Data warehouses serve as flexible and scalable repositories for centralizing an enterprise’s data and facilitating access to information for analytics. Businesses continue to adopt data warehouses at a fast pace — the data warehouse market is expected to grow at a compound annual growth rate (CAGR) of over 15% through 2025.

We’ve compiled a list of six data warehouse best practices that can help businesses meet their needs and improve time to value for data analytics and business intelligence.

1. Involve stakeholders early and often

A data warehouse must meet the needs of stakeholders like department managers, business analysts, and data scientists, as they all access the information the warehouse contains to run analyses and create reports. Incorporating feedback from these parties improves the chances that an organization’s decision-makers will have the information necessary to make informed choices and reduces the chance of requiring substantial changes later.

For instance, collaborating with high-level managers and executives keeps a data warehousing project in tune with the enterprise’s overall business strategy. Working closely with these key decision-makers also helps ensure that they buy into the project from the beginning. Without the support of management, a data warehouse project may never get off the ground, or may be terminated down the road.

2. Incorporate data governance

If the data fed into a warehouse is of poor quality, then centralizing it for analytics is pointless — the results of analyses will be inaccurate and misleading. To avoid this, organizations should implement robust data governance processes. Departments should work to define the security, collaboration, and retention policies for their data assets based on their business and legal requirements.

3. Define user roles

User roles determine who can read, write, and update data within a warehouse. If, for instance, a field needs to be updated in a department’s source data, whose responsibility is it to update the ETL jobs to account for this? Do code and data updates need management approval? What happens if someone wants to integrate a new data source?

Without appropriate processes and access control, users will likely break the data pipeline by failing to coordinate their efforts, particularly at large organizations with many analysts working with the data warehouse. But enterprises should also ensure that user controls are not too restrictive, as overly bureaucratic systems can inhibit productivity by preventing experimentation and iteration.

Organizations should strike a balance between security and the operational flexibility necessary for analysts to work effectively. As part of that balance, an enterprise could have analysts set up sandboxes to test changes to data structures and require that they obtain approval before merging altered data.

4. Understand data warehouse schema design

An organization should design its schemas to suits the data warehouse technology it’s using and its business needs. For example, the normalized structure of a snowflake schema requires less storage and processing resources than the slightly more denormalized data structure of a star schema, but the latter facilitates faster data queries. The scalability of cloud data warehouses now allows enterprises to denormalize their data to increase querying speed free of resource constraints.

5. Iterate and test — then do it again

Taking an agile approach to developing and maintaining a data warehouse can improve the repository’s performance and ability to adapt to an organization’s changing needs. By utilizing short development cycles with small, well-defined tasks and testing plans, development teams can get faster feedback on their results from relevant stakeholders and then iterate to improve their systems and processes. This creates a quick feedback loop for product development, and allows an enterprise to identify and resolve issues with its warehouse before they impact users.

6. Take advantage of ELT and cloud data warehouses

ETL (extract, transform, load) and ELT (extract, load, transform) are the processes used to ingest data from its source, transform it as needed, and store it in the data warehouse. By moving the transformation step to the end of the process, ELT allows organizations to ingest data and begin analyzing it more quickly. Cloud data warehouses are well-suited for use with ELT, as their scalable CPU resources can handle data transformation after loading. Cloud data warehouses have many other advantages in comparison to their on-premises counterparts as well.

Since ELT loads data into the target system before it’s transformed, an enterprise must have a data modeling tool, even if it’s only SQL, to prepare data for use in analytics. The data engineers who build and maintain data warehouses should seek guidance from subject matter experts so they can provide end users with data in a format that meets their needs.

Using a cloud data warehouse with Stitch

A well-structured and maintained cloud data warehouse allows organizations to access and analyze data. Stitch provides a comprehensive data pipeline for replicating your enterprise’s data to your preferred cloud data warehouse. Try Stitch today for free.

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