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.