As a linguist, I am fascinated with words, their meanings and use. So when I recently saw the words “learning analytics” in a conference paper title, I started thinking about the prevalence of the word “analytics” itself.
In the last decade, we have preceded “analytics” with many modifiers referring to concepts that are relevant to each of us as producers and consumers in the 21st century: data analytics, web analytics, marketing analytics, business analytics, predictive analytics, advanced analytics, text analytics, visual analytics – and now learning analytics. “Analytics” seems omnipresent: in emails, on social media, print ads, commercials, all around us, and ever-growing in popularity. Add to that SAS’ own recent release of Analytics 14.1 and I began to wonder: When did the term first start being used and in what context? How have the meaning and context changed over the years?
Dictionary.com puts the origins of the word in the 1590s (so the term itself is not as new as you may have surmised!). It means, according to Merriam-Webster, a “method of logical analysis.”
Stop right there.
Clearly, Merriam-Webster is behind the times, because it means a lot more to us today than just a method of analysis. So let’s turn to other sources for a more current definition. Dictionary.com and Wikipedia acknowledge the original meaning but add the object of analysis: data – often big data – and the purpose: deriving meaningful patterns. But even that does not quite capture the full meaning; there is more to the term “analytics” than statistical jargon – like logistic regression, for example. After all, I don’t know of any print ads, commercials or movies about the usefulness of logistic regressions, but I thoroughly enjoyed the movie Moneyball, which touts the value of analytics over gut feeling.
The most important part of the definition is what Wikipedia states as the purpose of analytics – “to describe, predict, and improve business performance” – since the most common application of analytics is for business data. The connection between analytics and its business uses is evident in this bubble chart, built with SAS Contextual Analysis from a sample corpus of online blogs, news, reviews and tweets mentioning analytics. In the contexts where “analytics” is mentioned, “business” and “business analytics” also figure prominently alongside “Google”/”web analytics” and “predictive analytics.”
But, I would argue, analytics has a much broader usage than just for business performance – it has come to be applied to performance in every sense of the word, as the phrases “sports analytics,” “performance analytics” and “learning analytics” surely prove. If you think about it, any area where optimal performance is desired could potentially benefit from “analytics,” i.e., data-analysis-driven decision making. To capture the meaning of the term in this day and age, I would propose looking to SAS’ own definition of analytics, which captures all of the crucial elements of why analytics is being easily adapted to nearly every domain: algorithms (methods of analysis), data and a purpose: solving problems and making the best decisions possible.
I would go a step further and make the claim that this purpose is easier to achieve with data visualization, which takes the old adage “a picture is worth a thousand words” to heart and illustrates complex statistical results with comprehensible images (read more about visualizations in this recent blog entry).
An example of translating complex analytics into a meaningful image is the word cloud below, created with SAS Visual Analytics, which shows the top 100 words from my analysis of Internet documents referring to analytics. The size of the font reflects the relative prominence of the term in the data (the corpus of documents).
As you can see by the words highlighted in yellow, this word cloud reinforces the idea that the value of analytics is to provide intelligence to model, track, predict, learn, know, understand, improve – in other words, to make better decisions for one’s company, organization, enterprise or industry. (As a fun challenge, try to locate concepts from the previous sentence in the word cloud).
Another method linguists use to trace language change, in addition to comparing formal definitions and corpus analysis methods illustrated above, is to zero in on how thought leaders in a domain use the language. One look at the recent Analytics Experience conference agenda also confirms that analytics is all about “transforming data into business value” and that visualization plays a large role in that transformational process.
How have you seen the term analytics applied and used recently? Have you noticed a shift in meaning from a method of analysis to a decision-making tool?