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Academic Analytics – Minnesota Commits to a Strategy

Academic Analytics

The University of Minnesota has committed itself to information-based decision-making, strategic planning, and continuous improvement. A key component of these efforts is getting the right information to the right people at the right time. The University contains pockets of excellence in measurement, analysis, and reporting, but does not currently have an institution-wide academic analytics strategy to achieve these goals. Implementing an academic analytics strategy holds the potential for improving the efficiency and effectiveness of programs and activities across campus. Such an undertaking, however, would not come without cost. Process reviews, increases in staffing, new training offerings, expanded communications, and software purchases to implement an academic analytics strategy will require resources. This project will explore the benefits, opportunities, risks, requirements and roadblocks a business intelligence installation offers the University of Minnesota.


Vice President Robert Kvavik has applied the term academic analytics to the application of data and analysis to decision-making in higher education. Academic analytics, at its heart, is a set of tools, techniques, and processes that support data informed decision-making, for both operational and strategic ends. It involves taking data that was previously in the exclusive domain of technical experts and putting it into the hands of a broader set of people. In addition, such a system can provide those experts with the tools to do more sophisticated and impactful analysis leading to actionable insight. Academic analytics, therefore, can be viewed as an umbrella concept for a set of activities, technology and processes that help a university better understand itself, its environment, its history, and its future.

There are at least three faces to an implementation of an academic analytics system: a technological face; a procedural and political face; and a human face. The technological face is probably the most widely understood in higher education, with a myriad of companies, including the major database providers, offering software packages or suites, frequently under the rubric of business intelligence. Many companies market particular configurations of these tools as optimized for higher education institutions. These systems and suites consist of tools such as dashboards, scorecards, OLAP cubes, drill-down and drill-through reports, and the supporting data management technology to accumulate, organize, aggregate, and present the data.

The policy and process face of an academic analytics system may be less familiar, but is also critically important. In a supportive environment, a less-technologically sophisticated implementation can still reap significant benefits. To create such an environment, institutional data must be defined clearly and created consistently to allow systems to present that data accurately. Decisions must also be made about what to measure, how to measure it, when to measure it, and how to get that information into an accessible location for inclusion in the academic analytics framework. For academic analytics to have an impact on institutional performance, strategic and operational priorities must also be expressed in measureable terms, and data must be explicitly and transparently used to inform decisions.

The human face of academic analytics is likewise underappreciated yet critical. Employees who access the data need an understanding of what data mean and how to use the tools to analyze and manipulate it. They must also understand what data is available and what is not. This, in turn, requires readily available documentation, training, and access to both technical and content experts. Likewise, decision-makers need to have experts on staff who can serve as advisers and interpreters of data and trends.

The University of Minnesota has elements of an academic analytics strategy in place through its enterprise resource planning environment (PeopleSoft), data warehouse, enterprise reporting system (Management Reporting), and compact process. In many of these areas, the university has an advantage over its peers. Other pieces of the puzzle, however, are missing, and an overall academic analytics strategy has not been fully articulated. A sound evaluation of the university’s current status, that of its peer institutions, and the costs, benefits, and risks of pursuing an academic analytics strategy is clearly needed to inform progress in this area.

Alignment with Strategic Priorities

An academic analytics strategy has the potential to contribute to the strategic goal of building an Exceptional Organization. Through striving toward this goal, the University of Minnesota seeks to “Be responsible stewards of resources, focused on service, driven by performance, and known as best among our peers”. This goal is supported by four strategic objectives:

  • Adopt best practices and embrace enterprise standard business practices, processes, and technology to achieve efficient, effective, and productive operations
  • Promote nimble decision-making using data, information, research, and analysis
  • Align resources to support strategic priorities
  • Commit to service and results that are best among peers

By leveraging technology, skills, and process improvements to bring data and analysis to bear on both strategic and operational decision-making, an academic analytics approach can empower decision-makers throughout the organization, make clear areas where resource allocations are at odds with priorities, and provide mechanisms to compare performance against professional standards or peer benchmarks.

In addition to alignment with strategic priorities, a successful academic analytics implementation can facilitate the integration of relevant information with several of the items listed in the University’s Criteria for Decision Making list found below. Academic analytics can be seen as a key enabler for moving the University forward as it directly impacts the decision making process at the highest levels of the University.

Criteria for Decision Making

  1. Centrality to Mission
  2. Quality, Productivity, and Impact
  3. Uniqueness and Comparative Advantage
  4. Enhancement of Academic Synergies
  5. Demand and Resources
  6. Efficiency and Effectiveness
  7. Development and Leveraging of Resources

This position is further re-enforced by the six summary recommendations found in the “Final Report to the University Community” released on August 7, 2006 from the Administrative Service and Productivity Task Forces & Steering Committee. These recommendations clearly point to a robust management information system as a key assets in fully integrating these recommendations into the fabric of the University of Minnesota.
Alignment with Ongoing Activities

Previous committees and research teams have built some of the groundwork for this project. Among the groups whose work might be relevant for this project are:

  • SPIF Project on Collegiate Management Metrics (Gillard, et. al.)
  • SPIF Project on Strategic Academic Decision-Making (Warren, et. al.)
  • PEL Project on Administrative Metrics (2007-08 PEL project)
  • PEL Project on Research Metrics (2007-08 PEL project)
  • Metrics Steering Committee (Disbanded, new structure not yet in place)
  • OIT Business Intelligence workplan item (undefined)

Project Goals

The role of the President’s Emerging Leaders team will be to research and evaluate the necessary antecedents for the pursuit of an academic analytics strategy at the University of Minnesota. The team will not be asked to assemble a Request for Information (RFI) or Request for Proposals (RFP) for software vendors, but rather to determine the organizational readiness, possible benefits, and potential roadblocks to an academic analytics strategy. Among the questions that could be answered are: Specific strategic questions to be addressed include:

  1. Define the potential use and benefits of Academic Analytics within in the context of planning and decision support in higher education generally, and the University of Minnesota specifically.
  2. Identify the primary technical, financial, and human resource issues that will challenge the implementation and use of academic analytics at the University.
  3. Recommend a process(es) to integrate academic analytics into planning and decision making at the University and a structure for its management and maintenance once implemented.
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