Analytics might be defined as people asking questions and deriving answers using data. Even as our computational and data capability has transformed in the last 25 years, that definition needs little alteration.
Analytics truly is an ancient and fundamentally human activity, now amplified and augmented by modern computing capabilities. The essential algorithms and processes used in analytics have changed far less than our capability to execute them. If you look at textbooks from the early 1990’s, most elements of current analytics thinking are there.
And now, almost anyone with an interest and internet access can apply analytics tools, processing, and thinking to their daily activities. What was once the province of a relative few with access to arcane and difficult-to-use tools is now widely, and almost freely, available.
Is that good? Absolutely. The more, the merrier. If I could, I’d invite anyone working with data and an interest to Columbus for a one-week short course in exploratory analysis. That short course wouldn’t make people expert at deriving any analytics answer, but it would make people aware of the questions analytics addresses, and some of the thought process in addressing those questions. And that’s a start.
A great contribution from analytic thinking is that it makes for better discussion and problem-solving all around. Good analytics works in the realm of verifiable facts – the invariable basis for informed discussion. The alternative is to ignore analytics tools and processes, resulting in a continuation of the trivial arguments which pass for much of discussion today. Can people hurt themselves using complex tools when they are just starting? Sure – trust me, I’ve been there. But that’s OK – mistakes in analytics are part of analytics, and working within the process is ever so much better than working outside of it.
Analytics also improves dialogue through its fundamental recognition of limits, and its sometimes irritating dismissal of absolute truths. The first duty of analytics is frequently to identify the limits of analytics outcomes themselves – while we may not discuss that often, it’s fundamental to analytics nonetheless. Analytics tells us that we don’t really know how good our knowledge is until we break it – every model, every theory, every data set, every process has its limits. Finding those limits is frequently the topic of good and creative analytics work.
Beyond better dialogue, why is it desirable for more people to apply more analytics more-or-less all of the time? Because analytics results depend on context – biases, uncertainties, nuances of question, interpretation of answers, implicit metadata, and the entire universe of subject-matter knowledge – and those supplying that context are the ideal people to apply analytics in the furtherance of knowledge and ideas, rather than data experts. Applying analytics without the nuances of problem context is like using a chain saw to trim a tree, based only a rough idea of what a tree should look like. Context and problem knowledge can, should, and do rule the problem-solving process – if you like, it’s data we can’t do without.
Then do analytics experts matter? Of course. They matter in the same way that experts in storage, in databases, in visualization, and application development, or a score of other data-related disciplines matter – as experts helping people to understand and solve data-related problems. But integration, context, and collaboration are the order of the day, if we’re to move forward, and I’ve minimal patience for the idea that data science, or data scientists, (or any other technical discipline) somehow stands apart or even above the general flow of problem-solving progress. 80% percent of the analytics problems are solved by 20% if the techniques, and everyone everywhere should be encouraged to use those techniques whenever and wherever they can – in database design, in data assessment, in performance tuning – you name it.
There really is so much to accomplish, and analytics can help with accomplishing it – this time, more really is better.