Power of data

The Smart Manager - - Contents - DR AB­HISHEK NARAIN SINGH IS AS­SIS­TANT PRO­FES­SOR (IN­FOR­MA­TION MAN­AGE­MENT) AT IN­STI­TUTE OF MAN­AGE­MENT TECH­NOL­OGY NAG­PUR

Un­der­stand­ing data at hand is a must for busi­ness growth, opines Dr Ab­hishek Narain Singh, In­sti­tute of Man­age­ment Tech­nol­ogy Nag­pur.

When it comes to us­ing data to drive busi­ness, or­ga­ni­za­tions such as Google or Face­book are iconic… When they started in 2007, big data was not what it is to­day. All four Vs that de­fine big data—vol­ume, va­ri­ety, ve­loc­ity, and ve­rac­ity—were at lower lev­els. But per­haps more im­por­tantly, there was not much pre­vi­ous ex­pe­ri­ence of work­ing with big data and us­ing it to drive de­ci­sion mak­ing in or­ga­ni­za­tions. At that time, the ques­tion was still out as to whether hav­ing all that data is use­ful. To­day, the feel­ing is that the value of data has been proven, and it’s more of a ques­tion of how to get it. 1 Dili­gent busi­nesses are those that en­sure data pri­vacy and se­cu­rity while lev­er­ag­ing its mul­ti­ple ad­van­tages.

In the world we live to­day, data is king. Many economists be­lieve that ‘data to this cen­tury is what oil was for the last cen­tury’—a driver for growth, change, and suc­cess. The find­ings of The Dig­i­tal Re­alty Data Econ­omy Re­port2, a study by Devel­op­ment Eco­nom­ics, sug­gest that there is huge po­ten­tial for the data econ­omy to grow fur­ther, boost­ing busi­nesses and cre­at­ing more jobs. As per the re­port, the size of the data econ­omy of Ger­many ac­counts for ¤108.3 bil­lion, cre­at­ing 1.95 mil­lion jobs, and has an un­tapped po­ten­tial of ¤87.9 bil­lion. The data econ­omy con­trib­utes $1 tril­lion to the US econ­omy every year. The num­bers look promis­ing and en­cour­ag­ing enough to have strong faith in the power of data.

Ac­cord­ing to a May 2018 Forbes ar­ti­cle, the amount of data cre­ated every sin­gle day amounts to 2.5 quin­til­lion bytes. To put it an­other way, al­most 90 per­cent of the data in the world was gen­er­ated over the last two years. Now, one can imag­ine the speed and vol­ume of data be­ing gen­er­ated through in­nu­mer­able sources like IoT, sen­sors, wear­able de­vices, tweets, YouTube videos, mo­bile com­mu­ni­ca­tions, chats, pic­tures, emails, blogs, Skype, print me­dia, TV, smart de­vices, and so on. To give some statis­tics, Google pro­cesses more than 40,000 searches every sec­ond (ie, 3.5 bil­lion searches per day), 456,000 tweets are sent and 4,146,600 YouTube videos are watched per minute; and every minute, 154,200 Skype calls are made, 156 mil­lion

emails are sent, 16 mil­lion text mes­sages are writ­ten, and 15,000 GIFs are sent via Face­book mes­sen­ger. On top of this, we have hu­mon­gous amounts of data gen­er­ated through plat­form-driven ser­vices like Uber, Venmo, and Spo­tify. Pre­cisely, big data is get­ting big­ger and big­ger day by day in vol­ume, ve­loc­ity, va­ri­ety, ve­rac­ity, and ‘value’.

Ac­cord­ing to es­ti­mates, by 2020, 15 to 20 per­cent of global GDP will be based on data flows. It is be­lieved that by 2022, the size of the dig­i­tal econ­omy in In­dia will be ap­prox­i­mately $1 tril­lion, and it could con­sti­tute al­most 50 per­cent of the en­tire econ­omy by 2030. The In­dian govern­ment’s thrust on Dig­i­tal In­dia and ecom­merce space are the build­ing blocks for this. In such a sce­nario, en­trepreneurs and even es­tab­lished busi­nesses con­stantly look out for new op­por­tu­ni­ties and unique value propo­si­tions for cus­tomers. Paytm, Ola, and Big­Bas­ket are a few ex­am­ples. With all this abun­dance of data get­ting gen­er­ated all around, the vi­tal ques­tion that a de­ci­sion­maker has in mind is: how to make sense of it? How this mas­sive data (struc­tured and un­struc­tured) gen­er­at­ing from mul­ti­ple touch­points in dif­fer­ent for­mats, of­fers in­sights into the prod­ucts or ser­vices of­fered by the com­pany and help man­agers sense the pulse of cus­tomers, which may en­able them to make the right move at the right time?

turn­ing data into in­sights

It of­ten hap­pens that or­ga­ni­za­tions have a lot of data (in­ter­nal as well as ex­ter­nal col­lected through a range of sources), but they do not know how to process it to gain value. Ha­bit­u­ally, they col­lect data they them­selves do not know what to do with. At times, ei­ther it is a man­date from the head of­fice or a reg­u­lar, not-so-re­quired ex­er­cise. Many a time, this job is out­sourced to a mar­ket re­search agency, sup­pos­edly an ex­pert in con­vert­ing data into in­sights, with­out ex­pect­ing much in re­turn. How­ever, if fol­lowed cor­rectly with mind­ful ef­forts, the jour­ney of con­vert­ing data into in­sights can be an en­rich­ing ex­er­cise for any or­ga­ni­za­tion. Lead­ing to a data-driven dis­cov­ery—find­ing hid­den pat­terns and un­usual cor­re­la­tions—this may help busi­nesses and de­ci­sion-mak­ers know the un­known.

Ir­fan Ka­mal, Se­nior Vice Pres­i­dent, So­[email protected] in his Har­vard Busi­ness Re­view ar­ti­cle ‘Met­rics are easy, in­sight is hard’, ar­gues that in con­trast to abun­dant data, in­sights are rel­a­tively rare. In the con­text of mar­ket­ing, he sug­gests a four-step mar­ket­ing data-cen­tered process: 01 col­lect, 02 con­nect, 03 man­age, and 04 an­a­lyze and dis­cover. He fur­ther rea­sons that brands and com­pa­nies that are able to de­velop in­sights from any level of data will be win­ners. Thanks to dis­ci­plines like data science, busi­ness in­tel­li­gence, and big data and an­a­lyt­ics, or­ga­ni­za­tions are equipped with a va­ri­ety of tools to map data with busi­ness re­quire­ments and out­comes.

Data-driven de­ci­sion­mak­ing has mul­ti­ple ad­van­tages, which make busi­ness pro­cesses more ag­ile and help man­agers make bet­ter­in­formed de­ci­sions.

There have been suc­cess­ful cases where data and in­tel­li­gent tech­niques (de­rived from past data and ex­pe­ri­ences) put to­gether have solved prob­lems in map­ping crime, dis­as­ter man­age­ment, mar­ket­ing cam­paigns with greater ac­cu­racy, pre­dic­tions with re­spect to con­sumer de­mand/pref­er­ence and po­si­tion­ing of­fer­ings ac­cord­ingly, pro­vid­ing govern­ment schemes and ser­vices more ef­fec­tively, and many more.

data man­age­ment and de­ci­sion-mak­ing

Un­like ear­lier, when hard­ware and later soft­ware used to drive (or dom­i­nate) the en­tire knowl­edge dis­cov­ery process, or­ga­ni­za­tions have now re­al­ized that data is at the core of their busi­ness. In any given sit­u­a­tion, avail­abil­ity of rel­e­vant data and re­quired ca­pa­bil­i­ties to an­a­lyze it en­able bet­ter de­ci­sion-mak­ing. In­stead of re­ly­ing on gut feel­ing—which may have the lim­i­ta­tions of over- or un­der­es­ti­ma­tion, wrong judg­ment, or bi­ases—man­agers pre­fer to go with ‘what data says’. This also helps them ra­tio­nal­ize their de­ci­sions and the de­ci­sion-mak­ing process. Chances of er­ror get min­i­mized and you are ready to deal with a range of sce­nar­ios, thanks to sim­u­la­tions, pre­dic­tive mod­el­ing, and sen­si­tiv­ity anal­y­sis. Datadriven de­ci­sion-mak­ing has mul­ti­ple ad­van­tages, such as bet­ter un­der­stand­ing of the sit­u­a­tion at hand (due to the avail­abil­ity of his­tor­i­cal data), as­sess­ment of al­ter­na­tive so­lu­tions (on key per­for­mance in­di­ca­tors), and map­ping it against the best pos­si­ble out­come (bench­mark­ing), which make busi­ness pro­cesses more ag­ile and help man­agers make bet­ter-in­formed de­ci­sions.

To deal with the un­prece­dented speed at which data is be­ing gen­er­ated, cap­tured, stored, and dis­sem­i­nated, and gain an edge over oth­ers, or­ga­ni­za­tions need a ro­bust data man­age­ment strat­egy. Ul­ti­mately, in this age of cut­throat com­pe­ti­tion, one who has the right data can make the right de­ci­sions and win the game. It is piv­otal for busi­nesses of this age to source data from mul­ti­ple touch­points—wher­ever there is a po­ten­tial cus­tomer and which­ever medium he or she en­gages with—and col­late it to get a holis­tic view for de­vis­ing strat­egy. The role of the Chief Data Of­fi­cer (CDO) be­comes very im­por­tant in this re­spect. He or she is re­spon­si­ble for putting a data gov­er­nance struc­ture in place. As data de­rives value for any de­ci­sion, its man­age­ment and safe­keep­ing are cru­cial.

data pri­vacy chal­lenges

It is good to have a heap of data to take bet­ter and in­formed de­ci­sions for your busi­ness and cus­tomers. But, at times, com­pa­nies col­lect too much of user data (that too with­out their con­sent) in the name of pro­vid­ing them con­ve­nience—for ex­am­ple, serv­ing rel­e­vant ads and tar­geted pro­mo­tional com­mu­ni­ca­tions. In the dig­i­tal age, many be­lieve data pri­vacy is a myth. Tim Cook, CEO of Ap­ple Inc., in his key­note speech at the 40th In­ter­na­tional Con­fer­ence of Data Pro­tec­tion and Pri­vacy Com­mis­sion­ers held in Brus­sels in Oc­to­ber 2018, em­pha­sized the fact that data it­self is be­ing weaponized against peo­ple and so­ci­eties, ar­gu­ing that ‘pri­vacy is a fun­da­men­tal hu­man right’ and that trade in dig­i­tal data has ex­ploded into a ‘data in­dus­trial com­plex’. Or­ga­ni­za­tions need to be cau­tious while col­lect­ing users’ data and while shar­ing it with third par­ties. Cook strongly voiced the need for a pri­vacy law that pri­or­i­tizes data min­i­miza­tion—min­i­mum users’ data to be col­lected by com­pa­nies, trans­parency, right to ac­cess, and right to se­cu­rity. In the dig­i­tal econ­omy, which thrives on users’ data, pri­vacy and data pro­tec­tion are of para­mount im­por­tance.

To deal with the un­prece­dented speed at which data is be­ing gen­er­ated, cap­tured, stored, and dis­sem­i­nated, and gain an edge over oth­ers, or­ga­ni­za­tions need a ro­bust data man­age­ment strat­egy.

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