Quality ingredients are a must if you’re looking for quality results and this is especially true for business intelligence. In order to draw useful conclusions, BI teams must be able to look at accurate data that is relevant for their specific queries. This can be more complicated than organizations may realize, thanks to the overwhelming amounts of data that companies are producing at all times. Sorting through this data, eliminating outliers or faulty numbers, and organizing it into useful segments are all necessary steps towards ensuring data quality.
Fortunately, this is a two-way street. The right BI tools can also be used to help improve the quality of data itself, in a self-supporting cycle that strengthens each element. In this episode of the BI Report, our panel will look at how to leverage your BI solutions to boost your data worthiness.
In this episode, we’ll cover:
— Why improving data quality is so critical for BI teams
— The Extract, Transform and Load (ETL) process and its strengths & limitations
— Using BI tools to refine data aggregation at the source
— And much more