Data-Driven Decision-Making: It’s a Catch-Up Game

Feeling the pressing need for sophisticated business intelligence solutions and processes, higher ed looks for models—wherever they exist.

Data-Driven Decision-MakingGood data is powerful. At Rensselaer Polytechnic Institute in upstate New York, Data Warehouse Program Manager Ora Fish was astounded when her data warehousing team was roundly applauded for presenting data on graduate admissions—data derived from the data warehouse—to top-level officials. “The cabinet had never had [solid] numbers before,” Fish realized. Suddenly, she says, “they didn’t have to argue about whose numbers were right. They could talk about the issues: how we’re doing in graduate admissions.”

Having an abundance of data residing in individual silos across campus, but little decision-ready information, is a typical scenario at many institutions. “We’ve always said the campus was ‘data-rich,’ even when we had manual collection of that data. But we couldn’t get access to it,” explains Michael Zastrocky, a VP with research firm Gartner. Zastrocky has more than 30 years of experience in higher education, and has followed the campus data situation for decades. “Then we moved to relational databases, and people said, ‘We’re still having trouble getting access to the right information.’ That’s been a major issue on campus.”

One problem: The terms data warehousing (DW) and business intelligence (BI) refer to very different things, although the two often go hand-in-hand. Data warehousing describes an architecture for storing data; business intelligence involves analytics run against that data in order to discover patterns and glean information to analyze past performance and predict future events. Typically, a data warehouse is constructed first, in itself a complex process that requires significant planning and institution-wide coordination. BI tools are then used on the warehoused data to produce reports, dashboard displays, and other windows into the data.

While many higher ed institutions now have extensive data residing in enterprise resource planning (ERP) solutions, the typical US university owns what only amounts to “an infant data warehouse,” says David Wells, director of education with The Data Warehousing Institute. While ERP solutions can offer detailed reports, and ERP vendors are moving to include more analytics solutions with their products, they can’t offer the kinds of nuanced ways to slice, dice, and analyze data that a data-warehouse- and-BI solution can. Yet higher ed lags behind private industry in its adoption of BI. “A few of the leaders in higher ed have decent data warehouses,” Wells says, but points out that most schools are still focused simply on maturing their data warehouse and data integration capabilities, “with analytics farther out on the horizon.” There are exceptions, however.

RPI and UI: Institutions Leading the Charge

Rensselaer Polytechnic Institute, for one, began to roll out a data warehousing solution in 2001, when it decided that the school’s new ERP solution couldn’t provide all the analytics and reporting needed. The warehouse was constructed across a three-year timeline; RPI also selected a series of BI tools from Brio Software (later acquired by Hyperion Solutions; www.hyperion.com) for analytics. RPI’s DW and BI solution is one of the most advanced in US higher ed, and Fish often speaks about it at conferences.

Several years ago at the University of Illinois (which, along with RPI, TDWI’s Wells cites as a leader in data warehousing), administrators decided to build an enterprise-wide data warehouse. At the same time, the university moved to a new ERP system, replacing a number of legacy data systems across UI’s three campuses. The new, institution-wide system raised the question: How to get meaningful data back out of the ERP system?

To meet this need, over a several-year process UI has constructed a mature data warehouse that includes all the core business data from the ERP. In addition, the university uses enterprise decision support tools from Business Objects to access and analyze warehoused data.

UI’s case is different from most others in higher ed not only because the initiative is so large and advanced, but because the institution’s data warehouse is truly institution-wide—a feat difficult to accomplish in higher education (see “The Challenge of BI in Higher Ed”). Still, UI’s Director of Data Warehousing Andrea Ballinger strongly suggests that the university’s concurrent rollout of the ERP system and the data warehouse is not an approach she would recommend to other schools. In fact, she says, trying to do both at the same time was perhaps the biggest challenge the school faced in its data warehouse deployment. But the deployment was also challenging, she adds, “because we were trying to do something that to our knowledge, no other university had done before. There are many data warehouses out there, but we knew of no other in higher education that started from zero, with a focus on enterprise-wide data warehousing. We wanted to do it right from the beginning, and end up with one fully integrated enterprise-wide warehouse.”

Bridging IT and Business

Ora Fish

"The key to a successful BI program is really about the successful marriage between IT and the business side of the institution."

Ora Fish, Rensselaer Polytechnic Institute

Key to building a data warehouse is bridging the long-standing gap between IT and business. More so than with many other technology solutions to business problems, data warehousing tests the bridges between IT and the rest of the campus.

At UI, for instance, the bridge between IT and business is largely handled by three “functional area coordinators.” These subject-matter experts focus on three key data areas: students, finance, and human resources. They act as liaisons between their areas of specialty and the data warehousing team. According to Aaron Walz, business architect for the Decision Support team, these individuals “translate what the customers are saying, putting that into a language that the technical staff can understand.” Yes, any significant IT initiative needs the business side of the house on board. But in such a case, says RPI’s Fish, “IT really needs to understand what questions business needs to ask—and at a much higher level, because now you’re talking analytics.”

That means that the people who know how to produce reports—often highly technical IT staff within the DW group— need a deep and broad understanding of what drives the institute. In the world of data warehousing, Fish says, “it’s really about the successful marriage between IT and the business side. That’s the key to a successful BI program.” And, don’t imagine that creating a data warehouse is a project with an end date, or that it’s a quick fix for internal reporting problems, she adds. Maintaining a data warehouse is an ongoing process that requires constant attention, user training, and marketing. “It’s not a project with beginning and end dates,” Fish explains. “That’s something that we’ve learned here: It’s a new service that we’re providing the campus.”

Even at UI, with its mature data warehouse, the process is ongoing, says Walz. “Ideally, in building an enterprise data warehouse, you don’t stop there; you build data marts and other structures to give people simplified ways to get at the data. We’ve started on that process, but it’s by no means complete.”

Build It and They Will Come?

Part of the data warehouse challenge is the constant effort to explain its usefulness to users. “‘Build it and they will come’ is a very ugly myth,” Walz says bluntly. When the university erected its new systemwide ERP structure, some of the standard mainframe reports were duplicated, but not all. Even so, Walz says, users “still had to be convinced that coming to the warehouse was worth their time and that they needed it. You have to provide something that is very targeted to what they’re trying to do, so they can see that it’s helpful.”

Rensselaer Polytechnic Institute

Both Rensselaer Polytechnic Institute (above)
and the University of Illinois are leaders in
academic data warehousing, but their business
intelligence challenges were markedly different.

His group puts ongoing effort into promoting the warehouse, including training sessions and periodic messages to everyone with a warehouse account, encouraging them to use it. The Decision Support team also analyzes who’s using the warehouse and how, and uses that information to drive marketing efforts. “We also make presentations to different groups on campus, such as Human Resources and business managers. We tell them, ‘Hey, here’s what you can do with the data warehouse.’” Walz says. “If it takes too much time, or if it’s too complicated to get access, they just don’t use it.”

Measuring the Return

Quantifying a return on investment (ROI) for a data warehouse or BI investment can be challenging in the private sector; in the complex world of a not-for-profit public institution like a university, it can be close to impossible. At UI, Ballinger says she is just beginning a project to collect returnon- value (ROV) figures that place a dollar amount on the school’s hefty investment in its data warehouse. (ROV attempts to look beyond ROI and take a broader, systemwide view of the contribution of IT.) In looking for measurable returns, Ballinger is seeking answers to this basic question: What has access to the data warehouse done to improve the efficiency and effectiveness of the units using it?

What Can You Do with Data Warehousing and BI?

WHAT SORTS OF DATA (that you’re not getting now from ERP or elsewhere in IT) can a BI solution provide? Here are three examples of of nuanced information that a DW/BI solution can produce:

  • Is our online ed competing with our traditional ed? The University of Illinois is spread across three campuses (Chicago, Springfield, and Urbana-Champaign). Springfield focuses on online and distance ed for liberal arts. When the Springfield provost asked, “Are we cannibalizing our existing business by offering courses online?” the campus used information in the warehouse to create reports that found they weren’t— students were supplementing their schedules with additional hours of online courses.
  • How attractive are we? One way that Rensselaer Polytechnic Institute (NY) uses its data warehouse is to track admissions closely by answering questions like: “Are we attracting the types of students we want? How well are we translating student inquiries into applications? How about converting those applications into admitting students?” When a new strategy for attracting students was introduced recently, the results were immediately available on a daily basis from the data warehouse via analytic dashboards.
  • Researching research. Rensselaer is a large research university, so it finds the data warehouse useful for monitoring research, with questions like: “Where, exactly, is our research money coming from? Who’s performing the research—are they working through the university or the research center?” Unlike in the past, “It’s very easy now to slice and dice, and everyone agrees on the numbers,” says RPI Data Warehouse Program Manager Ora Fish.

For example, a purchasing agent renegotiating a licensing plan based on bulk purchases might be able to save money, based on data revealing usage across the university rather than in one department. Ballinger says that her plan is to work at gradually gathering those sorts of dollar amounts into a cohesive picture. “We have a boatload of data about who’s using the data warehouse when,” she says, “but we don’t know what they’re doing differently from when they didn’t have the warehouse.” Because UI’s data warehousing project is such a large effort, Ballinger says it’s critical that she have that data in order to continue to make the case (or not) for the data warehouse.

Show Results Early

Setting up a data warehouse and BI program is a large and expensive undertaking that will require plenty of teamwork, meetings, and time from those campus “customers” who will be using the system. It’s therefore important to show results as quickly as possible. “We demonstrated the value immediately; within six months,” Fish says. “That’s important; you need to show payback in a relatively short length of time.”

That’s why Fish recommends that schools build an analytical dashboard along with the warehouse. Dashboards are tools that focus on specific metrics within a project, in order to give a quick, graphical assessment. By giving the leadership that sort of instant, high-level view of various areas, she says, RPI was able to demonstrate a nearly immediate return to top management. It was hugely satisfying, she adds, to pull data in from the warehouse and provide the first looks at an analytical dashboard. “People’s eyes were just popping out,” she recalls. “‘Oh, is that what we’re doing?’ they said. ‘I thought we were going up, but the numbers don’t support that!’”

The Challenge of BI in Higher Ed

LAUNCHING A DATA WAREHOUSING and business intelligence initiative is tough enough in private industry. But higher ed institutions face some extra hurdles in rolling out a data warehouse, according to David Wells, director of education with The Data Warehousing Institute, a training and educational institute for business and IT professionals. Among the challenges Wells and others cite:

  • Gathering consensus. Unlike a typical large business, a university isn’t a single enterprise. It can be politically difficult if not impossible to get various entities across campuses to agree on basic issues, such as what the end-purpose of the warehouse is, what data to share, and who should be in charge. “I think it’s a more challenging business case to make,” Wells says, partly because institutions typically divide immediately at the top into academic and administrative sectors. Because a data warehouse has to integrate across organizational boundaries, he says, the arguments that might sell administrators usually don’t resonate with the academic community, and vice versa. “It takes a real believer driving from the top to make it happen.”
  • Define, define. Because of the lack of a centralized, top-down structure, issues like defining the elements in the warehouse can be surprisingly difficult. “A major challenge is bringing the campus together in common definitions,” says Ora Fish, data warehouse program manager at Rensselaer Polytechnic Institute in Troy, New York. “Take a question like, ‘How many faculty do we have?’ It sounds very straightforward. Well, you have to define what faculty is, and then the answer depends upon who is counting.” The challenge with a data warehouse, Fish says, is to get everyone to agree on a single definition—in this case, for instance, a definition of faculty—then set up attributes that allow the data to be viewed from a variety of angles and nuances.
  • Hiding in the ‘shadows.’ Another issue that’s not unique to higher ed but may be exacerbated there, says Wells: the degree to which the operational data exists in “shadow systems.” Because colleges and universities have a strong tendency to build departmental systems that don’t depend on central computing, the same kinds of data, in entirely different forms, can be found in pockets across a typical university. “The data can have varying degrees of quality and completeness,” Wells says. “That can be a challenge.”
  • America’s top model? Unlike business sectors such as insurance, finance, or health care, where established models exist for creating a data warehouse, there’s no such model in higher education. “There don’t seem to be any good industry models for higher ed,” says Wells—perhaps, he suggests, because higher education in general lags behind corporate culture in embracing data warehousing and BI. In describing her school’s highly complex data warehousing rollout, Andrea Ballinger, director of data warehousing at the University of Illinois, confirms, “We couldn’t find models—and we couldn’t find anything in the corporate world that would fit, either.”
  • Use what you choose. Colleges typically decide issues by consensus and committee—not the easiest way to build cross-departmental, institution-wide solutions. Also, mandating use of a single product can be tough in a climate where academic freedom rules. The University of Illinois addresses this by offering a variety of tools for accessing the data warehouse, so that no one is forced to use a specific product.

On the other hand, higher education has advantages that some publicly traded companies, hounded by quarterly earnings reports, might appreciate. To wit: a focus away from pure profitability. “To do anything new and innovative in this type of culture is more challenging, yes,” says RPI’s Fish. “But yet, we’re different—we’ve been here more than 150 years. We’re not driven by short-term profits. We don’t have to make our quarterly numbers. We have a very settled and strong culture.”

Where to Begin?

With departmental information spread across the institution in silos, and with the significant challenges that building a data warehouse represents, the question then becomes one of where to begin. It helps to decide precisely what your “enterprise” will be, say the pros, since at a large university, that can be many different things. UI’s Walz recommends starting with questions like: “‘What is the enterprise at a university level? Is it the campus? Each college? The university layer that spans all campuses?’ There are different issues that you have to face.” Also, he recommends shifting the initial focus away from the technology and onto the users. “It’s easy to look at technology and tools. Instead, ask: ‘Who are the customers we intend to serve? Are they administrators, or lower-level staff?’ Understand the audience. Do you want people to be able to write their own reports? What needs are you trying to fill? What problem are you trying to solve?”

In UI’s case, the data warehouse was planned from the start to fill the needs of many user types—a solution that Walz says is far more difficult. “Our customers span everyone from administrators to secretaries. Some folks are using the warehouse to write their own reports. Some would prefer reports be handed to them.”

Not surprisingly, such huge endeavors must be driven by senior administrators. “Get buy-in and direction from them,” Fish advises. “Everything else can fall into place, but they have to sell it at the top.”

WEBEXTRA::The BI vendor lowdown is here.

Stay tuned for Part II of this series on data warehousing and BI, coming in November: We’ll look at specific solutions in use at various institutions.

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