| | APRIL 20249CIOReviewThe 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 consistently and sustainablyconsistently and sustainably. Obviously, pressuring business needs are prioritized and addressed by MVPs (minimum viable products), but the teams will always keep in mind what the endgame products should look like. The mindset differs from what we see in project teams as they tend to be more concerned with delivering the project scope, not necessarily with the long-term vision. Hence, it also aligns quite well with executing data strategies and the implementation of data management functions ­ especially the foundational functions (sometimes called "defensive") like Data Governance, Data Quality, Reference and Master Data Management, etc.We found the communication with business stakeholders got a lot easier as they seemed to better understand that requirements not shipped in MVPs do not fall through the cracks as they have seen happening in projects, but are backlogged for future product releases instead. There is no fear of losing an important feature and this helped a great time while resolving conflicting priorities. As "Product Managers", our data professionals must be constantly checking the effectiveness of their data products, listening to the users' feedback, addressing technical debts, and maintaining alignment with industry best practices and trends.Implementing a successful data strategy in large organizations takes lots of time and effort. While it is possible to achieve the goals with a set of correlated projects in a program, I would rather recommend adopting a product approach. The data strategy becomes a powerful mantra for the product teams and the talents are more engaged in something they believe in. They "own" the products and will think twice before taking shortcuts to just deliver something. More importantly, the foundational building blocks of your data management framework are rarely at the desired stage, so data teams have to strike the right balance between groundwork and innovative initiatives. Having a product mindset will also keep the focus on the endgame solutions and avoid the pitfalls of short-tem implementations that are not properly maintained and lose value quickly.
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