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The Secret to Successful Business Intelligence: A Top-Notch Data Warehouse

Outdated information and disagreement over data definitions was impeding Rensselaer Polytechnic Institute’s progress. To the rescue: a business intelligence plan that emphasized end user buy-in and support for accurate data.

Rensselaer Polytechnic Institute needed a better way to make admissions and financial decisions. Like many organizations, systems and processes for collecting and analyzing business data were fragmented. Executive meetings to discuss strategy too often stalled over the accuracy of reported numbers.

For example, how many faculty members or students did the university really have? Nobody agreed on a single set of terms to define them. Instead, different departments used their own definitions and different ways of looking at the data. On top of that, financial reports did not always contain the most up-to-date information. Furthermore, university researchers often kept track of their grants using shadow systems, requiring double the effort to get the university’s ledgers to match the researchers’. Finally, the admissions staff needed more timely demographic information about its applicants to inform student selection decisions.

Getting a handle on the data has been critical because higher education today is a tough arena. Government funding is down, requests for financial aid are up and admitting a diverse student body—in terms of gender, geography, ethnicity and academic achievement—has become more challenging. All these factors make balancing the supply of enrollment acceptances and financial aid with the demand from student applicants more challenging than in the past. The better Rensselaer could optimize its administrative resources and time, the more revenue it would have for courses and scholarships to attract the best and the brightest.

The answer was a business intelligence and enterprise data warehouse implementation. BI tools have helped Rensselaer to refine its recruitment strategies and save time doing so. But getting the ROI for such projects can be tricky due to the changes in data reporting and usage they require. CIO John Kolb and his team had to establish cross-functional support for the project at multiple levels within the university; develop a vision for the project that could be built out in steps; create enterprisewide processes for collecting and using data; and support end users with communication and training.

Here’s how they did it:

  • Create cross-functional support.
  • Think big, start small, deliver quickly.
  • Create one version of data truth.
  • Provide support for new behaviors.





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