Business analysis is only as good as the quality of the data. If the testing process is weak and the data quality and data integrity tests are suspect, then the business could be at risk. Learn how to get the most out of your data, warehouse, and business intelligence testing.
Informed companies make better decisions, and business intelligence (BI) solutions are vital to the health of enterprises of all sizes. The need for BI solutions is not about the technology but rather the information that the solutions deliver, so business owners should drive the need for BI solutions. The technology team does not create the data or use it in a business context, but it does provide mechanisms to store, retrieve, and archive the data accurately and securely.
BI tools organize dissimilar and diverse data across the enterprise, enabling multidimensional relationships of the data that can provide keener insights that empower decisions to leverage opportunities with on‐demand intelligence. More than just spreadsheets on steroids, BI allows the ability to tailor the information to the user's perspective.
In today's economic climate, there is a focus on reducing waste and maximizing benefits on information already known about consumers and end-users. A business that invests time and money into its BI initiative has built and substantiated a business case around why that investment will yield a positive business return from both revenue and cost perspectives. It has a solid framework and data architecture, good governance, aligned leadership, and the right tools. But, this business's analysis is only as good as the quality of the data. If the testing process is weak and data quality and data integrity tests are suspect, the business could be at risk.
Where to Start
As with any strategy, BI initiatives must be defined, organized, developed, and tested. It is especially important to define a data stewardship role—a person or committee who will own and police data integrity between key functions in the company. This role becomes essential when integrating data across conflicting business rules.
The transformation of data into information and then into intelligence (or, "intelligent decision making") is the primary goal of BI tools. Starting with the source data coming from multiple systems, the scope of testing will need to be considered due to the various combinations of migration, conversions, and transformations of data across the enterprise. Add to these combinations the many relationships and dimensions of the BI tool, and the number of test combinations is in the billions. For complex systems, the combinations could easily approach the trillions.