Data Warehousing and Data Mesh work better together
Data warehousing requires data centralization, whereas data mesh enables a decentralized approach to data access. Organizations might think that the solution to their data management strategy requires a choice between the two, but the reality is that both approaches can and should co-exist.
To me, it feels like the market has gone back and forth between the best approach for years. The reality, however, is that data exists in multiple locations for a variety of purposes. Not all data integration is used for data warehousing or analytics outputs and organizations require flexible data architectures to access information assets in the way they need to effectively succeed in their roles. Industry trends shouldn't be used to direct a company, but should be a guide that provides a company with the added flexibility needed to give data consumers access to the information they need when they need it.
Companies need to evaluate what data approaches make the most sense for their needs. Here are some of the reasons organizations might choose both and why they go better together:
- Data warehouses and data lakes enable data centralization for specific purposes. No matter how flexible, the reality is that data outputs are usually tied to third-party analytics and visualization tools. In most cases, there isn't the flexibility to change pipelines quickly making analytics pre-defined. The ability to explore is there but within the confines of what exists in the warehouse. This limits visibility and only takes into account what has been asked for. At the same time, this type of data is more easily governed and can be pre-created to ensure that regular access to similar data is available for decisions.
- Data mesh enables data flows across the organization that follow the flow of business processes. For operations and operational insights, this is great because data does not have to be moved to gain insight. It enables a more flexible approach to information insights.
- Organizations that leverage both approaches are more apt to align their data pipelines to business outcomes. Instead of being limited to a data warehouse or specific data gathering and storage processes, organizations have the flexibility to design answers to challenges without disrupting data at the source.
- Not all data warehouses support latency requirements for real-time needs. Depending on the investments made and how entrenched analytics are, some organizations will need to consider additional toolsets to support newer needs. Enabling data mesh can support more diverse needs without always needing to add to infrastructure or tax budgets.
- More organizations are looking at operations and process integration with analytics. This usually requires a lot of moving pieces and a general understanding of data flows and data asset value. Data mesh allows that level of data movement while the data warehouse ensures that curated content can be consumed and insights gleaned, making both work more effectively together.
Organizations should not have to choose between one of the other. Successful data management requires agility and flexibility in approach and access. Therefore, many companies need to leverage both centralized and distributed approach to data storage and pipeline creation. Diversity of data needs requires the ability to think more broadly about technologies and infrastructures that support both analytical and operational requirements. Over time, the more organizations look to adopt a variety of concepts to meet data needs, the more likely they will build up the teams that support overall business outcomes.