The Benefits of Advanced Analytics in a Complex Data Environment

Problem:

An industrial manufacturing distribution company sought a growth strategy through aggressively paced acquisition. As more and more companies were acquired issues in reconciling the various data structures began to grow. Over the course of two decades of acquisition, the data environment had grown so large and complicated that simply getting a list of the company’s products involved thousands of lines of SQL and processes within processes… within processes. As the complexity of the data environment grew the quality of the data being amalgamated declined. This led to issues across many of the company’s operations that ultimately resulted in a loss of customers and profit.

Solution:

We determined that in order to simplify the data environment a new architecture had to be implemented. Using the Kimball approach to data warehousing, we focused on what data brought value to the business. We spoke with stakeholders across the company to properly define the meaning of each datapoint. Using these reconciled and agreed upon definitions each datapoint was dimensionally modeled. Fact tables were built on top of the dimensional tables and ETL was implemented to keep the warehouse up to date and to handle any slowly changing dimensions. After completing these steps and allowing ample time for QA, we began rewriting crucial reports based on the newly created warehouse tables.

Outcome:

Employees were shocked as the time needed to produce reports was dramatically reduced. The data we provided finally matched third party results and employees were starting to trust the data in their reports again. With a simplified data environment, we were able to provide more advanced analytics in our reports. All these factors led to a reduction in issues across many of the company’s operations, which ultimately led to happier customers.