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Business Officer Magazine

Managing Faculty Talent

With an analytics dashboard, human resources and academic leaders at the Ohio State University have at their fingertips trend data that supports faculty workforce planning.

By Laura Gast and Ken Orr

*The Ohio State University's Faculty Analytics Dashboard has literally woken up administrators.

During a recent demonstration of the dashboard tailored for them, chairs and deans from across the campus learned about the information available for strategic planning by using this tool. Since it was after lunch, one of the deans was beginning to nod off, until we began discussing faculty retirement eligibility. As the data within the dashboard appeared on the screen, he became instantly awake when he realized that 68 percent of the professors in his college were either currently eligible to retire or would be eligible in the next five years. He was not the only one to have an “aha” moment while reviewing the data on the dashboard.

There were growing concerns about the aging workforce and how many of our talented faculty would become eligible to retire within the next few years. This was one of the major reasons the Faculty Analytics Dashboard came into being. Concerns about faculty retirements gave urgency to addressing the growing frustration on Ohio State's campus that basic information about faculty was not easily available to colleges and departments. Not only was the data not available in an accessible system, it was managed by multiple offices.

While the human resources office managed appointment information, the academic affairs office handled the promotion and tenure process and the assignment of faculty to one of our three regular faculty tracks (tenure, clinical, or research). Information on the number of years faculty members had been at their current faculty rank of professor, associate professor, assistant professor, or instructor—was not held centrally and needed to be collected from individual departments if required for analysis.

Against this backdrop, a faculty data workgroup, created by the university's provost, evolved into a project team charged with aggregating this data and creating a simple, flexible tool to support college and departmental leadership in faculty talent management through a graphic-based dashboard application (see sidebar, “Dashboard Development Timeline”).

The dashboard uses data from Faculty Analytics and is designed to be easy to use while providing the option to see aggregate data as well as individual faculty member information. It also contains historical information (prior 10 years), allowing for trend analysis. The dashboard's primary purpose is to support faculty talent management processes.

Simply stated, the Faculty Analytics Dashboard is an easy-to-use tool that provides faculty information in both graphic and tabular form. From initial concept through actual implementation, this tool has been extremely useful to deans, directors, chairs, and senior HR professionals for performing trend analysis, diversity planning, retirement projections, and other decision making to aid in managing faculty talent.

Form and Function

The Faculty Analytics Dashboard main page (see Figure 1) contains multiple graphs that cover the following metrics:

  • Age distribution.
  • Retirement eligibility.
  • Turnover percentage.
  • Voluntary turnover by type.
  • Number of years in rank distribution.
  • Tenure status.

Academic leaders and their staff members are able to quickly filter the graphs to limit them by college/department, rank, track, and demographic values. If further information is required, clicking one of the graphs displays a full-page view (see Figure 2). Here, more filter options are available, and users can also drill down into the graph. For example, an end-user can click on the voluntary turnover percentage graph and drill down to see distribution by gender.

The data can become more dynamic for users if they switch to the pivot table view (see Figure 3). The pivot table contains the counts and percentages that make up the graph and allows users the full flexibility to add, remove, or reorder fields, create totals, focus on items, change labels, and export data—thus customizing reports to best meet their needs.

Download and Development

The initial work on the dashboard got under way six years ago when OSU's provost created a faculty data workgroup charged with:

  • Reviewing data currently available on faculty and instructional staff.
  • Determining where it was housed.
  • Identifying information gaps and data inconsistencies along with the factors that give rise to them.
  • Making recommendations for immediate and long-term solutions.

When the workgroup met, its first task was to understand the nature of the existing faculty data. Once that was accomplished, the remainder of the time was spent on identifying the gaps in the information and designing a comprehensive set of variables to be incorporated into a central database. This new database could then be queried to answer the wide-ranging data questions posed by various external and internal groups. The workgroup issued a report containing a set of recommendations with the priority being to build a faculty data warehouse with a reporting function to make faculty data readily available to colleges and departments. As a result of the report, a project team was formed to build the faculty data warehouse.

To move forward with kicking off the project, we knew that we would need to give careful attention to three critical areas: subject matter expertise, scope, and support.

Subject matter expertise. The normal procedure for projects like this was to create a big, campuswide committee to determine design requirements. We knew that we didn't have time to try to operate with such a large committee, so we gained the provost's agreement that we could keep our group of subject matter experts (SMEs) to no more than four individuals. For our institution, that was virtually unheard of at the time, but we were insistent. We ended up with a wonderful SME group that had fiscal, HR, and administrative experience.

Since personnel expenses comprise more than 70 percent of our budget, the fiscal perspective was important for managing faculty “lines” and the budget. Having the HR perspective was important in helping to manage (recruit and retain) faculty talent. Administrative perspective was important since faculty administrators provide unit leadership and create strategic plans for the unit. They make decisions about compensation, disciplines that are the most critical to pursue for the department or school at what times, and so forth. All of these critical perspectives were represented while still keeping the size of the SME group manageable.

Querying the Dashboard

Here's an overview of some of the categories of business questions we asked to determine what kinds of data would be most helpful to include in the dashboard.

Basic demographics

  • What are the basic demographics of the faculty population—gender, ethnicity, age, marital status, veteran status?
  • By campus, college, and/or department?


  • What percentages of the faculty have degrees from institutions in Ohio?
  • What are faculty members' alma mater institutions?

Administrative appointments

  • What percent of faculty holds administrative appointments?
  • What is the gender/ethnic mix of faculty administrators?

Length of service, tenure, and rank

  • How long have faculty members been at OSU, and what has been their progression through the academic ranks?
  • Can we forecast the number, type, and disciplines of faculty who are eligible for retirement?


  • What is our retention level for faculty?
  • What is the level of turnover for faculty?

New hires

  • What is the success rate of our faculty hires and how many attain tenure?
  • How many leave OSU?
  • How many of our faculty hires are our top-choice candidates?


  • What are the compensation levels of high-level OSU faculty administrators and how do they compare with other institutions?
  • How do their salaries compare to newly hired faculty?

Scope. Once the core project team began meeting, our first step was to create a list of all the business questions about faculty that we wanted to answer. The list was quite exhaustive and contained more than 40 broad categories. For example, one series of questions addressed what data was maintained relative to faculty rank, tenure, and promotion (see sidebar, “Querying the Dashboard”). What is the rank distribution of regular faculty members? How long have they been in their current rank? What is the rate of promotion and tenure? Because of our project timeline, we had to determine a manageable number of business questions to establish the scope of the project for the initial phase. Additional data could be added to the warehouse over time.

The scope agreed upon by the SME group was the data contained within the university's PeopleSoft HR system. A single data source would simplify the extraction, transformation, and process of loading the warehouse while providing a great deal of data for reporting and analysis purposes. We wanted to load 10 years' worth of data into the warehouse but decided instead to use data beginning from the implementation of the PeopleSoft HR system in July 1997. Including data from our legacy appointment system would have introduced a great deal of complexity in terms of trying to map old data structures into those created from the current system. With the initial scope defined, the Faculty Analytics project represented the first time that trend data on faculty was easily available from a single source.

Support. One of the keys to the success of the Faculty Analytics project was the support of our provost. For example, we found we needed to extend the project timeline to redesign a key report because after the team members tried using it, they realized the report wasn't answering the questions around which it was originally designed. With the provost's support, we took the time to completely change the underlying structure and reprogram the report. The new and improved report was an unqualified success and well worth the extra work to create it.

Rollout and Reporting

From the array of business questions presented, it quickly became apparent that static reporting would not meet the needs of our various internal customers—at least not without creating dozens of versions of each report. The university's enterprise business intelligence reporting tool was Brio Insight (which later became Hyperion Intelligence and is now owned by Oracle). This tool supported a variety of reporting options, one of which was pivot table reporting.

We determined that delivering data via pivot tables would provide customers the flexibility to answer a variety of business questions within a single report and to organize the data in the way best suited to their areas. By further supplying an extensive “pick list” of fields that could be added to the pivot table reports, we could increase the flexibility of the reports and therefore their usefulness.

Access to the Faculty Analytics Dashboard is controlled centrally, and approval is needed to gain access to the reports. Initially the dashboard was made available to HR central staff and all HR professionals distributed across the university. As academic leaders, including deans and chairs, were introduced to this tool, they have requested and received access to the dashboard directly. The reports generated from the dashboard are self-service reports in that users pull the data themselves when information is needed.

Training has taken a variety of forms. At first, awareness sessions were held to make potential users aware of the reports and their content. A training session is available to faculty leaders as a part of OSU's academic leadership development series. We also provide individualized training for any college or department upon request.

The initial rollout of Faculty Analytics reporting began with three reports: a basic report for demographics, counts, and lists, and two salary-based reports. Two additional reports, which focused on faculty transactions (hires, promotions, separations, and so on) and a report providing information on faculty earnings, were subsequently rolled out. Here is the menu of reports currently available:

  • Faculty demographic counts and lists, contains data on faculty gender, ethnicity, time in rank, and headcount/FTE.
  • Faculty transactions, identifies faculty transactions over time—hires, terminations, transfers, promotions, and so forth.
  • Annual base and FTE equivalent base salary, compares annual base salaries across years at a specific point in time each year.
  • Current/previous annual rate for reporting with percent change, compares change in annual rate for reporting salaries over time.
  • Faculty compensation—cumulative actuals, analyzes total actual compensation (including quarter off-duty, supplemental compensation, and so on) from all funding sources.

The annual base and FTE-equivalent base salary report and current previous annual rate for reporting with percent change reports are both helpful in the annual merit compensation process. For example, these reports can be used to compare base salaries by rank and by faculty member to analyze equity trends.

The faculty compensation—cumulative actuals report has also proven extremely useful to the fiscal officers on campus. Not only are they able to view earnings by individual, type, and source, but the data is summarized to provide information for budget planning. As soon as this particular report was rolled out, we began receiving inquiries about when it would be available for staff employees as well because it would be so useful.

One key use of the Faculty Analytics compensation reports is in counteroffer situations. Departments use the reports to generate a complete listing of faculty salaries at a specific rank within the desired discipline, and the chair or dean can determine the impact of extending a counteroffer on internal equity and the departmental budget if other adjustments are needed. The ability to include years in rank, FTE equivalency, gender, and ethnicity, as well as a history of salary increases, provides additional insight when making these decisions.

We implemented these reports with a “soft” rollout so that if any issues were discovered among the early adopters, we could fix problems before a wider audience used the reports. We felt very strongly that if users tried the reports and mistrusted the data for any reason, they would conclude that the Faculty Analytics data was “wrong” and might never come back to use the reports again.

The Faculty Analytics project team is often cited as a best-practice example of how a cooperative project should work. Prior to this effort, similar projects were staffed out of one office, and project team members from other areas would move to the project away from current roles and responsibilities. Because our project was small, those of us who were only part time on the project stayed on in our current roles and truly cooperated across organizational boundaries. While we moved more slowly than a project team dedicated completely to a single project, we were able to stay involved in the work in our areas of expertise rather than being isolated and focused only on this effort.

Last year the efforts of the Faculty Analytics project team were recognized by the College and University Professional Association for Human Resources (CUPA-HR) when we received the organization's SunGard Higher Education Innovation Award. This award recognizes human resource innovations in technology, process improvement, partnerships that advance the profession, or approaches to current challenges facing HR.

Implementation and Intelligence

Dashboard Development Timeline

We developed the Ohio State University's Faculty Analytics Dashboard over the course of almost six years. If you're considering a similar undertaking, you may find it helpful to review this summary of the major steps we took in our process.

Summer 2004

A faculty data workgroup, created by the provost, met to determine the nature of the existing faculty data. The workgroup identified gaps in the information and designed a comprehensive set of variables to be incorporated into a central database.

The workgroup issued a report containing a set of recommendations with the priority of building a faculty data warehouse with a reporting function to make faculty data readily available to colleges and departments.

Early 2005

As a result of the report, we formed a project team to build a faculty data warehouse during the coming year. The core project team began meeting in May and created a list of all the business questions about faculty that we wanted to answer. The initial rollout of faculty analytics reporting occurred in December with three reports: a basic report for demographics, counts, and lists, and two salary-based reports.

Late 2007

We built a proof-of-concept dashboard. The first step was to create a proof-of-concept product based on simple spreadsheets to demonstrate that it was possible to build what we wanted. Its purpose was to explore the feasibility of creating a tool using our faculty analytics data repository that would be flexible and easy to use.


The Faculty Analytics Dashboard team built a prototype. After receiving positive feedback from subject matter experts in the human resources office, the team worked with them to identify key metrics as well as to define the dashboard functionality.

Early 2009

The Faculty Analytics Dashboard was fully implemented. In all cases, the initial feedback was very positive, and members of the project team were eager to begin using the dashboard.

Once Faculty Analytics was well established, we wanted to use the data source to put information into the hands of leadership to assist with decision making at the university. Since its implementation in early 2009, the dashboard has mainly served as a tool to support strategic planning. For instance, certain pieces of information, such as estimated retirement eligibility, aren't available from other sources, so we are supporting academic leaders in planning for potential talent gaps due to retirements. While the current state of the economy encourages faculty to remain working, there will be talent shortages when our eligible faculty do decide to retire.

The ability to look at 10 years' worth of data in a graphic format highlights trends and encourages leaders to dig deeper to see what is driving the displayed trends. University executive leadership uses dashboard information prior to embarking on a faculty recruiting campaign and when assessing the success of recruiting, selection, and retention strategies. For instance, the dashboard provides valuable information regarding diversity, both in regard to gender and nationality, to identify those areas that the department should target for future recruitments.

Academic leaders are also using the dashboard information for purposes not initially identified by the development team. For example, academic leaders in the College of Veterinary Medicine used the dashboard to get information for their departmental academic program review. The chair of the department of veterinary clinical sciences used the Faculty Analytics Dashboard to assess faculty composition, leadership, and succession planning over the next several years based upon time in rank and pending retirements. Additionally, assessment of the time-in-rank of associate professors was useful, as was evaluation of salary/compensation based upon faculty type (tenure track versus clinical track) and rank.

The dashboard is still relatively new to the community and is being marketed by word of mouth as well as by presentations to academic leadership groups. These groups enthusiastically added presentations to their full meeting agendas, and individual leaders in the groups are championing the dashboard and the value it brings to faculty talent management.

Next and Near Future

We view the current faculty dashboard as simply a starting point for what is possible. In the future, we hope to include data from further sources, such as additional demographic factors about faculty, student credit-hour data, grants and awards, and perhaps even publication and presentation data.

We can increase the usefulness of the tool by incorporating data on the factors that are important for decision making and strategic planning. For the staff dashboard currently under construction, we hope to be able to collect and include data on a variety of factors, especially performance, to support our talent strategy. We need to understand what is happening with our top talent (top performers and those with key skill sets) so that we can implement proactive retention strategies to make sure we keep that talent at the university.

One area of potential development for the dashboard is to use it as a benchmarking tool. To do that, we would have to incorporate data from external sources for benchmarking purposes. Some data is readily available for other institutions (such as base-salary data), but other information can be challenging to obtain and use because of inconsistent definitions even for basic data.

Since development of the Faculty Analytics Dashboard took quite a bit of time, one of our hopes is to find a way for other institutions to be able to use what we've done and eliminate the need for them to reinvent the wheel by having to develop their own tools from scratch. We have approached the Inter-University Council in Ohio, a group of four-year public institutions, and have made an initial presentation to that group with the hope of having other schools use our product. Several other Big 10 institutions have seen the dashboard in our CUPA-HR presentations, and we would love to have discussions about common use of the tool for benchmarking purposes.

Larry Lewellen, Ohio State's vice president for human resources, sums up the impact of the Faculty Analytics Dashboard at our university: “This simple-to-use but robust tool, and the data it generates, creates conversations and partnerships with academic leaders and an appetite for use of data by our senior fiscal officers and senior human resource officers; helps us make informed diversity-related decisions and other talent-related decisions; and aids us in making decisions around the difficult task of budget and position reallocation.”

LAURA GAST is senior research consultant and KEN ORR is resource planning analyst at the Ohio State University.