Data-Driven Decision-Making >> Data Pioneers
- By Linda L. Briggs
- 10/27/06
At the dawning of analytics use in higher education, some schools and vendors are indisputably ahead of the data warehousing and business intelligence pack.
Everyone 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.
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, TDWI:
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.
- Understand the drivers. How do external forces—political, economic, social, and technological—
affect higher education?
- Measure results. How should you measure when much of value is realized as intangibles?
- Make it a business initiative. D'es this become “make it an academic/administrative
partnership?”
- Practice ‘user first’ design. How complex should design be when academic users are
more advanced than the typical corporate user?
- Create new value. Value is important, but how do you define and measure it?
- Attend to human impacts. Do sophisticated users alleviate or amplify the impacts?
- Focus on information and analytics. Without standard performance metrics, where do
you start?
- Practice active data stewardship. How challenging (and how achievable) is this in an
institutional culture?
- Manage BI as a long-term investment. Again, d'es institutional culture aid or aggravate?
- 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.
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.”
"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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