Paving the way for Big Data an­a­lyt­ics in Sri Lanka

Daily Mirror (Sri Lanka) - - ICT -

OF­TEN TIMES, SRI LANKAN COM­PA­NIES ARE UN­AWARE OF THE WEALTH OF DATA THAT THEY POS­SESS OR HAVE EASY AC­CESS TO BASE ON PRO­CE­DURAL RECORDS GEN­ER­ATED BY THE BUSI­NESS OVER TIME

Sri Lanka is a land which is well ac­quainted with the con­cept of pre­cog­ni­tion, and a per­sis­tent desire to pre­dict the fu­ture. Even the most ra­tio­nal among us can­not help but be tempted by the idea.

If you knew what was com­ing to­mor­row, would you act dif­fer­ently? What would you do? How would things change? Nat­u­rally, many have tried to step in and ful­fill this la­tent de­mand for a more cer­tain fu­ture. We see it in horoscopes, and in the con­fi­dent procla­ma­tions of as­trologers. Yet time and again, these in­ter­me­di­aries fall woe­fully short of pro­vid­ing any real in­sight to what lays ahead.

Tools change with the times, and to­day’s tech­no­log­i­cal in­no­va­tions are rapidly en­abling a brave and ex­cit­ing new world of pos­si­bil­i­ties, one in which data be­comes a cat­a­lyst for an econ­omy, a so­ci­ety, and a na­tion that is free to un­leash its true po­ten­tial. Across the globe, we see nu­mer­ous ex­am­ples of pre­dic­tive an­a­lyt­ics en­abled by rapid ad­vances in the abil­ity of a given or­ga­ni­za­tion to col­late and an­a­lyze mas­sive quan­ti­ties of data – es­sen­tially en­abling a given macro-sit­u­a­tion to be an­a­lyzed si­mul­ta­ne­ously at face value, down the most minute, granular level. This ap­proach is ca­pa­ble of driv­ing en­tirely un­prece­dented par­a­digms of de­vel­op­ment in nearly ev­ery facet of life.

From the way we grow our food, to the way we run our busi­nesses; from the treat­ment of dis­ease, to the de­vel­op­ment of break­through prod­ucts and ser­vices in the bank­ing and fi­nan­cial sec­tor; from pre­dict­ing nat­u­ral dis­as­ters to de­vel­op­ing smarter fi­nan­cial mod­els, change is com­ing, and big-data is the key. Our emerg­ing dig­i­tal econ­omy will ul­ti­mately op­er­ate ac­cord­ing to a dras­ti­cally dif­fer­ent set of rules, and suc­cess in this new en­vi­ron­ment boils down to how we frame an is­sue, and how in­tel­li­gently we re­spond to the in­sights we are given.

While the gen­eral ap­proach of how big-data works is fairly con­sis­tent, its ap­pli­ca­tion and in­te­gra­tion into dif­fer­ent in­dus­tries can be a bit more com­plex. Con­se­quently, spe­cific ap­proaches must be tailored for each sec­tor.

Gen­er­ally speak­ing, big-data is sim­ply a catch-all term used to de­scribe a process by which pre­dic­tive math­e­mat­i­cal al­go­rithms are ap­plied to very large, com­plex, rapid­ly­chang­ing datasets in or­der to ex­tract pre­cise, re­li­able in­sights into what is most likely to hap­pen next, based on a com­plete anal­y­sis of ev­ery such sit­u­a­tion in the past.

As is of­ten the case, it is the bank­ing, fi­nan­cial ser­vices, and in­sur­ance (BFSI) that is of­ten the first adopter of cut­tingedge tech­nolo­gies and Though many BFSI or­ga­ni­za­tions are be­gin­ning to dis­rupt their an­a­lyt­ics land­scapes by gather­ing im­mense vol­umes of data as­sets, these com­pa­nies are at vary­ing lev­els of Big Data ma­tu­rity. As cus­tomer vol­ume in­creases, it dra­mat­i­cally af­fects the level of ser­vices of­fered by the or­ga­ni­za­tion.

Ex­ist­ing data an­a­lyt­ics prac­tices have sim­pli­fied the process of mon­i­tor­ing and eval­u­a­tion of banks and other fi­nan­cial ser­vices or­ga­ni­za­tions, in­clud­ing vast amounts of client data such as per­sonal and se­cu­rity in­for­ma­tion. But with the help of Big Data, banks can now use this in­for­ma­tion to con­tin­u­ally track client be­hav­ior in real time, pro­vid­ing the ex­act type of re­sources needed at any given mo­ment. This real-time eval­u­a­tion will in turn boost over­all per­for­mance and prof­itabil­ity, thus thrust­ing the or­ga­ni­za­tion fur­ther into the growth cy­cle.

In agri­cul­ture too, Big Data is tak­ing a fig­u­ra­tive sledge­ham­mer to cen­turies old in­ef­fi­cien­cies. Glob­ally, it is es­ti­mated that US$ 940 bil­lion ev­ery year as a re­sult of in­ef­fi­cien­cies in plant­ing, har­vest­ing, wa­ter use and truck­ing, as well as un­cer­tainty about weather, pests, con­sumer de­mand, all of which con­trib­ute to greater un­cer­tainty, which fur­ther com­pro­mises the abil­ity of the sec­tor to be re­spon­sive to change.

Once again, it ap­pears that Big Data will play an in­creas­ingly vi­tal role in re­solv­ing these chal­lenges. Through the de­ploy­ment of pur­pose de­signed sen­sors, farm­ers are gain­ing granular vis­i­bil­ity into oil con­di­tions, as well as de­tailed info on wind, fer­til­izer re­quire­ments, wa­ter avail­abil­ity and pest in­fes­ta­tions. GPS units on trac­tors, com­bines and trucks can help de­ter­mine op­ti­mal us­age of heavy equip­ment. Data an­a­lyt­ics can help prevent spoilage by mov­ing prod­ucts faster and more ef­fi­ciently. Un­manned aerial ve­hi­cles, or drones, can pa­trol fields and alert farm­ers to crop ripeness or po­ten­tial prob­lems.

One com­mon thread that runs through all of great­est suc­cess sto­ries in Big Data is found in the struc­tured ap­proach taken to adop­tion. It is sim­ply not enough to im­ple­ment these so­lu­tions sim­ply be­cause the com­pe­ti­tion is do­ing the same. Rather, each step into the world of big-data must be cal­cu­lated, pre­cise, and based on the fun­da­men­tal ques­tion: what will this in­for­ma­tion do for my or­ga­ni­za­tion?

Cur­rently, the Sri Lankan mar­ket is al­ready play­ing host to sev­eral im­por­tant ex­per­i­ments into the field of Big Data, how­ever the rea­son for their im­por­tance is more to do with the fact that these are his­toric first steps, rather than the fact that they are revo­lu­tion­ary steps for­ward. Nev­er­the­less, we must learn to crawl be­fore we can learn to walk, and in Sri Lanka, we see some ex­tremely en­cour­ag­ing signs with re­gard to the up­take of Big Data.

Of­ten times, Sri Lankan com­pa­nies are un­aware of the wealth of data that they pos­sess or have easy ac­cess to base on pro­ce­dural records gen­er­ated by the busi­ness over time. Sim­ply by be­ing a lit­tle more pre­cise about what how they an­a­lyze their data, com­pa­nies can gain un­prece­dented in­sights into em­ployee per­for­mance, op­er­a­tional ef­fi­ciency, prod­uct pop­u­lar­ity, and cus­tomer pref­er­ences. Equipped with this knowl­edge, or­gan­i­sa­tions can cat­e­go­rize their cus­tomers into dis­tinct seg­ments de­fined by their de­mo­graph­ics, reg­u­lar trans­ac­tions, and any other fields rel­e­vant to a given busi­ness. Such busi­nesses can tai­lor their ser­vice of­fer­ing, prod­ucts port­fo­lio and mar­ket­ing cam­paigns to have the most im­pact on cus­tomers in­clud­ing through the use of per­son­al­ized mar­ket­ing.

As with most in­ter­na­tional cases, in Sri Lanka too, we an­tic­i­pate the bank­ing and fi­nance sec­tor to lead coun­try into the field of big data, and al­ready the first ten­ta­tive steps in this di­rec­tion have been taken by lead­ing fi­nan­cial in­sti­tu­tions in the coun­try, how­ever, the true po­ten­tial of big data in this sec­tor re­mains largely un­tapped. This po­ten­tial can be di­rected to­wards in­ter­nal im­prove­ments to pro­cesses such as in ar­eas of fraud de­tec­tion to en­sur­ing com­pli­ance with reg­u­la­tory and statu­tory re­quire­ments, but can also be uti­lized to­wards driv­ing a deeper un­der­stand­ing of the cus­tomer, a fac­tor es­sen­tial to suc­cess in any ser­vice in­dus­try.

My an­a­lyz­ing so­cial me­dia, trans­ac­tion his­to­ries and com­par­ing such be­hav­ior with cus­tomers in sim­i­lar cat­e­gories, Sri Lankan banks will be able to de­ploy pre­dic­tive an­a­lyt­ics to de­ter­mine with un­prece­dented ac­cu­racy what a given cus­tomer will do, and what type of ser­vices they will re­quire, even be­fore the cus­tomer re­al­izes their own need.

Such tech­niques can be sim­i­larly uti­lized in the re­tail sec­tor, or the leisure sec­tor to de­liver tar­geted pro­mo­tions based on cri­te­ria such as cus­tomer pref­er­ences and stock avail­abil­ity in or­der to en­sure op­ti­mized sales.

A sin­gle bill might not re­veal much, but the en­tirety of an or­gan­i­sa­tion’s billing records can be mined for in­valu­able data. In re­tail, it could be de­ployed to de­ter­mine at what times of the day, week, month or year a par­tic­u­lar prod­uct reaches peak pop­u­lar­ity.

In the pub­lic sec­tor, these tech­nolo­gies can be uti­lized to save lives, pre­dict­ing fu­ture nat­u­ral dis­as­ters and help­ing to mit­i­gate them both through the abil­ity to pro­vide longer warn­ing pe­ri­ods for evac­u­a­tion and in terms of as­sist­ing in the co­or­di­na­tion of re­lief ef­forts. Most im­por­tantly, while the func­tion­al­ity of big-data is grow­ing at an ex­po­nen­tial rate, the costs as­so­ci­ated with them are not. In­deed the big data an­a­lyt­ics tools of to­day are avail­able at a frac­tion of the cost and ul­ti­mately, we are con­fi­dent that it is not nec­es­sar­ily the size of the in­vest­ment, but the in­tel­li­gence be­hind it that will de­ter­mine who emerges as the next gen­er­a­tion of busi­ness and cor­po­rate lead­ers. In the age of in­for­ma­tion, knowl­edge is power.

(Zahir Fuard is a co-founder of TYE So­lu­tions, Sri Lanka’s fastest grow­ing open source an­a­lyt­i­cal so­lu­tions provider. Hav­ing worked with a di­verse range of clients in­clud­ing large telecom­mu­ni­ca­tions, lo­gis­tics, banks, agro ,MNC’C and pro­duc­tion houses. For more info visit www.tyeso­lu­tions.com.lk)

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