Leaders at higher education institutions understand that the use of analytics can significantly transform the way they work by enabling new ways to attract current and potential students, improve student retention and completion rates, and even boost faculty productivity and research. However, many of these leaders are still unsure how to incorporate analytics into their operations and achieve the results and improvements they envision.
If used effectively, the enormous amount of information generated by higher education can enable institutions:
- to better understand the needs of students;
- to improve the quality of teaching, learning and counselling;
- to reduce costs; and
- to predict and avoid risks
The analysis tools exist.
But if the conditions for their effective use do not exist, the enormous volume of data does not produce concrete results. In fact, while artificial intelligence and machine learning make headlines, most universities lack that level of analytical sophistication.
According to IBM (IBM, 2016), more data have been created in the last two years than in all of humankind’s previous history.
But, the raw data are of limited use. To extract value from these data we need to refine, integrate and analyze them for understanding.
The potential benefits are too great to ignore.
Universities have tried to re-evaluate and reconfigure their business models in the hope of better serving students, communities and economies. These efforts to improve student outcomes while reducing costs have focused primarily on the large-scale adoption of programs, practices and services designed to optimize learning outcomes, shorten time to degree, reduce excess credit and streamline credit transfer, all while improving teaching, learning and advising in a cost-effective manner.
These seem like difficult but not impossible tasks.
Data analysis is at the heart of collecting the evidence and knowledge needed to achieve the transformational changes required. In recent years, sound data analysis has proven to be a key ingredient for strategic innovation.
- the higher education community is showing signs of embracing the analytical revolution, and
- that the data and analysis tools are abundant, the reality is that most institutions are not able to use them optimally, for several reasons:
- insufficient or misaligned resources,
- endless demands for information,
- disjointed or rigid infrastructure,
- limited skills and experience, and
- lack of trained executives to manage data
Any of these challenges can undermine the development of an analytical culture.
Analytics is the visual representation of the evaluated data. To transform it into visual representations, such as tables, charts and graphs, it is necessary to apply human criteria. Analytics is an informative tool that does not replace reflection, evaluation and decision making.
How can universities overcome existing barriers to harnessing the power of data analysis?
Many institutions would benefit from a solid data base based on accuracy, timeliness, relevancy, integration and security.
As the volume of data available increases, so do the pressures to use it, so it is important to develop procedures to ensure that it is of quality and usable in a contextualised way. There are multiple steps in the acquisition, processing and analysis of data. These include data discovery, extraction, reformatting, uploading, normalizing, enriching, comparing, presenting, and integrating workflow.
Data and information need to be delivered in a timely and accessible manner, otherwise their usefulness may be lost regardless of their accuracy. This is especially true for colleges and universities seeking real-time solutions to the challenges facing students. The longer it takes to acquire, process and analyse data related to the student’s life cycle, the less likely it is that knowledge can be used to predict risks and prescribe solutions.
The aim is to translate accurate and timely data into programs or services that support students and decision makers, but this is rarely achieved. Somehow there is so much data that it becomes difficult to separate the good from the bad. With so much data, it becomes more important to identify the right analysis tools and infrastructure. Analysts must be prepared to offer knowledge, products and services that are important to the end user.
Decision makers want access to information in near-real time, which means that the steps of data acquisition, processing and analysis must be done quickly. A major obstacle to providing accurate, timely and relevant insight has been a lack of integration. Difficulties in connecting data from disparate sources create a host of challenges, including differences in storage, definition, structure (or lack thereof), and intended use. Unstructured data, which can be incredibly rich, accounts for 90% of institutional and corporate data. This makes effective integration an important step.
We must protect and use data ethically. Policies and best practices on data privacy and security, intellectual property and ethical practices deserve careful attention. Analytical functions must adhere to best practices to maintain privacy and security, and create ethical review boards to mitigate the risks associated with the analytical revolution, large data, and predictive analysis.
There are also other factors to consider:
- The right infrastructure is needed to acquire, process and analyze data from various sources in a relevant and secure manner. In recent surveys on the main problems faced by the participating institutions
- more than half (57%) of respondents chose data governance as their main problem.
- The main problems were related to technology. These included data quality, data metadata and definitions, predictive analysis, data visualization, integration and self-service.
Investing in quality data, knowledge and infrastructure requires that higher education institutions reorient their cultures towards a collaborative model of data-based decision making. Without a culture of analysis, efforts to integrate analysis can lead to concerns about quality, the elimination of choice, the tracking of students, the cutting of programs and jobs, and the loss of institutional identity. Leadership must advocate the use of data and the linking of data analysis into a future vision focused on student success and institutional sustainability.
Improving student success
The main advantage of the analytical revolution is the success of the students. Some examples of predictive analytics results at various universities:
- by using a predictive grade model to place students in the courses that offered them the greatest chance of success.
- increasing graduation rates and reducing gaps in graduation rates for low-income, under-represented, first-generation students.
- decrease in credit hours at the end of the course.