So much has changed in our dig­i­tal land­scape in recent years. What start­ed as a quest to cap­ture data and get a view of how the busi­ness is per­form­ing has become a plat­form for con­tin­u­ous learn­ing. Peo­ple and machines now can work togeth­er to con­tin­u­ous­ly uncov­er new – and hope­ful­ly bet­ter – ways to get things done.

The Inno­va­tion Mystery

But I often won­der: Is this new world order real­ly about inno­va­tion? Or are we real­ly just invent­ing or improv­ing? Accord­ing to Bob Park­er, group vice pres­i­dent of IDC, and Yaad Oren, vice pres­i­dent of Cor­po­rate Strat­e­gy at SAP, machine learn­ing may be one of the lat­est tech­nolo­gies to help deci­pher this mystery.

“Busi­ness­es have tra­di­tion­al­ly invest­ed in point solu­tions to solve par­tic­u­lar chal­lenges, such as fraud detec­tion for a cred­it card process,” Park­er observed dur­ing the Amer­i­c­as’ SAP Users’ Group (ASUG) web­cast Learn­ing to Inno­vate by Inno­vat­ing from Learn­ing. ​“Now with machine learn­ing, they are mov­ing away from a point-solu­tion view of the world to cre­ate a deci­sion­ing environment.”

Machine Learn­ing: Turn­ing Small Projects Into Enter­prise Transformations

3M Com­pa­ny, one of the most respect­ed inno­va­tors of the old and new econ­o­my, has famous­ly main­tained a rep­u­ta­tion for churn­ing out one suc­cess­ful inno­va­tion after anoth­er. This track record is main­ly depen­dent on its dis­tinc­tion between inven­tion and inno­va­tion. The com­pa­ny describes ​“inven­tion” as that eure­ka moment of sud­den dis­cov­ery when the prover­bial light bulb over the head turns on, while ​“inno­va­tion” is con­sid­ered the con­tin­u­ous improve­ment that comes from learning.

Aug­ment­ed and Auto­mat­ed Decision-Making

If we accept the 3M asser­tion, we also have to agree that a process can ben­e­fit from a sys­tem. This is where machine learn­ing fits in so well. ​“Things are mov­ing so quick­ly that we can’t rely on our inter­nal experts to wade through data on their own to make a deci­sion,” advised Park­er. ​“We have to aug­ment – and in some cas­es, auto­mate – that process through the appli­ca­tion of machine learning.”

One of the most sig­nif­i­cant chal­lenges to lever­ag­ing machine learn­ing is scal­ing inno­va­tion from a small depart­men­tal project to an enter­prise-wide dig­i­tal trans­for­ma­tion. Oren sug­gest­ed, ​“Busi­ness should nev­er stop learn­ing how to inno­vate. A vari­ety of skills and ser­vices are need­ed to cap­i­tal­ize on the lat­est tech­nolo­gies and the mod­els to dis­cov­er capa­bil­i­ties that offer dif­fer­en­ti­at­ing value.”

How to Over­come Tal­ent Shortages

Con­sid­er­ing the cur­rent fer­vor around data sci­ence, peo­ple who are experts in the recent crop of dig­i­tal tech­nolo­gies and best prac­tices are in high demand. Unfor­tu­nate­ly, there is a short­age of this valu­able tal­ent. With machine learn­ing, busi­ness­es can over­come that com­mon chal­lenge by get­ting access to the algo­rithms and capac­i­ty for han­dling large vol­umes of data to sup­port inno­va­tion process­es. This is how orga­ni­za­tions can tru­ly use data to dri­ve sig­nif­i­cant com­pet­i­tive advantage.

Rapid Prob­lem-Solv­ing Dri­ven by Machines

The con­cept of data-dri­ven deci­sion-mak­ing may have been around for years, but rapid inno­va­tion in machine learn­ing is unleash­ing tremen­dous busi­ness poten­tial. Thanks to this dig­i­tal oppor­tu­ni­ty, deci­sion-mak­ers can gain a bet­ter under­stand­ing of every­thing their busi­ness touch­es and affects while solv­ing com­plex prob­lems in real time.

Dis­cov­er how your busi­ness can lever­age data to auto­mate sus­tain­able inno­va­tion. Watch the Amer­i­c­as’ SAP Users’ Group (ASUG) web­cast record­ing Learn­ing to Inno­vate by Inno­vat­ing from Learn­ing, fea­tur­ing Bob Park­er, group vice pres­i­dent of IDC, and Yaad Oren, vice pres­i­dent of Cor­po­rate Strat­e­gy at SAP.