In the rapidly evolving landscape of data management, the concept of treating data as a product is gaining significant traction. This approach marks a departure from the traditional centralized data warehouse and data lake models, introducing a more flexible, autonomous, and efficient way of managing data. Let’s delve into the key distinctions and benefits of adopting a data product-oriented approach.
From centralized control to autonomous teams
Traditional data warehouses operate in a centralized manner, following a waterfall methodology. The process begins with gathering business requirements and setting priorities, followed by data modeling, ingestion, cleaning, and publishing. Typically, this involves a building phase and the subsequent publishing of new tables in the data catalog. A governance team then steps in to create metadata, ensure data quality, construct lineage, and apply business terms.
In contrast, the data product model champions autonomy. Here, teams have the freedom to manage changes without the need to prioritize use cases or strictly control the data modeling and governance phases. All data governance responsibilities reside within the same team that manages the data product, fostering a cohesive and efficient workflow.
Standardization in interfaces, not processes
One of the key shifts in the data product approach is the focus on standardized interfaces rather than standardized processes. In a DWH, the standardization of processes is crucial to avoid a chaotic, monstrous system. However, in the data product model, the emphasis is on how data is exposed through control ports, metadata and observability interfaces. The internal implementation can vary, with no strict requirement to follow staging, bronze, silver, and gold tiers. The critical aspect is ensuring the end result is exposed consistently and effectively.
Shift in responsibilities and timing
The data product model also brings a significant shift in responsibilities and timing. Unlike the traditional model where data governance comes as a subsequent phase, in the data product approach, data governance is embedded within the IT delivery process. This means that governance duties are performed by the same team managing the data product, ensuring that governance is an integral part of the entire lifecycle.
Moreover, the definition of done (DoD) in a data product context encompasses more duties than typical data pipelines. It focuses on creating a product that can be maintained and evolved by the same team, emphasizing sustainability and continuous improvement.
The bottom line
Adopting a data product-oriented approach transforms how organizations handle data. It empowers teams with autonomy, ensures consistent and standardized data exposure and embeds governance into the delivery process. This paradigm shift not only enhances efficiency and agility but also aligns data management practices with the evolving needs of businesses in a data-driven world.
As organizations continue to navigate the complexities of data management, embracing data as a product will undoubtedly pave the way for more innovative, responsive and sustainable data practices.