with our data teams. Once we have defined our data strategy ­ set the field game, targets, and reasons to win -- aligned with the program strategy, we arranged product teams to work on individual elements of it. While some teams are developing software as their products, others have been creating the building blocks for other data and functional product teams.The ultimate goal of defining an organizational structure like that is to build high-performance teams to ship products that will satisfy customers' needs throughout a long journey Laércio QueirozEXECUTING THE DATA STRATEGY IN A PRODUCT-ORIENTED ORGANIZATION
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