Data strategy – What does it mean The strategy broadly needs to address the following foundational pillars aligned with building a connected data and decisions systems foundation that will drive innovation at scale and industrialization at scale. The pillars must be realized iteratively along with biz use cases aka decision backwards to drive value realization in an iterative manner. Deliver Data at Speed : “Platform economies & Productized thinking” “Data platform as a product“ The process of managing all data types (including multi-modal data), from initial sourcing to the creation of data products ready for enterprise use, should be centred around composable and reusable services/Gen AI agents. This facilitates rapid data onboarding and expedites the utilization of data throughout the organization. The services for data ingestion, integration, and harmonization should be designed to handle diverse data types, large volumes, & high velocities, in a metadata driven way, that are composable & promote autonomous DataOps across the entire enterprise. The architecture and design should revolve around an Integrated Data Platform as a product, with provision for extensibility & scalability to accommodate both new & existing services/ Gen AI agents, encompassing cloud agnostic & cloud native paradigms, addressing the entire spectrum of Data Engineering, AI Engineering, & Gen AI Engineering. Data as a strategic asset, Metadata as a product – Data Governance, democratization & Data Culture Data assets should be productified Data assets should undergo a transformation akin to the treatment of an enterprise's products and services. Within each domain, there should be a clear definition of ownership and management responsibilities for every data product. This approach ensures accountability for maintaining catalogue, data quality and lineage, and in enabling data consumption that aligns with various analytics requirements, as well as considerations for privacy, security, and regulatory compliance, which are essential for BI, AI, and Gen AI paradigms. Furthermore, the management of metadata & data should be intertwined when handling data products. This integration offers a seamless and intuitive experience for searching & discovering data, augmented by a social context that fuels creativity & opens countless possibilities. Think of it as creating a digital data storefront or marketplace, akin to the user-friendly experience one encounters on platforms like Netflix but tailored specifically for data products. DataOps across the entire enterprise.
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