“Analytics” certainly is a buzz word in the business world and almost impossible to avoid at any venue where the relationship between technology and post-compulsory education is discussed, from bums-on-seats to MOOCs. We do bandy words like analytics or cloud computing around rather freely and it is so often the case with technology-related hype words that they are used by sellers of snake oil or old rope to confuse the ignorant and by the careless to refer vaguely to something that seems to be important.
Cloud computing is a good example. While it is an occasionally useful umbrella term for a range of technologies, techniques and IT service business models, it masks differences that matter in practice. Any useful thinking about cloud must work on a more clear understanding of the kinds of cloud computing service delivery level and the match to the problem to be solved. To understand the very real benefits of cloud computing, you need to understand the distinct offerings; any discussion that just refers to cloud computing is likely to be vacuuous. These distinctions are discussed in a CETIS briefing paper on cloud computing.
But is analytics like cloud computing, is the word itself useful? Can a useful and clear meaning, or even a definition, of analytics be determined?
I believe the answer is “yes” and the latest paper in our Analytics Series, which is entitled “What is Analytics? Definition and Essential Characteristics” explores the background and discusses previous work on defining analytics before proposing a definition. It then extends this to a consideration of what it means to be analytical as opposed to being just quantitative. I realise that the snake oil and old rope salesmen will not be interested in this distinction; it is essentially a stance against uncritical use of “analytics”.
There is another way in which I believe the umbrella terms of cloud computing and analytics differ. Whereas cloud computing becomes meaningful by breaking it down and using terms such as “software as a service”, I am not convinced that a similar approach is applicable to analytics. The explanation for this may be that cloud computing is bound to hardware and software, around which different business models become viable, whereas analytics is foremost about decisions, activity and process.
Terms for kinds of analytics, such as “learning analytics”, may be useful to identify the kind of analytics that a particular community is doing but to define such terms is probably counter-productive (although working definitions may be very useful to allow the term to be used in written or oral communications). One of the problems with definitions is the boundaries they draw. Where would learning analytics and business analytics have boundary in an educational establishment? We could probably agree that some cases of analytics were on one side or the other but not all cases. Furthermore, analytics is a developing field that certainly has not covered all that is possible and is very immature in many industries and public sector bodies. This is likely to mean revision of definitions is necessary, which rather defeats the object.
Even the use of nouns, necessary though it may be in some circumstances, can be problematical. If we both say “learning analytics”, are we talking about the same thing? Probably not, because we are not really talking about a thing but about processes and practices. There is a danger that newcomers to something described as “learning analytics” will construct quite a narrow view of “learning analytics is ….” and later declaim that learning analytics doesn’t work or that learning analytics is no good because it cannot solve problem X or Y. Such blinkered sweeping statements are a warning sign that opportunities will be missed.
Rather than say what business analytics, learning analytics, research analytics, etc is, I think we should focus on the applications, the questions and the people who care about these things. In other words, we should think about what analytics can and cannot help us with, what it is for, etc. This is reflected in most of the titles in the CETIS Analytics Series, for example our recently-published paper entitled “Analytics for Learning and Teaching“. The point being made about avoiding definitions of kinds of analytics is expanded upon in “What is Analytics? Definition and Essential Characteristics“.
The full set of papers in the series is available from the CETIS Publications site.