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Case Study

Industrial equipment supplier engages Baker Tilly to simplify their on-premise data environment

Industrial equipment supplier engages Baker Tilly to simplify their on-premise data environment for their sales data. Read this case study to learn how this company is now set up to scale effectively and can continue to further enhance their reporting needs.
Semi truck driving down road
Case Study

Industrial equipment supplier engages Baker Tilly to simplify their on-premise data environment

Industrial equipment supplier engages Baker Tilly to simplify their on-premise data environment for their sales data. Read this case study to learn how this company is now set up to scale effectively and can continue to further enhance their reporting needs.

Client background

This company is an industrial equipment supplier with employees across the US and Canada. They manufacture and distribute large service equipment for the marine, oil and gas, transportation and construction and lift markets.

The business challenge

Over the past few years, this company acquired multiple companies, creating a mix of data sources with different granularity that was relatively unorganized. Because of this, they were looking to upgrade and simplify their on-premises data environment for their sales data.

Strategy and solution

Baker Tilly designed an ideal data warehouse that integrated all the company’s data into one uniform structure and place. We restructured their data using the Kimball method so that the company could easily scale as their data grew. With the data in a much more usable place, using Power BI we were then able to completely rebuild and enhance existing reporting with new KPIs and measures to help them better understand what the data is telling them. With the new and improved data warehouse, they are now set up to scale effectively and can continue to further enhance their reporting needs.

Baker Tilly was able to implement this solution by doing the following:

  • Extract data that came from existing systems and store that data in different fact and dimension tables to allow for easy exploration
  • Recreate measures and calculations from existing KPI reporting using the new data warehouse structure that was in place
  • Enhance existing reporting with new visuals and trending charts allowing the client to better understand what the data is telling them
  • Knowledge share and ensure end users know how to use the new data structure to its full potential
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