Analytics & BI in the Research Enterprise
Data analytics in research administration can provide critical insights to the work that is happening, how we strategically align resources, support innovative ideas, and create efficient processes. Analytics is defined as the “extensive use of data, statistical and quantitative analytics, explanatory and predictive models, and fact-based management decisions and actions” (Davenport and Harris, p. 7., 2007). The use of analytics in our decision making doesn’t have to be complicated.
Using data gives us confidence in our strategies, approaches, and helps make clear decisions. Patil (2011) believes that “a data driven organization acquires, processes, and leverages data in a timely fashion to create efficiencies, iterate on and develop new products, and navigate the competitive landscape” (p. 2). This is exactly how research organizations and research universities can use data to drive innovation and research growth.
The use of data within the university is happening every day. Institutions use predictive analytics to project enrollment trends, provide growth incentives, and target specific student populations. However, the use of analytics and data-driven decision-making within the research administration profession and how data analytics can drive growth and success is still in its infancy. The aim of this national data collection was to survey practitioners across the research administration landscape at various institutions to learn more about how they use data analytics in their day to day operations and to meet their strategic objectives. This article provides a summary of findings from the survey data collection that occurred in the spring of 2019. The goal of this survey was to start a series of conversations related to the type of data analytics institutions currently use, who is responsible for collecting the needed data, tools used to reveal the stories within the data, whether institutions have a defined data strategy, and the value seen in sharing our data for greater overall insights.
The survey instrument included items that cover a variety of topics that include questions related to the value of using data-driven decision-making; usefulness in the data your institution collects; or has access to data and information. It asked: does your institution foster a data-driven culture, and what are the current relevant challenges you face when using data analytics within the research domain. The remaining sections of this article will summarize key findings.
The survey instrument was sent to a variety of listservs online through professional organizations and advertised in an article published in the SRAI CATALYST. Fifty-six responses were received from forty-eight named institutions:
- 41% (n=23) of the respondents primarily work in a pre-award office (central or dept/unit)
- 14% (n=8) were senior level administrators which include vice presidents, provosts, and chancellors
- 55% - The remaining 55% were evenly distributed between post-award offices, proposal development, compliance, and one respondent from technology transfer and commercialization
Responses came from both central administration staff and departmental level staff. The majority (68.9%) of those who responded have worked in research administration for at least 10 years. The majority of respondents provided contact information for additional survey follow-up. Figure 1. represents the variety in geography of respondents and institution size as represented by research and development expenditures from the Higher Education Research & Development survey conducted by the National Center for Science and Engineering Statistics, a division of the National Science Foundation.
Figure 1. Map of respondent institution location and size of their 2018 R&D expenditures as measures by the NSF HERD Survey.
Individuals responsible for data analytics tend to know who is requesting or in need of the data, but there is no unified view of customers or clear understanding of how this information and data analytics can impact the research domain. Research Administration offices are using data analytics to better organize the work and visualize strategic data to improve it, but there is no clear strategy on how to use data to drive change and growth at the majority of responding institutions. We acknowledge that the survey instrument itself assumed that institutions had a clear focus on using data analytics and metrics for decision making within their research domain.
Customers across campus tend to be satisfied with the result of efforts - it has increased the return on investment (ROI) in research and data teams can respond to needs faster. However, respondents acknowledge an almost fifty percent split between whether they trust the data quality in their data systems when it is used for decision making—even though they do find business value in having the information.
Data analytics efforts are supported by senior leadership, and there is a desire for offices to support the development and implementation of technical solutions to enhance data use and data-driven decision-making. Data teams have the right skills and colleagues are respected as experts in their specific areas. However, data analytics is only one part of their job responsibilities for those working with data in the research office - they have other responsibilities in research administration and very few institutions have full-time data staff within their research administration units. They either rely on other units on campus (Institutional Research) or add reporting to a research administrator’s duties.
The three biggest challenges reported include access to necessary business data, no centralized database, and inefficient data tools. Below are 10 findings from survey responses and some possible recommendations:
- Leveraging data analytics can be troublesome because we…
disagree or strongly disagree (68%) that they cannot identify valuable audiences…meaning they know who is requesting or needing the data... but 50% say they have no unified view of customers or how to support them.
- The use of data analytics and metrics ...
86% indicate that using data analytics and metrics helps them organize and visualize strategic data to improve their offices or research communities…and 59% say analytics/metrics lift campus customer satisfaction…59% indicate they increase return on investment in research and 77% believe analytics allows them to respond faster to customer needs.
- My institution has a clear strategy related to the use of data analytics to drive change and growth…
respondents are mixed related to a campus-wide strategy, but 52% disagree within research admin offices, meaning they do NOT have a clear strategy on the use of data analytics to drive change and growth within their area.
- Our data analytics are based on good quality data which is trusted throughout the institution/organization, 50% agree or strongly agree.
- Does your institution/organization have staff with the right analytical skill set to manage the data and find insights? Just over half (52%) answered Yes.
- Members of my team…
respect colleagues who are experts in their specific areas (57%)
However, to what extent do you agree that your data analytics efforts are supported by senior leadership, 52% agree or strongly agree.
- To what extent do you agree that our office should support the development and implementation of technical solutions that are enhancing how we use data to make decisions, 66% agree or strongly agree.
- Is reporting and data analytics their full-time job or just part-time, i.e. they are also responsible for something else? Sixty-eight percent indicate reporting is just part of their job. Most of the respondents also did pre- and post-award work.
- What are the main obstacles that prevent you from having access to all the datasets that are relevant to your work?
Top 3 categories Include:
No access / Univ politics-territorial 23%
Lack of centralized database 23%
Data extraction tools inefficient 15%
as a result, staff have challenges...
Respondents indicate that they know who their customers or stakeholders across campus are but lack the ability to properly support them. Research administrators, especially those in leadership positions should take an active role in understanding the research strategic plan for their institution and explore ways in which their office(s) can support units across campus. This may be in the form of supporting senior leadership (vice presidents, provosts, etc.) with improved data reporting related to the research domain or working with specific units to target areas of growth and opportunity. When organizations effectively use data analytics for decision making, they in return are creating a culture that fosters information seeking, accountability, ROI, and create better tools for competitive intelligence. When staff know and understand the strategic direction and vision, they are better able to look at their resources, available data, and provide better services and innovative approaches to improve.
In order to make sound business decision, institutions need to have reliable data sources, processes, and technical staff that can meet the needs of an ever-growing data culture. Research administrators should invest in improved data infrastructure and employer employees to learn new skills related to using data and information. In addition, large institutions, or those which have seen significant growth should invest in hiring talented staff that can provide data analytics to your stakeholders. Based on workload constraints and the need to process more and more proposal submissions and awarded projects—research administrators cannot be responsible for effective reporting or use of existing data if it is only one part of a larger list of duties. Investing in staff provides efficiencies, promotes culture change, and can impact growth strategies.
Finally, research administrators who work on data teams should have strong relationships with other data teams across campus. This may include central IT offices who manage enterprise systems. Having strong relationships can help build influence, accessibility to resources, and a wide range of data sources that can be used for strategic growth and analysis.
|Dr. Baron Wolf
University of Kentucky
Notre Dame University