Cultivating a Data Culture in Higher Education

Kristina Powers, Angela E. Henderson, Kristina Powers, Angela E. Henderson

• Publisher: Routledge
• Year: 2018
• Print ISBN:
  9781138046801
• eBook ISBN:
  9781351694513
• Topic: Education
• Subtopic: Education General

Higher education institutions have experienced a sharp increase in demand for accountability. To meet the growing demand by legislators, accreditors, consumers, taxpayers, and parents for evidence of successful outcomes, this important book provides higher education leaders and practitioners with actionable strategies for developing a comprehensive data culture throughout the entire institution. Exploring key considerations necessary for the development of an effective data culture in colleges and universities, this volume brings together diverse voices and perspectives, including institutional researchers, senior academic leaders, and faculty. Each chapter focuses on a critical element of managing or influencing a data culture, approaches for breaking through common challenges, and concludes with practical, research-based implementation strategies. Collectively, these strategies form a comprehensive list of recommendations for developing a data culture and becoming a change agent within your higher education institution.

Part I

Importance, Planning, and Context

Chapter 1

Developing a Data Culture

Angela E. Henderson and Kristina Powers

WHAT IS A DATA CULTURE?

At the core level, a data culture focuses on the use of information to make sound decisions that help an institution attain a competitive gain. It is not a focus on numbers, but rather on effective use of resources to make advantageous decisions. Let us be clear that a data culture is not a reliance on figures at the expense of individuals with subject matter expertise within the institutions.

To some, the term “data” itself implies a dehumanizing stigma; however, effective data use relies entirely on the knowledge, context, and interpretation of institutional stakeholders. Data is not dehumanizing, but seeks to bring stakeholders together to a shared understanding and communication of institutional information. A data culture is not a culture of numbers or accountability, but rather one of awareness resulting from alignment and entwinement of institutional environment, individuals within the environment, and actions, contextualized through the lens of data (Figure 1.1). In this environment, through cultivation and involvement, data becomes information.

With increasing federal, state, and external reporting expectations, data has become synonymous with accountability in higher education. It must be noted that a culture of “accountability” and culture of data are not the same thing. A culture of accountability is often prompted by external forces such as accreditors or reporting mandates. Although such a culture may be effective in the short term, it is difficult for an organization to develop and advance due to the impetus stemming from external forces rather than internal ones.

Stakeholders tend to be resistant to the requirements forced upon them, cutting into their already busy schedules. If there is no evidence of accountability data being used to make improvements, stakeholders become resentful. Further, accountability initiatives tend to focus on specific metrics of interest to external bodies rather than cohesive data of benefit to the institution and ultimately student success. This narrow focus ignores data, which may be valuable in a broader context.

FIGURE 1.1 Elements of Data Culture.

Lastly, a culture of accountability often involves only individuals at the upper levels of the institution and does not encourage data integration and discussion throughout the institution. In contrast, a data culture seeks to democratize and distill data into information to guide decision-making that focuses on the institution’s mission.

WHY CULTIVATE A DATA CULTURE?

Benefits

While large companies that have arguably earned the title of “data leader” such as Google, Amazon, Facebook, LinkedIn, Walmart, FedEx, UPS, and General Electric rely on data to maintain a competitive edge, this approach has not been common in higher education, despite clear advantages to be gained in improved competitiveness from using data. Benefits of a strong data culture include improved communication and understanding of institutional metrics and trends.

While many people do not like to discuss the revenue side the equation, the fact is that presidents are expected to have a balanced budget. And no senior leader is pro-deficit. Organizations that successfully use data realize more growth than their counterparts that are not using data effectively.

For example, “Retailers who leverage the full power of big data could increase their operating margins by as much as 60%” (Marr, 2015, p. 1). Consider a public health example, “Estimates suggest that by better integrating big data, healthcare could save as much as $300 billion a year—that’s equal to reducing costs by $1000 a year for every man, woman, and child” (Marr, 2015, p. 1). Given that organizations are achieving these results using 0.5% of collected data is amazing (Marr, 2015). Imagine the progress higher education institutions could make in the areas of student success and cost containment by using a fraction of the remaining 99.5% of collected data.

The public outcry over increased higher education costs has led to greater scrutiny of institutional resources. Thus, there is not only an incentive to having a strong and positive data culture, but there is also a risk of not having a data culture.

Risks of Not Having a Data Culture

Institutions tend to be data rich and information poor, awash with data points but yet lacking processes to transform that data into actionable insight. This disconnect prompts decision makers, who don’t have time to devote to unraveling the web of data, to make choices based on isolated strands of data drawn from the mass. In short, institutions without a data culture make decisions without knowing the whole story. Such decisions have the potential to have substantial impact upon students, faculty, staff, alumni, and prospective students, to name just a few. Without a cohesive data culture in place, decisions with widespread implications are made in isolation and campus mythologies seep into the decision-making process, influencing outcomes.

CHARACTERISTICS OF A SUCCESSFUL DATA CULTURE

A successful data culture exhibits four key behaviors, it: 1) shares data, 2) values data, 3) trusts data, and 4) uses data. Figure 1.2. shows example strategies for each of these characteristics.

Shares Data

Transparency and access to data are key characteristics of a successful data culture. Patil and Mason (2015) advocated “everyone in an organization should have access to as much data as legally possible” (p. 6). In an environment where access to data is easily granted rather than withheld, stakeholders are more likely to feel empowered to use data. Of course, granting access alone is only one piece of the equation; data themselves must be accessible to users as well. In this respect, it is critical that data systems and reports allow intuitive interpretation. Lacking this, stakeholders may feel that while they can access the data, they can’t make sense of it, prompting them to retreat from data use. For infrequent data users, data should be humanized and contextualized to foster understanding.

FIGURE 1.2 Characteristics and Behaviors of Successful Data Cultures.

Values Data

Integration of data review in recurring meetings maintains an emphasis on the importance of data at the institution. Rather than simply providing attendees with a report, time is set aside at the start of each meeting for data review and discussion. This ensures not only awareness of current data, but also that all involved have a shared understanding and interpretation of the data. This practice reiterates that data are an inherent part of discussions and decision-making. Value can be further demonstrated by the creation of a dedicated chief data officer role within the institution. This not only elevates the importance of data to an executive level, but also ensures that an individual with data proficiency is included in key discussions.

Trusts Data

Trust is critical to a successful data culture. Without it, gaining buy-in from institutional users is nearly impossible. Institutions with strong data cultures understand that data quality is the key to establishing broad data use. Weathington (2016) acknowledged lack of trust in data quality as “the number one reason why people are reluctant to rely on data” (p. 1). Trustworthy data do not happen overnight or without effort. Resources must be devoted to data cleansing and validation and supported by processes that emphasize the importance of data quality. In the absence of such processes, we return to the “garbage in, garbage out” mantra.

Validation of multiple data sources is another substantial task, but one that must be undertaken to ensure consistent data quality. While definitional issues will always result in a slight variation across reports, efforts should be made to ensure that these variations are clearly labeled and explained. This strengthens the perception that although data may differ, there is a legitimate reason for the variation. Whenever possible, data providers should use shared definitions to ensure consistent data.

Uses Data

All the effort of sharing and validating data is wasted if the data are not used. As Patil and Mason (2015) observed, many entities fall victim to the “if we build it, they will come” attitude, expecting data development work to result in immediate and extensive use. For data to be used, and used effectively, it must meet the needs of institutional stakeholders.

ASSESSING INSTITUTIONAL READINESS FOR A DATA CULTURE

Now that we have highlighted the advantages to establishing a data culture, the next obvious question is how to begin such an undertaking. It must first be noted that a data culture is not defined solely by the presence of data across an institution. While collecting data is simple, “it’s what your organization does with data, however, which defines the culture” (Stringfellow, 2016, Harris section, para. 3). Prior to implementing steps toward a data culture, it is necessary to evaluate the current level of data literacy. One way to gain an understanding of the current state of the culture is to survey the campus community regarding its perception and use of data. Some sample questions to guide assessment of the current culture are shown in Figure 1.3. As discussed in depth in Chapter 2, understanding institutional culture and perceptions is critical. Conducting an audit to gain insight into existing perceptions regarding data not only inherently validates data-informed planning (what could be more data-centric than collecting data on data use to inform planning of a data culture?), but also provides baseline data for future assessment.

Perceptions of data:

■ I think it is important to use data to inform decision-making.

■ I like to use data.

■ I feel comfortable using data.

■ I feel comfortable interpreting data.

■ I find data confusing and difficult to understand.

■ I know where to get data necessary for my position.

■ I have access to all the data I need to do my job.

■ Data I have access to are easy to use.

■ Data I have access to are easy to understand.

■ Data available to me are useful.

■ I know who to ask if I have questions about data.

■ I receive sufficient training on data use.

■ I prefer to receive data as a finished report.

■ I prefer to receive raw data and analyze it myself.

Frequency of data use:

■ How often do you use each of the following campus data systems/reports?

■ Student information system

■ HR information system

■ Course scheduling system

■ Reports created by Information Technology

■ Reports created by Institutional Research

■ What are your primary concerns or frustrations with existing data systems and structures?

FIGURE 1.3 Questions to Guide Assessment of the Current Data Culture.

Data collected from such an audit can be used to identify areas within the institution that will require a significant allocation of time and resources in the pursuit of a data culture. All units within the institution must be led to see the value of data and making decisions from a data evidenced perspective; some will require more guidance in this endeavor than others. Stringfellow (2016) noted that individuals faced with a shift to a data culture tend to have two chief concerns: “the first is that they will be scrutinized (and potentially punished) because everything is being measured more publicly. The second is the concern that becoming more numbers-focused will cause it to feel like a less personal work environment” (Marcotte section, para. 2).

While a strong data culture advantages all institutional stakeholders, do not expect overwhelming support at the outset of the initiative; Weathington (2016) suggested that approximately three-quarters of stakeholders do not place a high value on data initially.

ORGANIZATIONAL CHANGE

Cultivation of a data culture is most easily grounded in a teleological frame of organizational change. That is, organizational change which is intentional and flexible. ...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright page
• Dedication
• Table of Contents
• List of Figures
• List of Tables
• Preface
• Acknowledgments
• PART I Importance, Planning, and Context
• PART II People, Leadership, and Relationships
• PART III Perceptions, Usability, and Communication
• PART IV Putting the Culture Pieces Together
• Biographies
• Index