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Data Driven Decision Making in Higher Education
“Improving decision making across the campus”
February 2004
TABLE OF CONTENTS
1
The Imperative for Data Driven Decision Making in Higher Education .................... 1
2
The Decision Making Environment in Higher Education ........................................... 2
Demands for Improved Decision Making Capabilities .................................................................2
Challenges for Improving Decision Making Capabilities ..............................................................3
3
A Framework for Improving Decision Making Capabilities ........................................ 5
Five Guiding Principles for Developing Decision Making Capabilities ........................................5
Program Lifecycle Management ....................................................................................................6
4
The Road Ahead ............................................................................................................. 8
Next Steps.......................................................................................................................................8
Conclusion ......................................................................................................................................8
Data Driven Decision Making in Higher Education
1 The Imperative for Data Driven Decision Making in Higher Education
Colleges and universities constantly strive to achieve their institutional missions in the face of
increasing competition and limited resources. They make crucial short- and long-term decisions as they
do so, however critical questions remain:
 Do institutions have necessary facts to make well-informed decisions?
 Are some decisions being made based on intuition because relevant data is unavailable?
 If adequate data and analysis capabilities are not available to inform decision making, what
risks does the institution assume?
Addressing these questions is important for improving decision making across the institution.
How are decisions made today? Often they are based on intuition; someone has experience in an area,
and relies on what they believe is true to make a decision. In other words, no formal data gathering or
analysis is performed which might improve decision making. Sometimes decisions are made when data
is queried and a report is produced, but this can cause problems in that a report is only as good as the
data available, and data can become out-of-date, incomplete or can be difficult to assemble. Often
times when good information is difficult to obtain, it is likely that this information will not be pursued.
Unfortunately, in an environment of increasing accountability and public responsibility, not having solid
data and analysis behind decisions has the potential to place institutions at risk. It also limits the
decision making tools an organization can use, such as scenario planning or the creation of an
institutional or performance scorecard.
With effective data driven decision making capabilities however, higher education administrators and
staff can more accurately identify trends, pinpoint areas that need improvement, engage in scenariobased planning and discuss fact-based decision making options and likely outcomes. These advanced
decision making capabilities benefit an institution not only with respect to long-term planning, but also
in assisting routine decision making that—when acted upon throughout an institution—can add up to
tremendous performance improvements over time.
Many administrators now recognize the importance of having accurate and accessible data in order to
support the decisions that improve day-to-day operations, as well as long-range strategic planning.
These data driven decision making capabilities can be greatly enhanced within institutions through the
use of data warehousing. Data warehousing collects and organizes data from multiple sources so that it
can then be easily analyzed, extracted and used. Because of this, the data warehouse is a core
component for enabling data driven decision making for higher education institutions.
This document provides the first step towards improving data driven planning, decision support and
reporting capabilities at higher education institutions. This is accomplished through a scalable and
comprehensive data warehousing approach. Using this document as a guide, colleges and universities
can evaluate their current capabilities, assess their access to relevant information, and take actions to
dramatically improve data driven decision making capabilities at their institution.
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Data Driven Decision Making in Higher Education
2 The Decision Making Environment in Higher Education
The decision making environment in higher education is very complex. Multiple entities and
stakeholders need reliable access to specific information in order to make sound decisions. Therefore,
improving decision making capabilities means assessing and overcoming these challenges.
Demands for Improved Decision Making Capabilities
Higher education institutions are inundated with demands for information needed to support
administrative, academic, planning, regulatory, legislative, research and operational interests. Varying
constituents at all levels require dynamic views of information and customized analysis capabilities.
Requests for more on-demand, comprehensive capabilities steadily increase as constituents strive to
make informed strategic and day-to-day decisions. The figure below illustrates the complex landscape of
higher education, highlighting various parties who have unique information requirements.
Each member in this community requires access to relevant information in order to guide and support
their decision making. Administrators in particular often have a greater need for information access and
analysis capabilities. The table below expands on several roles within the spectrum of higher education
administration, and provides examples of the types of information that support decision making by
people within those roles:
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Data Driven Decision Making in Higher Education
Administration
President’s Office
Finance
Provost’s Office
Facilities and
Infrastructure
Information
Technology
Information that supports decision making
- Operating performance metrics for departments or divisions
- Financial information (e.g., reports on the use of federal and state funds,
grants, status of the endowment, comparative spending trends)
- Admissions and enrollment statistics
- Budget information (e.g., from the Institutional level down to individual
transactions, comparisons of financial commitments with overall budget,
projections of budget short-falls)
- Endowment funds information (e.g., estimates in the growth of scholarship
and/or endowment funds)
- Spending trend information (e.g., across departments, facilities and
campuses)
- Vendor purchasing information (e.g., spending information to support
strategic sourcing initiatives)
- Enrollment information (e.g., monitoring historic enrollment trends and
contrasting with current projections, projecting how many course sections will
be needed based on specific enrollment figures)
- Staffing information (e.g., estimating early retirements, position vacancies
and number of new hires required)
- Racial and gender information (e.g., comparing racial and gender equity
across all units)
- Facilities utilization information (e.g., information for facilities scheduling,
maintenance scheduling and planning)
- Safety and compliance information (e.g., ensuring all facilities are ADA
compliant)
- Service level information (e.g., reviewing network performance, helpdesk
responsiveness and mean time to resolution)
- Service usage pattern information (e.g., planning for needs in IT environment
and services based on institution needs and usage patterns)
The institution must be able to respond to the short-term, tactical needs of these constituents as well as
meet their long-term, strategic requirements. In the competitive educational landscape, the demands of
these and other groups will only continue to increase, with timeliness, collaborative enablement, data
usability and multi-point access emerging as key factors in improving data driven decision making.
To address increasing demands for improved decision making capabilities, many institutions have
embarked on data warehousing initiatives. However, most institutions face several challenges they must
overcome in order to realize success on a broad scale from these initiatives.
Challenges for Improving Decision Making Capabilities
Does this sound familiar?
A university decides to build a data warehouse with three key outcomes identified: 1) Integrate
disparate silos of information; 2) create an easy-to-use interface; and 3) build a finite set of
queries to enable critical data analysis for achieving the institution’s strategic mission. Two
years later, the university still struggles to align users and administrators with a common vision
for data access. At the same time, increasing requests for ad-hoc queries prevent timely access
to the data by the people who need it the most.
As higher education institutions try to meet the information demands of their constituents, their data
warehousing initiatives can start off with high aspirations, but often never get off of the ground, or fail to
deliver expected services. In order to implement a successful data warehousing initiative, institutions
must first consider and reconcile four critical challenges.
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Data Driven Decision Making in Higher Education
Key Data Warehousing Challenges
Challenge
1. Data required for decision
making is undefined
Explanation
Without clearly understanding the users’ requirements for
information, data sources cannot be identified and utilized for
the users’ analyses and decision making needs.
2. Data is everywhere and
nowhere
When data resides on multiple systems across many different
departments, it’s difficult to increase access and make
intelligent use of that data.
3. Users lack the ability to
conduct a deeper analysis
of data
Institutions are locked into manual, paper-based methods for
reviewing performance, meaning users can’t drill down to
discover cause-and-effect patterns and make decisive
improvement decisions.
4. Demand is high and
resources are low
The demand for decision making information exceeds the
time, money and IT resources available to meet the
requirements.
As a key first step in addressing and overcoming these challenges, institutions should set decision
support goals, and then use those goals to guide their data warehousing initiative. For example, an
institution might use the following goal statement as a guide:
Data warehousing will provide consolidated and customizable views of institutional and
departmental data, structured to optimize analysis, reporting and decision making
capabilities. In support of these objectives, data and information will be extracted from
multiple sources, making available more effective and efficient queries on a scalable and
sustainable platform. Data will be turned into high-quality information to meet analysis,
reporting and decision making requirements at all levels. Interactive content will be
seamlessly delivered to anyone with information access privileges, in any role in the
institution—from deans to department heads, faculty to senior administrators—anytime,
anywhere.
Setting clear goals for future capabilities increases the likelihood that institutions will successfully
implement data warehousing initiatives and reap the benefits of data driven decision making, including:
•
•
•
•
More efficient use of available funds through better understanding of projected expenses and
revenues
Improved utilization of facilities through accurate space planning closely aligned to classroom
needs and growth patterns
Immediate access to institution-wide assessments and surveys, even across distributed
programs or multiple campuses
Improved planning across the institution through the analysis of common, consistent and
relevant data
Data warehousing enables dramatic improvements in decision making capabilities, resulting in many
benefits for an organization. The next steps in addressing data warehousing challenges are to ensure
the development of a scalable, sustainable platform for data warehousing, and a clear roadmap to
follow for the development and implementation of improved decision making capabilities.
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Data Driven Decision Making in Higher Education
3 A Framework for Improving Decision Making Capabilities
Does this sound better?
Through a data warehousing implementation, administrators can quickly realize the productivity
and efficiency gains they envision. They can readily create the initial analysis tools that will allow
users throughout the university to easily access the data that is most important to them for
decision making purposes.
Implementing data driven decision making capabilities is a journey, not an event. Each institution will
have a different starting point and end goal, so a solution framework is important for initiating and
keeping efforts on track. The five guiding principles below and the program lifecycle that follows can
provide the framework that will help institutions plan and implement improved decision making
capabilities.
Five Guiding Principles for Developing Decision Making Capabilities
Institutional data driven decision making initiatives should consider these five guiding principles as part
of their initial planning. Following each of the principles is a description of how Microsoft directly
supports institutions in developing their data driven decision making solutions.
1. Provide an enterprise-class data infrastructure: Institutions need to understand and prioritize the
demands made by all of their constituents. This includes not only data access and system
responsiveness, but also the stability and security within the infrastructure to operate effectively,
efficiently and securely.
Microsoft Enablement: Microsoft offers an information infrastructure that is an essential
foundation for any data driven decision making solution. The Microsoft Windows® family of
servers has been engineered to meet demanding requirements for availability and reliability.
Built on this robust foundation, a data driven decision making solution will be able to handle
large and complex data warehousing applications that are an integral part of comprehensive
data driven decision making initiative.
2. Design a comprehensive and scalable solution: Higher education institutions need the flexibility of
building out their data driven decision making capabilities as budget and resources allow. The solution
should be designed to easily provide increased capability as data sources expand and user needs
evolve.
Microsoft Enablement: Microsoft offers a robust database platform with Microsoft SQL
Server™. Microsoft SQL Server offers several key components, including:
 A relational database for storing data from across the institution
 Software to extract, transform and load data from existing systems
 An Online Analytical Processing (OLAP) engine for analyzing data against multiple
dimensions that map to specific business rules
 Data-mining capabilities that allow users to search for hidden trends and patterns
 Graphical administrative interfaces that provide a consistent view of the information to
all users
 Tools for creating, managing and delivering reports in both paper and interactive
formats across an institution.
3. Utilize familiar tools: With limited IT resources to support ongoing initiatives, institutions should
design a data warehousing solution that is easy to maintain, requiring minimal user training and ongoing support.
Microsoft Enablement: The Microsoft Office® System provides familiar tools, applications,
solutions and technologies for accessing, analyzing, visualizing, and reporting data from
virtually any source. The tight integration of products and technologies—including Microsoft
Office Excel®, Microsoft Office SharePoint™ Portal Server, Microsoft Office Visio®, Microsoft
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Data Driven Decision Making in Higher Education
Data Analyzer and Microsoft Office Web Components—enables the Microsoft Office System to
deliver a rich set of applications accessible to parties requiring information for decision making
across the institution.
4. Create true closed-loop analysis capability: Closed-loop analysis (CLA) is a process intended to set
improvement goals, monitor progress, assess impact or effectiveness and realign objectives as
required. It becomes a real-time process when data is continuously collected and evaluated. Decision
making capabilities can be greatly enhanced with closed-loop analysis processes and enabling tools.
Microsoft Enablement: OLAP Actions, a new feature of SQL Server Analysis Services, extends
the power of analysis with functionality that allows decision makers to automatically drive
processes based on the results of their analyses.
5. Design a maintainable and sustainable solution: Decision making information requirements will
evolve over time as users become accustomed to making use of information to improve administrative
performance. These new information requirements can place additional demands on an institution’s
information technology resources, and institutions must be prepared to sustain their solution.
Microsoft Enablement: Microsoft offers a cost-effective integration approach to tap into
multiple data sources. With the latest release of Microsoft BizTalk® Server (BTS), Microsoft is
extending its ability to support industry standards in higher education. Institutions will be able
to reduce the time and costs of integration projects with new BTS platform functionality and
open industry standards support.
With these five guiding principles in mind, institutions should consider a program lifecycle management
framework as they move towards implementation.
Program Lifecycle Management
As data and decision making needs change, institutions will constantly cycle through the program
lifecycle stages. Successful management of each stage will determine their level of success. The
lifecycle stages and supporting activities are highlighted below:
Assess current capabilities and define operational needs
During this stage, the information requirements of each constituent are determined and prioritized. This
includes not only the data access and analysis requirements, but also the system performance
requirements. In addition, the gaps between the current and desired capabilities are determined. The
effort and activities required to close the gaps are captured in a master program plan, which most likely
will result in “waved” releases of increased capability.
Design data and system architecture
The data, system (hardware, software and networking) and security components are architected and the
total solution is designed. The design follows a set of standards defined to reduce overall maintenance
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Data Driven Decision Making in Higher Education
costs and accelerate future improvement initiatives. During subsequent capability improvement
projects, institutions need only refine their standards, not redefine. During this stage, proof-of-concept
testing can greatly enhance the ultimate capabilities and potential for success of the project.
Develop solution
During this stage, the individual components are developed, configured and tested. This includes
individual components, as well as system-level and stress testing. Integration and legacy interfaces are
also created and tested. Security procedures should be well exercised after all other testing is complete
and before progressing to the next stage.
Implement
The system is taken “live.” A phased rollout is recommended to bring increased volume on the system in
a steady and controlled manner. Required training and support is coordinated.
Evolve
Ongoing maintenance and support begins. In addition, feedback from constituents is captured and
retained. Finally, lessons learned during the previous stages of the lifecycle are incorporated into future
upgrades and waved releases in the master implementation plan.
The following illustrates the results of a successful data warehousing initiative:
A Successful Scenario
A college has multiple constituents with varying and immediate needs, but with limited
resources to support their data warehousing effort. The college must effectively prioritize and
focus its efforts to areas that will provide the greatest impact. It chooses to focus initially on the
two areas with the greatest demand for dynamic reporting and analysis:
1) External Performance - Enable an institutional scorecard that provides a single view of
progress against the institution’s mission and goals. The president, provost, deans and
department heads each have the ability to drill down on performance metrics and trends to
develop fact-based presentations on their progress in meeting goals and recommendations.
2) Financial Analysis - Consolidate information from disparate financial systems that can
provide timely and accurate budget information from the institutional level down to
departmental transactions. Financial planners have the ability to conduct true “what-if”
analyses with visibility and access to information which allows them to spot both high-level
trends and the causal factors.
A solution for these two areas can be built on a common platform of available packages that is
both scalable and portable, which enables the solution to be replicated and rapidly rolled out
across all offices and campuses. Such a solution can also provide a template to leverage in the
next implementation.
By considering the five guiding principles and the program lifecycle stages, institutions can better grasp
the journey that lies ahead in developing their data driven decision making capabilities. Microsoft has
the resources and solutions to support institutions throughout the various stages of their entire data
driven decision making journey.
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Data Driven Decision Making in Higher Education
4 The Road Ahead
Next Steps
Institutions that are considering a data warehousing initiative to improve decision making capabilities
have a number of options to explore. These resources can provide an effective strategy with which to
align the growing needs of the institution with the appropriate solution.
Microsoft QuickStart Sessions: Microsoft QuickStart Sessions are designed to help education
institutions with expert technical services. Specific QuickStart services include low-cost Architecture
Design Sessions and Proof of Concept Workshops.
Architecture Design Sessions: This session is a two- to three-day intensive workshop designed to
help CIOs and their staff define or refresh the vision and architecture for their data warehouse.
During the session, Microsoft experts assist in outlining the various systems that are available, and
work collaboratively with the team to define the overall project vision and a potential pilot project.
Proof of Concept Workshops: This is a two- to three-week process designed to develop a working
implementation of a solution with limited functionality. The workshop is an ideal method to
minimize risk and establish a foundation for a full solution implementation.
To continue the solution discovery process and learn more about Microsoft’s efforts to improve data
driven decision making capabilities in the higher education environment, visit Microsoft’s Web site at
www.microsoft.com/education
If you would like to identify a Microsoft partner to help you with your data warehousing initiative, please
visit http://directory.microsoft.com/resourcedirectory/Solutions.aspx
Conclusion
The importance of accurate, relevant and accessible data to support short and long-term decision
making can not be overstated. Higher education institutions can dramatically improve decision making
capabilities by implementing successful data warehousing initiatives that address identifiable
challenges, set attainable goals and follow the framework of guiding principles and program lifecycle
management. With the additional guidance and resources from Microsoft, colleges and universities can
confidently start down the path to improved data driven decision making capabilities at their institution.
The information contained in this document represents the current view of Microsoft Corporation on the
issues discussed as of the date of publication. Because Microsoft must respond to changing market
conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot
guarantee the accuracy of any information presented after the date of publication.
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