Abstract:
The budget period presents a valuable opportunity to step back and establish a solid foundation for value optimization. This includes refining the Data & AI strategy and vision, aligning initiatives with key strategic pillars, prioritizing initiatives based on their value and accessibility, and building robust business cases.
Data product management is a natural extension and enabler of the Data & AI strategy, offering immediate benefits, such as rationalizing initiatives, but requires a phased approach. For example, formalizing data requirements for each initiative can later create a clear lineage between data initiatives and data products. A key focus should be on shifting the mindset from a “data push” to a use case-driven “data pull” approach.
Early clarification of how an initiative generates value is essential. It leads to effective prioritization of data needs and helps define the minimum viable scope for data products, while keeping the target architecture in mind.
Moreover, establishing a value measurement methodology early in the process is crucial. It also is the prerequisite for value tracking. Designing a value measurement methodology ahead of the delivery is mandatory, ensuring that value creation is both measurable and sustainable once in production.
