In our last post, “What are Data Warehouses?”, we talked about what a data warehouse is, some of the history behind it, and when your organization may need a data warehouse solution.
Now we will going to explain the top three requirements for creating a data warehouse solution.
#1. Executive Sponsorship
Without buy-in from senior executives, data warehouse projects are likely to fail, as data fiefdoms will fight the “loss of power” and may actively sabotage the project.
This is something we’ve unfortunately seen happen at several client organizations. These “Data Kings and Queens” will often not attend meetings, causing the project to slow to a crawl; hide or not disclose key information about the data to ensure erroneous answers for the warehouse; or sometimes simply refuse to provide the data to the data warehouse team.
All of these examples have a critical impact on the success of the initiative and, left unchecked, will cause the ultimate failure of the project. Executive sponsorship helps to ensure that these issues do not arise and that the project meets the strategic needs of the organization.
#2. Robust infrastructure to meet the demanding needs of the data warehouse
The processing of data from individual transactions to analytical reporting data structures requires processing power (CPU), memory and a high bandwidth network. Infrastructure which is not architected to process an ever increasing amount of data in the same or shrinking amount of time will negatively impact the use of the warehouse because the data will not be available when a decision-maker needs the information to make his/her decision.
#3. A vendor to implement a custom data warehouse solution
The selected vendor must be able to capture and document the strategic and tactical requirements of the organization, design a flexible and robust solution addressing the needs of the enterprise, and be able to quickly and efficiently implement the designed solution for the chosen database and reporting platform.
There are many definitions for data warehouses and even more competing ways to design and build them, but the desired result is the same: glean insight from the organization’s data to gain a competitive advantage. Check out these related posts around best practices for data warehousing methodology, database lifecycle management, and evaluating the best data warehousing solution for your organization.
- What Are Data Warehouses?
- Data Model Best Practices for Data Warehousing
- Data Warehousing Documentation Requirements
- Measuring Data Warehouse Performance