A majority of institutions are collecting data, but not all are making predictions or taking action in certain areas that are widespread with strategic priorities. This presentation takes a look at a Canadian distance education institution who took a “bottom-up” approach to developing analytics and how this approach relates to well-known analytics maturity index models.
Whether you are swimming up-stream or carrying out marching orders, finding a way to develop your institution’s analytics maturity is increasingly important for higher education. There has been a lot of research done looking into the state of analytics in higher education. Data use at most institutions was still limited to reporting. Analytics, as defined by Educause, is “the use of data, statistical analysis, and explanatory and predictive models to gain insight and act on complex issues” (Yanosky, 2015, p. 6). A majority of institutions are collecting data, but not all are making predictions or taking action in certain areas that are widespread with strategic priorities. Higher Education struggles to realize the potential that can come out of analytics and many institutions have a hard time finding the resources necessary to help it reach full potential.
There are barriers and challenges when it comes to developing analytics maturity. According to Bichsel[1] and Yanosky[2], analytics development has costs, including staffing, training, tools required for analytics. In a study conducted by Educause in 2012 (Yanosky, 2015), institutions struggle with things like future budget costs and inability to direct existing resources to analytics, largely due to a focus on reporting and meeting accountability requirements rather than addressing strategic initiatives. The study also found that there are also issues surrounding the data: Is the data of the right quality that it can be used properly? When is it the right time to collect data, should it be transactional or “frozen” data? Those who own the data may be reluctant or unwilling to share data necessary for analytics, not allowing for data to be centrally accessed. An unwilling culture is also a barrier to developing analytics. Fear or mistrust of institutional data, measurements, analysis, reporting and change in general. There is a concern that institutions may not know how to use data to make effective decisions. Lastly, there’s expertise needed to implement a proper analytics process. Analytics is more about the people than the data and the tools. It’s not just about the analysis component, but the logic and communication components of analytics. What is happening more often than not in Higher Education is tools are deployed without the proper platform or strategy, with the result being that expertise are often in low supply and underestimated.
There is apparent interest in analytics, but it is still not regarded as a major priority for institutions. Institutions know it’s important, but they may not know necessarily why it’s important or how to get there. Stephen Few, a leading expert in data visualization, said in August 2016[3]:
“A common problem among many professionals is the inability of expert practitioners to communicate with their clients...The solution to this problem begins with awareness.”
There is a need for awareness when communicating knowledge, in particular data, to those who need to understand it. It is a common problem for professionals (doctors, lawyers, statisticians, IT specialists, etc.) to speak in the “own language”, often unaware that it’s unfamiliar to those they speak it to. It can be difficult to refrain from common industry or professional jargon once you become fluent in it, and the result is you become unaware that others don’t speak the same language. This is where the skill of storytelling needs to be developed.
“Highly skilled statisticians are incredibly valuable, but only if they can explain their findings in understandable terms. This requires communications skills, both in the use of words and in the use of graphics. Training in these skills is every bit as important as training in statistics.”
-Stephen Few, Perceptual Edge
Those who do the work of data analysis must also know how to clearly present their findings to those who rely on the information to make decisions and take action. Tools such as Tableau[4] help make visualizing data easy for those working in analytics in Higher Education. It allows for those reporting and analyzing the data to be able to interpret it and present it various institutional stakeholders in a way that makes sense and is relevant to their needs.
This presentation takes a look at a Canadian distance education institution who took a “bottom-up” approach to developing analytics and how this approach relates to well-known analytics maturity index models. Using the Analytics Maturity Index set up by Educause[1], this presentation reviews how the work done at this institution lines up to the requirements of each category and hopefully provides others with lessons learned and ideas for improving their own analytics maturity.
[1] (Bichsel, 2012)
[2] (Yanosky, 2015)
[3] (Few, 2016)
[4] (Tableau, 2017)
[1] (EDUCAUSE, 2017)