Big data ana­lyt­ics is big now in the dig­i­tal world. Take a moment to think about all the data being col­lect­ed some­where every minute about people’s trans­ac­tions, pur­chas­es, com­plaints, book­ings, can­cel­la­tions, pref­er­ences and requests. The chance to har­ness all this infor­ma­tion to “make sense of things” is an oppor­tu­ni­ty too promis­ing to ignore. This explains why data min­ing and ana­lyt­ics gurus (the whole big data indus­try) are in high demand.

But it’s impor­tant to note that data, alone, skews toward the quan­ti­ta­tive; and what that data “means” and/or what it pre­dicts is heav­i­ly depen­dent on human insight. That is, the fact that mil­lions of peo­ple have switched to some kind of smart­phone has cer­tain­ly helped those in the com­mu­ni­ca­tions indus­try lay out some fair­ly sound pre­dic­tions about how many more smart­phones will be sold in the future. And the data relat­ed to cur­rent users can also make a strong case for the type of fea­tures peo­ple will be look­ing for months from now.

It’s crit­i­cal to note, how­ev­er, that a large mea­sure of “beyond the num­bers” insight is required to move beyond bits and bytes to under­stand the deeply human dimen­sions of emerg­ing behav­iours. That peo­ple are mov­ing to smart­phones is some­thing that can be sup­port­ed by lots of data. What a nation of smart­phone users means for tele­pho­ny and a lot of oth­er indus­tries requires a more human­is­tic under­stand­ing — because it requires a deep­er under­stand­ing of why peo­ple are pay­ing more, and hap­pi­ly, to use smart­phones.

Col­lect­ing data is a sci­ence; adding insight is more of an art. Put them togeth­er and you have what I call “oppor­tu­ni­ty fore­sense”, or the abil­i­ty to sense shifts in mean­ing (and even iden­ti­ty) that can then be exam­ined and pro­ject­ed. Big data mod­el­ling only makes the process and out­comes from an oppor­tu­ni­ty fore­sense exer­cise that much more pow­er­ful. What do you need to achieve oppor­tu­ni­ty fore­sense?

First, you need reli­a­bil­i­ty. For years, those in the busi­ness of design­ing prod­ucts and ser­vices have sought to have data col­lect­ed that proves that an inno­v­a­tive idea can become a reli­able inno­va­tion. In oth­er words, some­thing new must not be suc­cess­ful once or a few times, or just in a lab: it must be reli­able every­where it is intro­duced. Only reli­able process­es make for wor­thy invest­ments. Reli­able process­es have long been endear­ing to finance, engi­neer­ing and oper­a­tions pro­fes­sion­als because they gen­er­ate pre­dictable results. Fedex didn’t grow its busi­ness because it could deliv­er one pack­age “absolute­ly, pos­i­tive­ly overnight”. Over time, it demon­strat­ed the abil­i­ty to deliv­er lots of pack­ages. How many? Go to the Fedex web­site, and you’ll find the answer: “more than 3.5 mil­lion pack­ages and 11 mil­lion pounds of freight” (on aver­age, dai­ly) to “more than 220 coun­tries and ter­ri­to­ries.…”

Global delivery mapYet, if you were involved in a new busi­ness some­how tied to par­cel deliv­er­ies, all the data in the world about pack­age sizes, car­go weight and deliv­ery vans would not be enough to ful­ly under­stand the crit­i­cal role that human needs and desires serve as the cor­ner­stone of new busi­ness oppor­tu­ni­ties. For that, organ­i­sa­tions must also embrace design tenets that include “valid­i­ty” as an alter­na­tive met­ric to “reli­a­bil­i­ty” for deci­sion-mak­ing, espe­cial­ly in ear­ly stage ini­tia­tives and oth­er cir­cum­stances where shift­ing par­a­digms dis­rupt con­ven­tion­al wis­dom.

Valid­i­ty derives from a deep under­stand­ing of an oppor­tu­ni­ty (often in the form of a prob­lem) and its con­text. Valid­i­ty is dri­ven by a per­cep­tion of how things could be and a cor­re­spond­ing hunch which can­not yet be proven as reli­able. Valid­i­ty there­fore thrives in an unpre­dictable world, and is informed by human sens­es such as sight, touch, taste, smell and hear­ing as crit­i­cal per­cep­tions that frame exper­i­men­ta­tion in search of con­cept valid­i­ty. When pur­su­ing valid­i­ty, less empha­sis is placed on repli­cat­ing a process and more atten­tion is giv­en to con­firm­ing intu­itions. By focussing on the intu­itive prop­er­ties of valid­i­ty, organ­i­sa­tions are empow­ered to explore new sources of val­ue with rigour while man­ag­ing both the down­side risk and pre­serv­ing the upside poten­tial — a crit­i­cal bal­anc­ing act in most risk-averse organ­i­sa­tions.

The sto­ry of how Fedex came to be and the tri­als of its founder, Fred­er­ick Smith, is cap­tured here. But one anec­dote, from that source, helps me make this impor­tant point. “While attend­ing Yale Uni­ver­si­ty, Fred Smith wrote a paper on the need for reli­able overnight deliv­ery in a com­put­erised infor­ma­tion age. His pro­fes­sor found the premise improb­a­ble, and to the best of Smith’s rec­ol­lec­tion, he only received a grade of C for this effort, but the idea remained with him.”

Although many have prob­a­bly laughed at that professor’s lack of oppor­tu­ni­ty fore­sense, I would just say that he was right in prob­a­bly want­i­ng some reli­able data about the then-inno­v­a­tive idea of an overnight deliv­ery ser­vice. But, if he had want­ed to real­ly be help­ful to Smith, he would have probed hard about the foun­da­tion for Smith’s per­cep­tion that any­one actu­al­ly need­ed the ser­vice — and would pay for it. In oth­er words, the pro­fes­sor should have grad­ed Smith’s idea on reli­a­bil­i­ty and on valid­i­ty.

Only by under­stand­ing the dif­fer­ence between a reli­a­bil­i­ty ques­tion and one of valid­i­ty can entre­pre­neur­ial thinkers make informed deci­sions about the nature of a prob­lem. And, only by this dual-focus approach can they be sure that they have the the cor­re­spond­ing tools need­ed to gath­er the insights need­ed to design a new prod­uct, ser­vice or busi­ness and then think through how to per­form against the promise of the inno­va­tion.

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