Improved data analysis continues to be a focus for enterprises as it promises a path to higher efficiency, a chance to outmaneuver competition, and the ability to proactively meet client needs by turning messy data into actionable insights. While business intelligence and data analytics are becoming the go-to approaches for embracing data analysis among enterprises, many companies are choosing to invest in developing their own data warehouse. To generate valuable insights from deep data analysis, enterprise companies need a reliable data warehouse as the foundation to build upon.
Today, a data warehouse is used to do more than just integrating data from multiple sources for better, more accurate analysis and reporting. A data warehouse must also be reliable, traceable, secure, and efficient at the same time. It needs to offer these advantages to differentiate itself, especially in business intelligence. This is where good data warehouse governance becomes very important. There are several enterprise data warehouse best practices and governance tips to keep in mind, along with key principles to implement.
Three Crucial Components
Good data warehouse governance starts with putting three crucial components in place: leadership, organization, and process.
Leadership: It is critical to create a team of data governance champions in data governance. This is a DevOps concept to roll out best practice adoption through strong and respected leaders from any position and hierarchy in the company. These leaders will coach the rest of the team to understand and implement the values of data governance. Governance needs the support of invested stakeholders in the company to ensure adoption success. Additionally, the leadership team needs to ensure the constant support of the business. Many governance projects fail when its gets tough because of a lack of support.
Organization: You will want to Build a plan that is flexible enough to support growth. For good data warehouse governance to be implemented, best practices and data management policies need to be implemented correctly and consistently. In the case of data warehouse governance, strict control and constant oversight are very important for maintaining the quality of insights generated by data analysis.
Processes: Good data warehouse governance isn’t something you can “set and forget.” It is a continuous process of review, fine-tuning, and enforcement of data governance policies. For the data warehouse to remain effective in serving its purpose, the data warehouse governance process must also be effective.
Continuous Change
Having leadership, organization, and processes makes it easier to achieve the primary objective of keeping the data warehouse relevant and effective, which often means adjusting to changing business objectives as needed. As enterprises begin their governance journey, strong leaders will need to be alert and ready to identify any necessary changes.
However, different decisions require different sets of information and insights. This gives the data warehouse champions a unique vantage point in seeing how the data warehouse must meet demand for information. The same is true for input changes. As demand for insights and information shifts, the data warehouse must be able to integrate data from new sources and also remain effective enough to carry out data analysis across the entire catalog.
The infrastructure supporting a company’s data warehouse may also change especially with growth and demand changes. At the very least, more storage will be required as more data sources are added. Advanced processing, such as machine learning-based big data analysis, requires varied computing power and memory. There are even changes in networking configuration to anticipate. Lastly, maintenance is the final component in the equation. It involves assessing potential risks, handling operation issues, and making sure that the data warehouse remains operational at an efficient level. That last part can be a big challenge for bigger enterprises, since they tend to overstress resources sooner than needed to fill performance gaps.
The Implementation Team
When setting up a data warehouse, it is important to make sure that data governance policies and enforcement efforts remain true to the business objectives of the company. However, an improved organizational element is not the only component needed to achieve good data warehouse governance. It is also recommended to assign implementation team leads that will handle the day-to-day tasks of ensuring compliance, maintaining the data warehouse itself, and making sure that the data warehouse (and the data streams it integrates) continue to perform optimally.
The implementation team should include the data governance champions as a best practice. As suggested earlier, these can be from any role in the team such as network administrators, systems administrators, data engineers, data modelers, and front-end developers for when UI or other components are needed.
The Payoff
There are multiple reasons why good data warehouse governance is a must, and it goes beyond the need for better data collection and management. For starters, data can be fully integrated and processed holistically. For example, data relating to financial activities of the company can be made more valuable when compounded with external data about industry average values, competitors’ actions, and market growth. This can be seen through sales data that can be analyzed more deeply within the context of market performance and changes. The result is a healthier data-driven decision making process, and one that encourages collaboration between departments. There is also the possibility of increasing revenue from good data warehouse governance, both from the reduction of CAPEX and OPEX, and from the increase in revenue through the discovery of new opportunities.
A data warehouse doesn’t have to be complex. Through good data warehouse governance and the implementation of data management best practices, everyone in the enterprise can play an active role in maximizing the business benefits of a data warehouse.