The analytics challenges of tomorrow…
Have vendors and platforms made us lazy? The status quo for most of the industry is tools designed to sell to us on how, through simply installing their solutions, we’ll discover insight in our data which will make our businesses more successful.
However, we all know that it’s not that simple. There are large gaps between the processes of collecting data, applying business context, discovering insight, and producing impactful recommendations; and it’s our responsibility as analysts to bridge those gaps.
Frequently then, our job is to build, explore, pivot, contextualise, and to discover valuable insight for our businesses. So we spend our time hunting for trends, reacting to changing data points, and finding nuggets of value in pivot tables and segments. We discover nuance and intelligence, which we marry with our understanding of the organisations we work in, interpret, translate, and share in the form of insight and action.
We’re daring explorers and adventurers, in the height of the digital renaissance.
Tomorrow, everything changes
The threshold of cost, effort, and complexity to tag, collect, store, process and integrate will continue to reduce, and businesses will increasingly connect their systems into data warehouses. The level of complexity and scope of ‘big data’ that this will create in even small organisations will be incomprehensibly challenging to analyse.
And so, we’re about to hit a tipping point. It’ll become, comparatively, woefully inefficient for a human analyst to explore data in the way we currently do. The idea of opening up your analytics package and going hunting for insight will seem laughably naive, in retrospect, when you consider the capabilities of machine learning processes which will analyse our entire data sets to discover trends we’d never see, and make recommendations we’d never consider.
We’re not quite there yet, though
Today, systems like Google Analytics’ Intelligent Alerts are still generally limited to alerting you on statistically significant outliers; things like “Your pageviews-per-session for visitors from Italy on mobile devices was up 500% on Tuesday!”. If I don’t market in Italy, or if pageviews aren’t an impactful metric for my business, this might not be insight, and arguably, even useful information. The system lacks the business context it needs in order to make useful recommendations.
The next generation of tracking solutions, then, will begin to enable us to define business scenarios or criteria and our preferred actions. We’ll build rules like “if it’s raining whilst the consumer is browsing our holiday website, we should increase the propensity to show them summer holiday offers“, or “if sales volumes are decreasing, prioritise the volume of sales over direct revenue”. The systems will have access to the relevant data and will identify opportunities based on our criteria, as well as triggering direct action in connected systems. They’ll also learn from these scenarios and become increasingly self-managing when it comes to identifying desirable outcomes and presenting relevant insight and opportunities.
We’ll become teachers, tutors, and mentors, managing and understanding the data sets and inputs, and letting the machines do the heavy lifting. As this revolution unfolds, we’ll find ourselves becoming curators of systems and data sets, and the amount of time we spend doing actual analysis will reduce.
And so, the analysts of tomorrow will spend today thinking about how they define success scenarios and criteria. They’ll be considering how they codify business objectives and commercial context into rules, and planning out how they manage, get access to and connect the data sets they’ll need.
First published on Inked on 13th September, 2016