What are analytics?
I’ve worked in information tech for five years now and when I hear the word analytics, I think I know what it means. If I had to explain analytics to someone, I’d say it’s when you use a tool to examine data in order to find insights – patterns or trends – that help you explain something or help make a decision. The tool that first comes to my mind is SPSS – simply because that’s what I’ve had experience using and what I’ve had customers use.
But this question – What are analytics? – is hard to answer definitively because there are many different kinds of tools for many different kinds of analyses, using many different kinds of data for many different purposes. I also think that because today we’re talking about software, that capabilities and how they’re delivered are always changing, being improved, and being used for new things, so much that the idea of analytics itself is always under construction.
In any case, there a few other stabs at what analytics means:
- Wikipedia: “Analytics is the discovery, interpretation, and communication of meaningful patterns in data.”
- Competing on Analytics: “The extensive use of data, statistical & quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions.”
- Gartner: “analytics leverage data in a particular function process (or application) to enable context specific insight that is actionable”
- Online Analytics: “using existing business data or statistics to make informed decisions”
- John Jordan: “uses statistical and other methods of processing to tease out business insights and decision cues from masses of data” (Ch. 29)
(I think I like Wikipedia’s best)
Examples of analytics aren’t hard to find though. A business intelligence tool like Cognos or Oracle BI (or a similarly capable and structured dashboard) that provides reports on historical or current data and allows the user to explore that data is one kind of analytics called Descriptive. Other tools like the aforementioned SPSS that do forecasting, modeling and rule based systems are called Predictive – and add to that optimizations, some forms of Machine Learning and recommendations engines and you have Prescriptive. Finally when you add things like machine reasoning then you get to the “cognitive computing” realm, which is where it ends for the vast majority of us today.
One thing I like to consider is that what so many people do with analytics software today they could technically do with a pencil and paper – and a lot of time. It’s the relatively low cost today to buy the analytics tools and ability to do a lot at once, and quickly, that makes these definitions the way they are today.
So what might all this mean for you? If you use a Fitbit or other similar wearable device, you’re likely to see analytics about your steps today, or how many swims you’ve completed over time. If you’re an accomplished Excel user, you might be turning spreadsheets into newer and more purpose-built visualizations that allow you to better understand the information and pick out anomalies – those are both analytics driven. The point is that wherever data lives, analytics can be valuable to help find relevant insights.