What Every Campus Leader Needs to Know About Analytics

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What Every Campus Leader

Needs to Know About Analytics

Donald M. Norris and Joan Leonard

Strategic Initiatives, Inc.

The purpose of this White Paper is to help campus leaders understand the principles, potentials, and pitfalls of deploying enterprise analytics in higher education. It describes the evolution of analytics and compares the alternative options for building or buying analytic applications. The paper describes the emerging next generation of enterprise analytics in higher education and their implications for decision makers. The White Paper concludes with a checklist for assessing your institution’s current data, information, and analytics environment.

From the start, we need to clarify two terms: analytic reporting tools and analytic applications. An

analytic reporting tool (often referred to as a business intelligence tool or decision support tool) is a commercially-provided or homegrown toolkit that provides query, reporting, data mining, or other

functionalities for end users. While these tools may provided rich reporting functionality, their capabilities for analytics are essentially dependent upon the richness and schematic of the data models to which they connect. In short, they provide little analytic capability when connected to normalized, transactional databases. Conversely, an analytic application is a complete, optimized, industry-specific solution that is customized to end-user needs. It enables front-line users to access, analyze, and act on information in the context of their institution’s business processes and key performance indicators (KPIs). Analytic applications combine data warehouse, analytic tools, and business process logic specific to the

institution. Many institutions have focused on analytic reporting tools when the real gold standard is an optimized analytic application.

What are higher education-specific examples of analytic reporting tools and analytic applications? An example of an analytic reporting tool would be the deployment of a commercial business intelligence product like Cognos, Business Objects, or SAS as a reporting and visualization product designed for users willing to learn the techniques necessary to master their usage. Analytic reporting tools are also essential components in an analytic application. On the other hand, a best practice example of an analytic application would be a multi-faceted, optimized data/information/analytics environment that enables front-line staff and faculty at an institution to aggressively address student progress and retention. The Higher Ed Analytics suite offered by iStrategy Solutions is an example of a full-fledged

analytic application product.

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Such analytic applications allow an institution to:

• Continually monitor dashboards portraying various aspects of student progress and engagement, “drilling down” with ease to examine the measures in more detail;

• Select from an extensive menu to create changing, complex combinations of variables that can immediately and dynamically create fresh views of the status and progress of various cohorts of at-risk students, combining information, statistics, and advanced visualization, as well as accessing static reports;

• Seamlessly “drill down” to view individual students in each cohort, then examine their academic performance (grades, progress on assignments, use of on-line, self-service resources) and their level of engagement in campus life (as reflected in participation in organizations, use of campus facilities, parking, food service, library, work study, and other measures);

• Compare real information on academic performance and engagement with predictive models based on past cohorts of similar students; and

• Launch automatic alerts for students at risk that inform advisors, counselors, and faculty who can intervene with individual students.

Such retention-enhancing applications exist today. They can provide “institutional intelligence for the masses.” Well-designed analytic applications are fast and easily extensible. In addition to the example just offered, similar analytic application examples could be explained for other aspects of campus performance and management. As this example illustrates, these robust analytic applications can leverage an institution’s data and information resources in ways that were impossible a few years ago. The next generation of analytic applications promises even greater value propositions for campus leaders.

We have kept this White Paper short, highlighting the issues campus leaders face as they contemplate analytic application alternatives. A glossary of terms is attached as an addendum to clearly define the meaning of terms, plus a graphic depicting the evolution of analytic applications, as well as additional resources for reference.

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Unleashing the Power of Data and Information

Most colleges and universities are awash in data. Unfortunately, they lack the capacity to seamlessly turn their data into meaningful information. Nor can they easily access, combine, and repurpose that

information to support analysis, drive decision making, and improve student success. In many cases, the data they need are hiding in plain sight. They are frozen in place by sub-optimized data models (i.e., either ERP databases or ineffective data warehouse designs), by difficult-to-use reporting tools, and/or by the proprietary nature of some systems. Rather than crafting analytical applications that create optimized data, information, and analysis environments, most colleges and universities have focused on individual analytic tools, with disappointing results. It’s time to change perspectives.

These shortcomings are especially vexing today. The leadership of colleges and universities are being challenged by their publics to demonstrate greater effectiveness and value. Leaders are striving to extend and leverage their IT investments – enterprise resources planning (ERP) systems, academic and assessment systems, enterprise portals, reporting/analytics, and other tools/applications. At the same time, they are building the organizational capacity to improve administrative and academic performance, seeking higher levels of accountability across the institution and to external publics.

To achieve these goals, institutional leaders should pursue two concurrent objectives for enterprise analytics.

• Unleash the power of their data and information resources by deploying their best optimized analytics solution from among current analytic applications alternatives. Institutions should strive for analytic applications that create optimized data/information/analysis environments, providing intuitive, user-friendly intelligence at reasonable price points. The best of such open architecture, optimized solutions should also be easy to extend and enhance. Until institutions get this first objective right, they will fail to achieve their analytics potential today or in the future.

• Chart migration paths to the Next Generation of analytics that will extract data from many new sources and incorporate even better visualization, simulation, dynamic modeling capabilities, and other enhancements. These journeys will transform the way that administrators, faculty and staff will make critical, day-to-day decisions based on real-time, dynamically viewed data. Next Gen analytics will draw from a wider array of data sources: all elements of administrative ERP, academic and assessment systems, auxiliary enterprises, career and workforce resources, internal and external benchmarking, and source data.

These Next Gen solutions will derive new value from existing institutional investments in ERP, academic and assessment platforms, information systems, and other applications (parking, telephone services, facilities management). They will dramatically reduce both the unit cost and the total cost of ownership of analytics. This will enable data-driven decision making to much broader cross-sections of the campus community.

The Evolution of Analytic Applications

A brief summary of the evolution of analytic applications is essential to understanding the choices available to colleges and universities today. This requires an understanding of the development of analytics in other industries such as healthcare, where advanced data warehouse-based analytics and performance metrics have been well established for years. The figure below (“The Evolution of Analytic Applications”) summarizes the adoption of ERP, BI, and analytics in both the corporate world and in higher education. These issues are described in even greater length in The Rise of Analytic

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Applications: Build or Buy? published by theData Warehouse Institute (1). You will find a figure we have adapted from this monograph (“The Evolution of Business Analytics”) in the glossary of this White Paper.

Analytics in the Corporate World. Let’s begin by examining the deployment of analytics in the corporate world. OLAP technologies were first deployed as early as the 1970s, but it wasn’t until the mid-1990s that substantial data warehouse products appeared commercially. By 2000,

custom-developed data warehouse applications had been custom-developed and progressively enhanced and refocused. By this time, the term “business intelligence (BI)” had emerged, describing a new generation of analytic reporting tools. Early-stage dashboards appeared in the early 2000’s, giving new life to desktop reporting and executive information system concepts that had been around for a decade or more.

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Business intelligence tools received another boost from the appearance of easier-to-use, integrated tool kits enabling power users to customize BI tools to meet their reporting needs.

The first examples of true analytic applications appeared in commercial settings in the late 1990’s and their sophistication grew steadily. Enterprises in a number of industries such as healthcare and financial services could either custom develop their own analytic applications or buy packaged, industry-specific analytic applications. These packaged applications were a significant breakthrough, offering rapid deployment, reduced risk, enhanced performance, and lower total cost of ownership.

Moreover, a highly important development in the analytics marketplace was the entry of Microsoft into the commercial marketplace. Beginning with OLAP engines embedded in products, Microsoft progressively expanded its range of analytics capabilities, eventually developing the tag line of “BI for the masses” as part of its overall strategy. By offering inexpensive BI products that could be deployed as part of analytic applications, Microsoft set the stage for significant reductions in the total cost of ownership of analytic application solutions. As the BI market has matured over the past five years, larger players have positioned for market share. This culminated in 2007 with acquisitions of Cognos by IBM, Hyperion by Oracle, and Business Objects by SAP.

Analytics in Higher Education. Higher education has lagged behind other industries in the development of analytics. Many of the BI tools developed in commercial settings have been

subsequently offered by vendors to the higher education marketplace. By 1998, first generation, custom-developed data warehouse applications had appeared in higher education as did non-industry-specific data warehouse tool kits without substantial analytics. Over time, custom-developed data warehouse and analytics applications were deployed by some institutions (ranging from fully “homegrown” to developed by consulting firms specializing in data warehouse/analytics). By 2002-2003, other ERP providers were offering data marts/data warehouses (Jenzabar, SunGard, Oracle). Over the course of time, the

sophistication of such offerings has evolved, improving the template-based tools that could be used to construct full-fledged analytic applications.

These template-based tool kits are a useful starting point for institutions wishing to build on an existing vendor relationship. However, these template-based tool kits require substantial additional effort by the customer, assisted by consulting services from the vendor, in order to configure a true analytic

application. The customer must use the template-based tool kit to create the data mapping, data modeling, and customization of reports and analytics to meet their institution’s distinctive needs. By the end of 2007, all of the major ERP providers were offering their customers various forms of analytic application options -- primarily template-based tool kits for building data warehouse functionality,

extended by the latest generation of analytic reporting tools (BI tools). Additionally, some ERP providers were also offering their customers the option of packaged analytic applications provided by their alliance partners.

The arrival of packaged analytic applications customized to higher education was heralded in 2004 by iStrategy Solutions’ deployment of its HigherEd Analytics Suite, which was the first packaged student information system (SIS) analytic application in the higher education industry. In early 2008, this was followed by a further advancement: a cross-ERP, packaged analytic application that was mapped to major platforms and included a range of modules (SIS, FIN, FinAid, HR, Alumni). The impact of this solution was further enhanced by its providing a range of tools options, including the use of Microsoft products, the competitive pricing of which drives down the total cost of ownership. Similarly, the use of Microsoft products has been deployed in some of the best custom-developed analytic applications by institutions who have elected to build their own data warehouse and analytic applications.

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Three Analytic Application Alternatives. In summary, today’s decision makers in higher education are faced with three general alternatives in creating analytic solutions to their reporting and analytic needs:

1) Packaged analytic applications; 2) template-based tool kits, and 3) custom-developed analytic applications. So-called analytic reporting tools (business intelligence tools) are part of each of these solutions. The following section compares and contrasts the characteristics of analytic application alternatives.

Making Sense of Today’s Analytic Applications Alternatives

Campus leaders have a range of “buy” versus “build” considerations for deploying analytics solutions in higher education. Our experience advising campus leaders suggests that the distinctions between these options and the value propositions associated with each are poorly understood. “Making Sense of Today’s Analytic Applications Alternatives” illustrates the current landscape of reporting and analytics alternatives, differentiating among: 1) packaged analytic applications, 2) template-based tool kits, and 3) custom-developed analytic applications. The table also explains the role of analytic reporting tools in each alternative.

Packaged Analytic Applications. Since the late 1990’s, packaged analytic applications have been available in healthcare and other industries. They are now available commercially in higher education and provide an attractive value proposition: rapid implementation, out-of-the-box, and low risk.

The concept behind packaged solutions is simple. A vendor, experienced in reporting and analytics in a specific industry sector like higher education, creates a packaged version of a complete optimized solution in a manner that can be quickly installed, refined and extended. Packaged analytic applications are pre-built, optimized combinations of ETL, data warehouse, OLAP, BI capabilities, business metrics, predefined reports, dashboards, and “best practice” processes. They include pre-configured data mapping into leading ERP provider databases. This vendor-supplied package provides higher education-specific analytics. It contains an integrated set of analytic tools, data models, ETL mappings, business metrics, predefined reports, and “best practice” processes that accelerate the deployment of an analytic application in a particular institution. They include pre-built, web-based reporting, dashboard, KPI capabilities, plus the capacity to leverage any open analytics tool/platform.

In most cases, these packaged analytic applications fulfill 85% or more of a typical institution’s data, information, and reporting needs. With an effective, extensible application, the remaining 10-15% of an institution’s requirements can be efficiently achieved by extending data models, business rulers, and content. These solutions can provide a string of advantages:

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• Well-executed, packaged solutions incorporate invaluable know-how on optimizing database design, data mapping, data modeling, standard reports, and dynamic viewing of changing variables relevant to higher education as accumulated through successful implementations at client institutions.

• When a pre-configured integration to ERP databases exists, this solution can provide an out-of-the-box data warehouse with the institution’s own data very rapidly -- with certain products, in as little as 2 or 3 days. After ongoing collaboration with the institutional team, a robust reporting and dynamic viewing environment can be deployed in as little as 6-10 weeks. This rapid deployment greatly reduces the risks of an unsuccessful, protracted, sub-optimized analytics implementation.

• An optimized solution for a robust analytic application can dramatically lower the total cost of

ownership to a fraction of the price of other solutions that are based on existing, expensive business intelligence tools. This is especially true when the solution leverages Microsoft technology and tools. Packaged analytic applications will be a powerful instrument for driving down the cost of analytics in higher education. Well-designed, packaged solutions have proven to be relatively easy to extend and enhance, quickly incorporating new/refined data definitions and new data sources. Open architecture analytic applications that are scalable and extensible are important elements of the migration path to the next generation of analytics in higher education.

Optimized, intuitive solutions that result from packaged analytic applications enable grassroots end users, not just power users, to benefit from advanced analytics.

Template-based Tool-Kits. This alternative is offered by ERP vendors as an extension of their ERP product and services stack. Its value proposition is that it provides a single-vendor solution, typically with a vendor with whom the institution already has a working relationship.

Enterprise Resource Planning (ERP) Systems are transaction-focused, complex databases with limited reporting/analytics capabilities. Currently, ERP vendors are offering a variety of add-on business

intelligence and data warehouse solutions that provide access to analytic tools and reports for the specific functional areas (i.e., Student Information Systems, Finance, Financial Aid, Human Resources ERP modules) covered by these ERP applications.

In addition, ERP vendors and their third-party alliance partners are offering a variety of ETL, data store, data warehouse, and business intelligence tools, designed to extend the ERP application stack. Even if highly integrated, these tools do not constitute a fully functional analytic application. Deploying these tool kits and using them to create robust analytic applications typically requires substantial client effort and/or consulting services to achieve the functionalities found in full-fledged packaged analytic applications. Basically, customers/consultants need to use the template-based tool kit to install, configure, and in most cases substantially extend the data models. In addition, the customers/consultants need to build OLAP cubes and dashboards, and develop reports and other adaptations required to meet the institution’s particular needs and fulfill its value proposition. In some cases, this may be complicated by

sub-optimized data, information, and analysis environments. Institutional staff may not adequately understand their data structures/resources and/or may lack the know-how about data modeling and optimizing the data/information/analysis environment that is necessary for a successful tool kit-based implementation.

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Custom-Developed Analytic Applications. Over the past decade, many early adopter institutions have opted to build their own “homegrown” data warehouses and analytic applications. The value proposition for this alternative was that you can get what you want – if you can afford to build it and have access to the requisite know-how.

Until recently, institutional leaders have chosen to “build” based on their perception that their institutions have unique functional requirements and/or that there were no packaged analytic applications that would provide a viable “buy” option. Homegrown analytic solutions vary in comprehensiveness from limited functionality data warehouse projects to comprehensive analytic applications that combine

applications/tools such as data warehouse, extract, transfer and load (ETL), online analytical processing (OLAP), business intelligence (BI), and/or predictive modeling and data mining with the institution’s distinctive business rules and logic (see the Glossary for a full explanation of these terms and how they fit together).

Data warehouse projects at most institutions can easily take several years and millions of dollars or more to complete. These in-house projects can only be undertaken successfully by institutions with significant developmental capacity and expertise in analytic data warehouse methodology. Many of these projects have proceeded and/or evolved incrementally over an extended period of time. Independent research by the EDUCAUSE Center for Applied Research (ECAR) has shown that many homegrown initiatives fail to deliver on their promise, falling short of expectations (2).

Other early adopter institutions chose a variation of the “build” alternative by bringing on technology consulting firms, specialized in data warehouse/performance management, to build customized analytic applications. This approach typically accelerates the time to develop and deploy the analytics application. The success of these projects has depended on the skill and experience of the consulting firm and the effectiveness of their working relationship with the institution. In scanning examples of customized applications, a number of the consulting firms were relatively inexperienced in higher education. Consequently, the consulting project involved considerable adaptation to higher education requirements in order to discover the best combinations of tools, process rules, and metrics to create a relevant solution. Development of robust, customized analytic applications may require a year or more of effort. Another consideration is that in the end, the institution is left to maintain and try to extend an application that was developed by a third party, but has no product life cycle for upgrades.

Analytic Reporting Tools. Analytic tools by themselves do not constitute a robust analytic solution, although they are a part of the solution provided by all three analytic application alternatives. They provide query, reporting, OLAP, ETL, data mining, and/or other functionalities for end users. In order to load institutional data and make these tools functional, it requires a substantial investment of client effort and/or consulting services. Analytic tools can be used to create data warehouse and analytics solutions, but the effectiveness of these tools is highly dependent upon the structure of the underlying data models. Additionally, these tools vary greatly in ease of use and the degree to which they support self service ad hoc reporting and analytics. . When a provider of an analytic tool (or an integrated set of tools) says their product can be running “out of the box,” they mean that the tool can be installed, but not delivering analytic information with the institution’s data as yet. Loading the data to make that happen requires a considerable consulting effort and consumes campus IT department time and resources to build specialized reporting data models that can be leveraged by analytic tools.

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Comparing Analytic Application Alternatives. Each institution faces a unique set analytical

circumstances, challenges, and opportunities. These are shaped by their current analytical capacity and campus leadership’s vision of where the institution needs to be in the future. One cannot compare the pros and cons of these three analytic application alternatives in the abstract. These assessments must be contextualized to the current conditions and future needs of each institutional setting.

That being said, the set of parameters/questions outlined in the table below, “Comparing Your Institution’s Analytic Alternatives,” is a good place to start. Each institution should evaluate its analytic application alternatives in its own setting, using these parameters and questions to frame and contextualize the value proposition for each alternative as it relates to the institution’s requirements/vision.

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The Next Generation of Analytics in Higher Education

The EDUCAUSE Review recently published several groundbreaking articles on the next generation of analytics in higher education (2). The figure below, “The Next Generation of Analytics” depicts the three elements of these powerful new analytics applications.

These Next Gen analytics display three primary characteristics:

• Access to a Broad Range of Data Resources. Next Gen depends on the capacity to map and extract data/information in context from a wide range of data “buckets.” The Data Layer in Figure 1 encompasses a far wider range of data sources than are deployed in today’s analytic

applications. Data will be extracted from familiar administrative ERP sources – student, financial, financial aid, human resources, development, and alumni – and also from specialized

administrative systems for food services, building security, parking, and other auxiliary enterprises. Academic Information Systems will be extracted for academic standing, course participation, attendance, and progress; participation in online discussions and engagement of online resources; and library data/information. Moreover, knowledge repositories and research sources will be available for both analytic and learning development applications. Structured and unstructured assessment data and information will be extracted, including student co-curricular transcripts and the non-private elements of student portfolios.

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• An important new analytic development will be the availability of crucial, contextualized

information on institutional alignment. Strategies, goals, actions, and measures will be available online, aligned at the institutional level and cascading to the college, department, and program-levels. Furthermore, strategic planning, accreditation, program review, continuous improvement, and performance measurement will be aligned and will be used to contextualize analysis. Finally, a variety of external information sources on peer institution performance data, institutional and program reputational quality, employer and workforce data, and national comparative data will all be extracted, providing a broader context for institutional analysis and comparison of performance and value.

One of the major developments in the data layer will be the increasing openness of existing ERP products and the growth of open source applications like Kuali, Sakai, uPortal and other

collaborative initiatives. The higher education community is committed to opening up the existing ERP and Academic Information Systems stacks.

• A Loosely Coupled Analytics Layer. The analytics layer will exist as a “cloud,” loosely coupled with the ERP stack and other data sources. It will consist of optimized combinations of familiar components: data warehouse, ETL, OLAP, BI, data mining and predictive modeling. These analytic applications will be decisively open architecture. They will include an unprecedented tidal wave of Web 2.0 “niche” analytic tools that address particular, specialized analysis and/or visualization and presentation needs. They will be seamlessly integrated into the “cloud” of the analytics layer.

The Next Gen analytics layer will feature a number of major advances. First, we will witness even greater optimization of the growing number of analytical elements that will be brought together in the analytical applications. Second, the speed and capability of data/information access and modeling will improve. Third, the capacity to extract data/information/context from an ever-expanding range of resources will extend the reach of the analytics layer.

• A Flexible Presentation Layer Combining Dashboard, Portal, and Portfolio Elements. The presentation layer will likely undergo dramatic enhancements. Campus leaders will demand the capacity to combine dashboard, portal, and portfolio modalities in the presentation layer. The sophistication of simulation and visualization tools will grow considerably as well.

– Today’s so-called “executive dashboards” offer a limited menu of key performance indicators (KPIs). Dashboard of KPIs with the capacity to dynamically revise and view KPIs and drill down to greater detail and examine cohort populations and individual performance;

– Balanced Scorecard/Strategy Map Views of the Institution (3) with drill-down capabilities to college, department, and program levels;

– Views of the Institution’s Strategies, Goals, Actions, and Measures (gleaned from strategic plans, annual operational plans and budgets, capital plans, and other sources)with drill-downs to college, department, and program levels;

– Views of the Institution’s Accreditation and Program Review Status for regional accreditation and all programmatic accreditations, showing status, corrective actions, and responsibilities;

– Views of the Institution’s Reputational Quality in all program areas and in comparison with peer and aspirational target institutions; and

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– Assessments of Institutional Performance and Value in comparison with peer institution, institutional targets, and national norms.

Progressively adding these capabilities to the presentation layer will be a major leap forward that will be part of an on-going migration path for colleges and universities.

Migration Paths for Next Generation Analytics

The first step along the migration path to Next Gen analytics is to deploy current analytic applications that put the institution on course to optimizing the institution’s data/information/analytics environment.

Campus leaders need to raise the analytic IQ of its leadership team, shifting the focus away from assembling sub-optimized collections of analytic tools. They need to achieve true analytic applications that position the campus data, information, and analytic environment for the subsequent steps on a migration path that will achieve the following capabilities:

• Administrative Analytics. Administrative analytics primarily draw from administrative ERP and related data sources. Most institutions focus on student success and retention in their initial efforts at implementing analytic applications. Improving retention and student performance/success is higher education’s genuine “killer app.” Improving retention by even a few percentage points can provide an immediate return on investment and justify the acquisition of a full analytic application solution Other administrative analytics that focus on process improvement, improved services, and customer satisfaction will also yield substantial dividends.

• Academic and Assessment Analytics. The next step along the migration path moves beyond administrative analytics to extracting data/information/context from existing academic information systems (learning/course management systems, assessment materials, online learning content). Currently, many of these data are frozen in place by proprietary restrictions in some L/CMS systems. This is one of the major forces behind the open source movement in higher education – access to the institution’s data. The key challenge for institutional leadership is to understand their current ecology for academic and assessment data and information, and then to craft a strategy that will enable unfettered access to this data. Over time, these data should be made part of the analytic application’s optimized data warehouse.

• Strategic Planning and Execution Analytics. Campus leaders should begin to include strategic planning analytics in their dashboards and portals. This step assures the effective execution of strategies aligned across the institution, from institution to college to department, and from planning to accreditation to continuous improvement. New generations of open source tools for achieving

alignment are being developed, and are described in the recent articles in EDUCAUSE Review(3).

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Learning and Career Analytics. This will become an increasingly important elementof institutional analytics. Improving preparation for life success and employment, facilitating transitions between learning and work and back again, and demonstrating the value added by institutions are increasingly important. It is already core to the mission of community and technical colleges. Learning and career analytics will likely be prominent in analytic applications developed for these types of institutions.

Many states are supporting strong 20 learning and work initiatives, and partnerships between PK-20, employers, and workforce organizations. These initiatives potentially require special kinds of analytic applications to enable cross-sector sharing of data/information/context. The recent articles in

EDUCAUSE Review (3) describe a variety of these analytic initiatives that could herald similar initiatives in many other states.

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Ultimately, the migration path to Next Gen analytic applications is likely to be an expeditionary campaign that with take years to unfold. At the end of the day, colleges and universities will evolve their analytic applications to a higher plateau of accomplishment, including a far broader set of data sources and a richer set of analytics.

Business Drivers for Analytic Applications

In conclusion, campus leaders should recognize that analytic applications in higher education are being shaped by a sound set of business drivers:

• The drive for performance improvement, accountability and demonstrating value in general to institutional stakeholders, both internal and external.

• The focus on new measures of performance beyond administrative and financial reporting to include academic assessment (recruitment; retention – not just freshman-to-sophomore year, but at all transition points – high school to 2/4 year colleges/universities to employment; graduation rates, and assessment of student competencies).

• The need to integrate reporting/analytics with strategic planning and alignment of goals, actions, responsibilities, and measures that cascade from state to system to college to department to program, so that administrators, faculty and staff across the organization are held accountable via access to personalized dashboards that support data-driven decision making and early alerts.

• The importance of moving beyond tools for “power users” to providing for “the masses” (all administrators, faculty and staff) with access to institutional intelligence and analytics. Student success and institutional/learner performance improvement measures (students at risk, retention of freshmen through graduation and employment) will require that the entire campus community have the ability to access/analyze data and take appropriate actions.

• The public demand for better fiscal management of funds, equipment, facilities, and human resources demands that attention be paid to demonstrating institutional performance and value.

These business drivers are capturing the attention of campus leaders across higher education. As leaders consider their future and the options and migration paths for analytics applications, leaders should answer the following set of questions about their current environment, as posed in the table below, “Your Current Analytic Environment Checklist.”

The bottom line is that institutions need to focus a great deal more attention on their data, information, and analytics environment if they are reap the benefits of their investments in information technology. Developing strategies and migration paths for optimizing analytic environments should feature

prominently in institutional plans for the future. In this way, institutions can rediscover and leverage their data that are hiding in plain sight.

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References

(1) Wayne W. Eckerson, “The Rise of Analytics Applications: Build or Buy?” The Data Warehouse Institute, 2002.

(2) Philip J. Goldstein, “Academic Analytics: The Uses of Management Information and Information Technology in Higher Education,” ECAR Research Study, Vol. 8, 2005.

(3) Donald Norris, Linda Baer, Joan Leonard, Lou Pugliese, Paul Lefrere, “Action Analytics: Measuring and Improving Performance That Matters,” EDUCAUSE Review, Jan/Feb 2008.

Donald Norris, Linda Baer, Joan Leonard, Lou Pugliese, Paul Lefrere, “Framing Action Analytics and Putting Them to Work,” EDUCAUSE Review, Jan/Feb 2008, electronic article.

John P. Campbell, Peter P. DeBlois, and Diana G. Oblinger, “Academic Analytics: A New Tool for a New Era,” EDUCAUSE Review, July/August 2007.

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About Strategic Initiatives, Inc.

Strategic Initiatives is a management consulting firm, headquartered in Herndon, Virginia and specializing in strategic planning for the effective utilization of information and communications technology in higher education and other industries. Strategic Initiatives is recognized as a leading practitioner and thought leader in the leveraging of technology to achieve organizational transformation and creating indispensible value propositions for its clients.

Over the years, Strategic Initiatives has utilized a series of publications, authored or co-authored by Donald Norris, to promulgate breakthrough concepts that have been widely deployed in higher education. These publications have included Transforming Higher Education: A Vision for Learning in the 21st Century, Unleashing the Power of Perpetual Learning, Revolutionary Strategy for the Knowledge Age,Transforming e-Knowledge: A Revolution in Knowledge Sharing, Value on Investment in Higher Education, and The Business Value Web. Recently, Dr. Norris co-authored a series of articles in the EDUCAUSE Review that espoused the principles of action analytics in higher education.

Over the years, the publication of many of these books, monographs, and white papers have been sponsored by leading technology companies serving the higher education marketplace, including PeopleSoft, WebCT, SCT, AT&T, KPMG, Knowledge Media, and iStrategy Solutions..

Contact

Donald M. Norris, Ph.D President, Strategic Initiatives, Inc.

703.450.5255 www.strategicinitiatives.com

What Every Campus Leader Needs to Know About Analytics