by Diann Daniel

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

Nov 05, 200710 mins
Business IntelligenceData Warehousing

Outdated information and disagreement over data definitions was impeding Rensselaer Polytechnic Institute's progress.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.


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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.

Once university leaders identified the need for the BI system, President Shirley Ann Jackson created a committee of top-level executives to sponsor it. The sponsoring committee was cochaired by Kolb and the vice president of finance. This group set strategy for the project.

These business heads appointed representatives to a steering committee, which developed the overall implementation plan and controlled the scope and budget of the project. In addition, a number of implementation groups were formed including a data warehouse group that housed both technical and business staff to execute specific pieces of the project.

Of course, creating cross-functional strategy and planning teams is important for any enterprise IT project, says John Hagerty, an analyst at AMR Research. But it’s especially true for enterprise data warehouse and business intelligence projects because their success depends on broad user support and because consequential business decisions are made on the faith that information is accurate.

When it came time to deploy the BI tools at Rensselaer, that top-down and cross-functional support was crucial. For example, Jackson made clear that she only wanted to see numbers that came from the data warehouse. Strict data governance was enabled through multi-committee support. And creating new processes for data reporting—such as how to divvy financial credit in multi-disciplinary research efforts—was aided by the cross-functional relationships and understanding that had been built up during the development phase.

Think big, start small, deliver quickly.

Once Rensselaer decided to deploy BI, the first six months of work focused on laying a strong foundation. The grand vision for the project assumed that eventually, all business data would be filtered using the BI tools. And so, the university developed an overarching data policy and procedures that could be used by any groups created to define, cleanse and manage information on an ongoing basis. At the same time, Kolb’s group created a systems architecture based on an Oracle data warehouse, Informatica’s PowerCenter data integration platform and Hyperion business intelligence technology.

For its first set of reports, the university chose financial information, unveiling a data mart (a collection of data about a specific subject) and reporting tools in November 2002. “We wanted to have a success at a very fundamental level,” says Kolb, “and finance touches everything.” That pervasiveness is not just in terms of the data itself—everybody has a budget—it’s also about the people involved. The finance group, like IT, works with everyone. And its success with the application made the group a powerful advocate. “Finance became a huge partner with us,” says Kolb.

Hagerty of AMR Research says focusing on quick gains is a key to success in any BI project. “Start small, deliver value and get people bought into the value along the way,” he says.

This first project exposed a lot of dirty data contained within the ERP system, which provided a powerful reality check for users. Those mistakes—say a missing zero—may have originated with an inattentive employee. With the improved reporting system, the finance manager was able to suss out those mistakes more quickly. As a result, finance became a vocal advocate of clean data, and helped to enforce the new enterprise data policies.

Another early project, which aimed to assist admissions, fed current applicants’ data into the data warehouse. Assessing applications—who was applying, how desirable candidates were in comparison to others, who had accepted and so on—had been a matter of looking at outdated information. Through business intelligence reporting, decision makers can see daily changes to the application mix. This has enabled the school to choose more selectively based on the right combination of students they want to admit according to such factors as academic excellence, leadership, diversity of experience, geography, gender and ethnicity.

Create one version of data truth.

Rensselaer created cross-functional groups to establish data accuracy, including teams which created common data definitions. Such activity, no matter how difficult it may be to get agreement, is crucial for the success of a business intelligence project. Kolb lists “one version of the truth” as the top reason why the effort has borne fruit.

Colin White, founder and president of BI Research, agrees. End-user-facing business intelligence tools and dashboards may seem to be the sexier aspects of BI, but data warehouses and the data governance to ensure clean and consistent data are the foundation of successful business intelligence. “Garbage in, garbage out: Unless you’ve got accurate data you don’t get the benefits,” he says. Deploying an enterprise data warehouse is often the one way you can guarantee clean and consistent data.

To ensure that data stays clean, you must put processes in place to make sure people find mistakes and correct them, says Ora Fish, project manager of data warehouse and business intelligence for Rensselaer. The university designated “data stewards” and “data experts” early in the project and made them accountable for the cleanliness of the data. “People are not going to do it out of the goodness of their heart,” says Fish. Functionally, the data stewards are associate vice presidents and are responsible for setting procedures and policies for data management. Data experts are typically senior managers who report to the stewards. The data experts meet to set campuswide data definitions and take responsibility for any necessary cleanup of existing information.

In addition, Rensselaer has other ways to enforce data cleanliness. One way is to define business rules for the data warehouse so that erroneous data is rejected (for example, if an expenditure is submitted against a fund that is not active or if a student is given two acceptances) and an e-mail is sent to the owner to correct it. Another is to mark nonstandard data (such as a grant that is submitted without a code, with permission from a key business user). A third is to have reports and queries analyzed regularly by data experts.

Finally, end users are held accountable for what they enter into the data warehouse. Knowing that, for example, the vice president of enrollment will look at a dashboard of student demographic data and see that some students’ ethnicities aren’t coded is a powerful motivator for that vice president’s staff, notes Fish.

Provide support for new behaviors.

It was clear that such big changes—no more reliance on shadow systems and the move to a new system—needed nurturing to take root, especially in an academic environment where many people pride themselves on individualism and freedom.

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To help ensure success, the data warehouse team collaborated with HR to write goals for using the data warehouse and business intelligence tools into the performance evaluations of some managers.

In addition, executives and faculty were given mandatory training on business intelligence tools, data models and operations. Beyond that, the data warehouse group offered study halls and other support sessions, as well as newsletters filled with tips. Project leaders also set up a web page of BI information, which outlines participants, committees, project presentations and includes user-friendly sections on data warehouse terms and functions.

An A+ for Rensselaer’s Business Intelligence

Rensselaer initially invested $1.2 million in the enterprise data warehouse and business intelligence systems. Operating them costs approximately $537,000 annually.

In return, executives estimate conservatively that the better decision making that comes from improved data analysis saves the university $820,000 annually. Business intelligence has enabled Rensselaer to be more selective in awarding financial aid, resulting in a yearly savings of $500,000. It has also helped optimize expenses. For example, by automating the creation of financial reports, the BI tools have saved $320,000 in staff costs and reduced the time it takes to produce the reports from days to hours. Because financial information is available in near real-time, financial aid and budgets can be more closely monitored, improving budgetary management. And better historic data enables better forecasting.

“They’re making some real strong decisions,” says AMR’s Hagerty. “Which applicants choose and what to reward them—that’s bringing BI to life.” All too often, he points out, what’s missing from business intelligence efforts is moving from insight to action, and therefore failing to exploit BI’s potential.

The Rensselaer team agrees. “True returns can only be achieved when the technology is adapted by the organizations and is penetrated into its processes,” says Fish. That success requires fundamental changes at multiple levels, which is only possible, she says, with executive-level commitment and sponsorship and close collaboration between the IT and the business side.

The BI and data warehouse project “has allowed Rensselaer to significantly improve consistency and just-in-time access to institute information,” concludes Kolb. “This has contributed to improved planning, forecasting and decision-making processes for virtually all university constituencies. The successful implementation of the project illustrates the transformative nature of IT.”