Data warehousing is a crucial part of any massive data initiative. Unfortunately, data warehousing is a potentially confusing and complex process that has deep consequences when performed improperly. A data warehouse that is not properly implemented, organized, and managed turns access into a stumbling block.
One benefit of establishing and optimizing your data warehouse is that it becomes much easier to run advanced search- and AI-driven analytics based on even large volumes of data from multiple sources contained within. This is a functionality most organizations will need down the line.
A good data strategy starts with good data warehousing. While there’s no one-size-fits-all approach, there are best practices to guide organizations in adopting an effective strategy.
Make these your priority as you engineer the ideal data warehouse:
– 1. Ensure Executive Buy In – A data warehouse should not be thought of as an IT initiative. Rather, it should be considered a critical asset for every department and professionals at all levels. To get the necessary institutional support, executive in the C-suite must understand and endorse the project fully.
– 2. Define KPIs and Expected ROI – Data warehousing has a lot of potential, but it shouldn’t be assumed to be working perfectly. Clearly defining performance indicators at the start helps to track the progress and impact. Setting benchmarks for ROI also ensures that data warehousing is delivering as much as it promises.
– 3. Start Early – The time to begin implementing a data warehouse is before or possibly while implementing enterprise-class technologies. Creating a data warehouse ensures that these technologies have a deep pool of historical data to draw from, increasing their utility and capability significantly.
– 4. Speak with a Specialized Consultant – Since every data warehouse is different, it helps to seek out someone with IT expertise relevant to your specific industry. That way the data warehouse incorporates all the most relevant data and responds to the unique workflows of someone in finance, manufacturing, sales, or marketing.
– 5. Study the Data Sources – The average business relies on multiple different system to generate data. Some of those may be interoperable now or some may not. Either way, they need to be interoperable with the data warehouse to seamlessly grow the size of the data set. Investigating integration issues early removes a lot of roadblocks further along.
– 6. Plan to Scale and Evolve – Data empowers businesses to grow and expand into new markets and target demographics. That means the data warehouses that businesses start with must be able to keep pace with that growth. Constructing a warehouse that is both flexible and extensible keep companies from becoming victims of their own success.
– 7. Prioritize Intuitive Access – Like most complex IT systems, data warehouses run the risk of becoming inaccessible and unwieldy as they grow larger. In other words, they become less manageable at the same time they become more important. Engineering the warehouse in a way that is intuitive and accessible for the largest number of stakeholders should be a priority from the outset.
– 8. Practice Data Governance and Auditing – Establishing strict governance at the start ensures that data warehouses evolve in a standardized and systematic way. Regularly auditing the warehouse confirms that governance is having the intended effect.
– 9. Incorporate Business Perspectives – Building a data warehouse traditionally falls to the IT team, but the warehouse is used by departments across the enterprise. Since business use is the intended purpose, the IT team should have or be able to access expertise on how business intelligence is utilized and why it’s required. That perspective leads to a more functional data warehouse overall.
There is still much left to plan before your data warehouse gets off the ground. But these best practices are a sound road-map. And once your warehouse is up and running, the complete power of data is instantly at your disposal.
