Data-Driven Decision-Making >> Data Pioneers

At the dawning of analytics use in higher education, some schools and vendors are indisputably ahead of the data warehousing and business intelligence pack.

Data PioneersEveryone on your campus needs information, and if your institution is like most schools, you have plenty of it to share. But which types of data warehousing and business intelligence systems you choose, and how accessible, usable, and meaningful those tools make all of that information, remain the big questions for many technologists and administrators. Fortunately, there are always those brave pioneers, willing to move forward into uncharted territory. It’s from those who go first that the rest of us take our cues.

Right now, say those watching DW and BI rollouts in higher ed, a handful of institutions have truly cutting-edge programs in place or in process (see “It’s a Catch-Up Game,” the first of this two-part look at datadriven decision-making, which ran in our August issue). More often, though, colleges and universities are in the early stages of implementing such projects (see “Analytics in Higher Ed: We’re Just Getting Started”).

Yet, the importance of making good choices for your academic analytics solution can hardly be overstated. With sophisticated financial and ERP systems in place at most schools, this is one of IT’s next frontiers. The choices you make now will do much to enhance your competitive edge down the road, allowing you to accurately gauge the success of recruitment efforts, pinpoint sources of research dollars, meet compliance regulations, report to outside agencies on exactly where dollars are going, and much more. With that in mind, let’s take a look at three higher ed institutions whose administrators have made very different choices in approaches to today’s data warehousing and business intelligence challenges.

The College of St. Scholastica: Breaking Down Silos via Data Sharing

An operational data store, or ODS, is sometimes introduced when an institution first begins to work toward implementing a full-blown data warehouse. The ODS can be used to collect data from legacy systems, as well as short-term data from multiple sources—say, finance, financial aid, and admissions. A data store is less complex than a data warehouse in its structure, and in the amount and type of data it contains. Generally, it’s updated frequently, and can be easily queried by users.

At The College of St. Scholastica, an independent private school in northeastern Minnesota, CIO Lynne Hamre is using a data store product from SunGard Higher Education called Operational Data Store. Within the year, she plans to move to a fullfledged data warehouse product, the Enterprise Data Warehouse, also from SunGard. The college selected the two SunGard products in 2004 and has had the data store working in production for about a year.

Moving away from data marts. Part of what pushed St. Scholastica to choose a SunGard product was the fact that the school has been running SunGard’s SCT Banner ERP system for 10 years. To access the data in the ERP system, the IT department had built a number of departmental data marts in-house, using Microsoft Access. (A data mart is similar to a data warehouse, but generally applies to a single department.) The data marts at St. Scholastica worked well for a number of years, Hamre says, but as they grew and proliferated, “we were having trouble keeping things consistent and reliable in the Access databases.”

Because of the numerous silos of information in the data marts, Hamre says, the IT department was struggling to meet the data demands of users. “People wanted historical data and trend analysis. They wanted to manipulate the data by themselves, and they wanted data from multiple campuses.” All of that was difficult or impossible with individual data marts. St. Scholastica first considered developing its own next-generation data mart using the Microsoft .NET platform. But the school eventually decided that the Sun- Gard products would be more costeffective, especially considering longterm maintenance.

Data Pioneers

WITH DISPARATE data marts,
The College of St. Scholastica
was
having trouble keeping data consistent
and reliable, and struggling to meet the
data demands of users.

Owning the ‘store.’ Users of the Sun- Gard data store come from various functional areas of the college (including admissions, the Registrar’s office, financial aid, and budgeting), and are familiar with the ERP system and the data mart. Their sophisticated manipulation of data was helpful when the SunGard ODS was implemented. Users have “really adopted the system as their own,” Hamre says. “They have a lot of ownership in it. They like the ability to get in and get the information they need, when they need it,” rather than having to request a report from IT. She points to the example of a VP and two associate VPs using the system to track enrollment for an accelerated eight-week program for non-traditional students: “They worked with one of our developers to develop a report that they can run on demand,” she explains. “With that report, they can track current enrollment in the program at any time, and from anywhere, without any contact with the IT group.”

Pushing and sharing. One of the college’s long-term goals is to push decisions out to appropriate decision-makers, Hamre says, and the SunGard ODS tool gives them the data to do that. “I’ve seen people who [IT] never used to work with, using this tool,” she says. “Those new users, drawn to the product’s ease of use, now use it for a range of tasks, from routine to sophisticated.”

But perhaps the biggest benefit of the system thus far is the ability to share data across departments, something the data marts didn’t permit. “We can break down those silos,” Hamre says, explaining that now, for instance, the Admissions office can share a report with Financial Aid, or Payroll can share with Human Resources, “instead of having different reports in different areas, and having to worry [that] people are getting different numbers.”

FACTBOX

A pronounced benefit of any data warehousing system is the ability to share data across departments; something that data marts, for instance, don’t permit. Data silos can thus be broken down: The Admissions office, for instance, can share a report with Financial Aid, or Payroll can share with Human Resources. There’s no more concern that different people are getting different numbers.

USF Health: Standardizing Financial Data

It’s no secret to technologists and administrators alike: The “people challenge” of rolling out a DW/BI solution can easily outweigh the technical aspects. Typically, IT staff and business users must work together closely to plan the project and determine data ownership and management rights; a DW or BI project spans many departments and calls for high levels of coordination, communication and agreement.

Standardizing and integrating. At the University of South Florida Health, the healthcare school within the University of South Florida, Jim McKenzie, assistant vice president of technology and CIO, has wrestled with plenty of people issues during an 18-month rollout of a data warehouse and Business Objects’ Enterprise business intelligence platform. USF Health’s challenge: within that 18-month timeframe, to find and implement a solution that could standardize the financial data in many departmental silos, often stored in Microsoft Access or Excel. The 8,500-user school needed to integrate with the main USF campus, which was running a Cognos data warehouse. It also needed a product that could seamlessly integrate with Access and Excel, as well as enterprise- level financial systems like SCT Banner, Oracle PeopleSoft, and in-house systems. McKenzie reports that to generate a meaningful report with the previous system required a business person to log into each of those systems separately, cut and paste into Excel, and then build a report. “It could take a week to generate a report that our current technology can generate in 15 seconds.”

A data warehouse was the obvious choice for a data storage structure, McKenzie adds, and securing a solid BI tool to analyze the data was key. “Just having people dump all the data onto large, fast servers was not going to address the business problems,” he explains, adding that the main university, which is huge and complex, has 41 formal IT organizations, “and an equal number of shadow IT organizations.” The financial system at USF required USF Health to pull financial data from several main campus systems, along with clinical financial systems and its own databases. Once the system is fully completed, data will be pulled from at least 22 other major enterprise-level systems.

FROM BUSINESS INTELLIGENCE TO ACADEMIC ANALYTICS

FROM DAVE WELLS, director of education, The Data Warehousing Institute:

BI best practices can offer you real guidance as you set out to launch a BI program for your campus. Several are described in TDWI’s April 10, 2003, FlashPoint newsletter, “Ten Best Practices for Business Intelligence and Data Warehousing,” but because they were originally devised for the non-academic workplace, let’s reconsider them in the context of academic analytics.

  1. Understand the drivers. How do external forces—political, economic, social, and technological— affect higher education?
  2. Measure results. How should you measure when much of value is realized as intangibles?
  3. Make it a business initiative. D'es this become “make it an academic/administrative partnership?”
  4. Practice ‘user first’ design. How complex should design be when academic users are more advanced than the typical corporate user?
  5. Create new value. Value is important, but how do you define and measure it?
  6. Attend to human impacts. Do sophisticated users alleviate or amplify the impacts?
  7. Focus on information and analytics. Without standard performance metrics, where do you start?
  8. Practice active data stewardship. How challenging (and how achievable) is this in an institutional culture?
  9. Manage BI as a long-term investment. Again, d'es institutional culture aid or aggravate?
  10. Reach out with BI/DW solutions. How difficult is this considering the complex organizational structures common in higher education?

Validation for compliance. The new system also addresses compliance laws such as HIPAA, Sarbanes-Oxley, and Gramm-Leach-Bliley, which mandate tighter data security and controls. McKenzie points out, “Locating our data in a single highly secured system like our data warehouse, and using highend tools like the Business Objects applications, we can drill down into data to see where it came from and thus be able to validate any single element in the data warehouse. The importance of this reverse data lookup cannot be stressed enough.”

Data ownership. Not surprisingly for such a large, cross-departmental project, data ownership issues cropped up throughout the project. With so many departments and organizations involved, there were considerations about who owned the data and would control it. McKenzie says that in addressing those issues, which are common in data warehousing and BI projects, he found the role of outside consultants invaluable. Because they carried no baggage nor represented alignment to a particular department, he says, consultants were better able to request data from various departments, and work across organizational boundaries. “Consultants avoided political battles,” he says. “They could make low-level requests, and they didn’t seem threatening.”

FACTBOX

For compliance laws such as HIPAA, Sarbanes-Oxley, and Gramm- Leach-Bliley (which mandate tighter data security and controls), locating data in a single highly secured system, and using high-end BI tools, allows users to drill down into data to see where it came from and thus be able to validate any single element in the data warehouse. Such reverse data lookup is crucial.

User resistance. People issues cropped up in other areas as well. The user-friendly interface of the Business Objects solution was a key consideration in its purchase, McKenzie says. But even after the rollout, users weren’t eager to embrace a new system. “People have been terrified of the system,” he notes. The biggest issue: Users feared that giving up their complex departmental Excel spreadsheets meant a loss of control. McKenzie says that as CIO, “one of my biggest roles has been to go around and assure people that they were now going to run a high-level application.” Aided by Business office users, he says, “I did a lot of handholding and encouraging.”

ANALYTICS IN HIGHER ED: WE’RE JUST GETTING STARTED

HIGHER EDUCATION IS in the early stages of a much more extensive use of tools and technologies for data analysis. That’s according to a December 2005 research report from the Educause Center for Applied Research.

In the survey, researchers tabulated responses from 380 campus executives (primarily CIOs) at institutions across the US and Canada, representing both public and private institutions and a range of sizes and budgets.

According to Phil Goldstein, an ECAR fellow and author of the report, the growing use of analytics will be driven by factors including increased competition for students, and growing demand for more sophisticated data from both accrediting bodies and state government.

The report uses the term “academic analytics” rather than business intelligence, but essentially addresses a broad definition of BI: As the report defines it, that definition is “the intersection of technology, information, management culture, and the application of information to manage the enterprise.”

According to Goldstein, “We’re at the beginning of the analytical story in higher education. Among the institutions that we surveyed, most plan to make upgrades in their capacities in the next two to three years, and adopt data warehouses and data marts and other analytical tools more ubiquitously. But we’re still at the beginning of that.”

The growth curve toward a growing use of analytics makes sense, Goldstein says, in light of higher education’s recent progress in moving to sophisticated enterprise resource planning (ERP) systems. With those now largely in place, institutions have strong transaction systems and are ready to add analytical tools on top for more advanced functions. The report’s findings include:

  • The most sophisticated users in higher ed typically are those with already strong analytical backgrounds, in the offices focused on budget and planning, institutional research, admissions, and in the Registrar’s office. “Those are parts of the institution that have always had people who are analytically strong,” Goldstein explains. In many ways, he adds, institutions are “just providing a new toolkit to people, so they can continue their role as deep analysts of information.”
  • Nearly half of the schools who responded to the survey are still doing most of their analytics by extracting data from back-end transaction systems, typically either ERP systems or pre-ERP systems, using tools such as Microsoft Excel or analytical software from SPSS, Goldstein says. The other half are implementing or have implemented data marts or data warehouses.
  • Enrollment management is the area in which both the use and impact of analytics is most strategic and beneficial—and that includes ways to improve both student retention and enrollment results. “It stands to reason that institutions would benefit from using these tools in areas that already have a lot of data about what students are doing,” Goldstein explains. “If you can start mining that data, you benefit. Certainly, focusing first on major revenue streams of the institution makes a lot of sense.”
  • Schools without sophisticated analytical tools could, and often were, performing data analysis anyway, Goldstein says, but “they were doing it the hard way.” That might mean, for instance, that they were pulling data out of one or more transaction systems, and using Excel or other so-called “shadow systems” to manipulate it. “While specific analytical tools make it easier to perform the kinds of analytics we’re talking about, they’re not the only way to get there,” Goldstein concludes. More survey information, which is available for purchase, can be found here.

Get help. Another important rollout lesson: Don’t try to go it alone. “I’m very much a CIO who d'esn’t believe in hiring consultants,” McKenzie says. “But in this case, it quickly became clear that no IT group at the university could roll this out alone.” The project was simply too massive and required too many highly specialized skills, he admits. “We needed [the outside consultants] and Business Objects to pull it off. We learned that the scope of a data warehouse project is one that most universities’ IT staffs cannot take on by themselves.” In fact, McKenzie says the scope was so large, and the system access issues so political, that having an expert third-party consulting firm handling much of the work made a huge difference.

Data Pioneers

GIVING FACULTY, staff, and deans easy
access to data was a key business intelligence
driver for administrators at Towson University.

Towson University: Out-of-the-Box Solution to the Rescue

One challenge with DW and BI solutions is that there are few or no existing data models for universities to use in implementing a data warehouse. In some industries, such as banking or insurance, models have been developed that greatly simplify the structuring and development of a data warehouse.

OOTB with partners. For institutions without the staff and funding to build their own models from scratch, iStrategy’s HigherEd Analytics Suite offers a solution. iStrategy takes a different tack by delivering a largely out-of-the-box data warehouse application specifically tailored to higher ed. Instead of requiring that the user create data models, the iStrategy application comes with a prebuilt data model, which in turn is mapped to existing ERP systems. iStrategy integrates with Oracle PeopleSoft and Datatel, and the company says that plans are in the works for integrations with Campus Management and SunGard Higher Education. The company also partners directly with Datatel and Campus Management, both of which offer iStrategy to customers.

Unifying reporting. For Baltimore’s Towson University (with 13,000 undergraduates and 5,000 graduate students, the second-largest public liberal arts university in Maryland), implementing iStrategy a year ago to work with the school’s Oracle PeopleSoft suite proved to be a good solution. In the fall of 2002, Towson had moved from a legacy system to Oracle PeopleSoft 8.0, according to Phil Adams, assistant director of information systems at the university. Before PeopleSoft, staff members needing reports had used Business Objects’ Crystal Reports reporting tool to create their own reports as needed from data in the legacy system.

With PeopleSoft in place, Adams says, users were sometimes frustrated by their lack of reporting capability and end-user tools, and the need to rely on the IT staff for reports and queries. That was a key driver in the choice of iStrategy: giving faculty, staff, and deans easy access to data. The school also wanted a single version of information—and the same answer every time the data was queried. “We had Crystal Reports, Access, [Frontier] ODBC Query, and 16,000 PeopleSoft tables,” Adams says. “Users could ask the same question and get two, three, four different answers.”

Phil Adams

"The ROI on an out-of-the-box DW/BI
product was a real deal-maker for us."

— PHIL ADAMS, ASSISTANT DIRECTOR OF
INFORMATION SYSTEMS, TOWSON UNIVERSITY

Consider ROI. Towson originally looked at several large data warehousing products. But a major advantage with iStrategy, Adams says, was that it wasn’t only a data warehouse product; it also had a very simple user interface for reporting and analytics. A real deal-maker for the IT exec was the return on investment he saw in the out-of-the-box product. “From an ROI standpoint, the time, resources, and staffing that we put into this is very minimal,” he reports.

After he’d rolled out iStrategy, Adams attended the Higher Education Data Warehousing Forum, begun in 2003 and held this year on the campus of the University of Illinois, Urbana-Champaign. (The conference Adams attended was held in April 2005 at Northwestern University in Evanston, Illinois) He attended presentations in which university technologists discussed spending several years and millions of dollars on data warehousing and business intelligence solutions. “When I heard the money projections, [estimates] were in the millions,” Adams recalls. “I couldn’t have justified it at Towson.”

With its prebuilt reports, and the manner in which the warehouse and reporting tools are packaged together, for the most part iStrategy was up and running at Towson within a week, Adams says. “I realized then that it was a very powerful tool. Now, looking back, I just feel fortunate we found the right solution for our needs.”

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