Data science news

Data visu­al­iz­a­tion tools need to be in­tu­it­ive 

The grow­ing volume and com­plex­ity of data that com­pan­ies are col­lat­ing and ana­lyz­ing, as well as the em­power­ing of more busi­ness end-users to ac­cess such in­sights, have raised the im­port­ance of in­tu­it­ive data visu­al­iz­a­tion in­ter­faces for ana­lyt­ics tools, say in­dustry watch­ers.

John Brand, vice pres­id­ent and prin­cip­al ana­lyst at Forrester Research, said there is now more gen­er­al users without tech­nic­al know-how hand­ling data ana­lyt­ics than in the past, when such tools were lim­ited to the tech de­part­ment.

At the same time, the re­la­tion­ship between dif­fer­ent data sets has got­ten more com­plex, Brand noted. Traditional data visu­al­iz­a­tion ap­proaches were simplist­ic in rep­res­ent­ing the cor­rel­a­tion such as through rows and columns on Excel spread­sheets.

Today, though, there is a great­er em­phas­is on in­teg­rat­ing data from a wide vari­ety of sources, so new meth­ods of visu­al­iz­a­tions such as in­fograph­ics, in­ter­act­ive bubble charts and 3D land­scapes are in­creas­ingly needed, he poin­ted out.

As more busi­ness end-users get ac­cess to ana­lyt­ics tools, the way data is presen­ted will need to en­able “cog­nit­ive” visu­al­iz­a­tion in or­der for them to bet­ter make sense of the in­sights.

Who’s Really Using Big Data

According to Harvard Business Review,Big Data clearly has the at­ten­tion of the C-suite and re­spond­ing ex­ec­ut­ives were very op­tim­ist­ic for the most part. Eighty-five per­cent ex­pec­ted to gain sub­stan­tial busi­ness and IT be­ne­fits from Big Data ini­ti­at­ives. When asked what they thought the ma­jor be­ne­fits would be, they named im­prove­ments in “fact-based de­cision mak­ing” and “cus­tom­er ex­per­i­ence” as #1 and #2. Many of the ini­ti­at­ives they had in mind were still in the early stages, so HBR weren’t hear­ing about ac­tu­al busi­ness res­ults, for the most part, but rather about plans and ex­pect­a­tions:

• 85% of or­gan­iz­a­tions re­por­ted that they have Big Data ini­ti­at­ives planned or in pro­gress.
• 70% re­port that these ini­ti­at­ives are enterprise-driven.
• 85% of the ini­ti­at­ives are sponsored by a C-level ex­ec­ut­ive or the head of a line of busi­ness.
• 75% ex­pect an im­pact across mul­tiple lines of busi­ness.
• 80% be­lieve that ini­ti­at­ives will cross mul­tiple lines of busi­ness or func­tions.

Data Analytics: The next big thing in Indian IT

Here’s the next big thing in India’s IT space firms that can be glob­al champs in crunch­ing huge volumes of com­mer­cially use­ful data for com­pan­ies. Think about it: co­pi­ous amount of data is be­ing gen­er­ated on so­cial me­dia for­ums such as Twitter and Facebook.

How can a com­pany get the nug­get of gold from this data mine, called Big Data in trade jar­gon? That’s the job of data ana­lyt­ics firms, where India is ex­pec­ted to play a dom­in­ant role.

Big Data Analytics a Big Benefit for Marketing Departments

Today’s mar­ket­ing de­part­ments face many chal­lenges. Organizations are still identi­fy­ing meth­ods to make their products more customer- and market-driven, while busi­nesses are pres­sured to drive more qual­i­fied leads to their sales teams and to work with pro­duct de­vel­op­ment to en­sure they’re de­liv­er­ing the products and ser­vices cli­ents are ask­ing for.

Some have iden­ti­fied mar­ket­ing ana­lyt­ics as a way to re­solve these chal­lenges. A re­cent sur­vey dir­ec­ted by Professor Christine Moorman and Sr. Professor of Business Administration T. Austin Finch with Duke University’s Fuqua School of Business, found that mar­ket­ing ex­ec­ut­ives in the Fortune 1000 and Forbes 200 plan to in­crease their spend­ing on mar­ket­ing ana­lyt­ics in the next three years, some by as much as 60 per­cent. Many will be start­ing from scratch, as only 35 per­cent of re­spond­ents cur­rently use mar­ket­ing ana­lyt­ics.

Marketing ana­lyt­ics used in con­junc­tion with big data will help many or­gan­iz­a­tions prop­erly eval­u­ate their mar­ket­ing per­form­ance, gain in­sight in­to their cli­ents’ pur­chas­ing habits, mar­ket trends and needs and make evidence-based mar­ket­ing de­cisions. As one ex­ample, look at how politi­cians are us­ing big data to identi­fy their tar­get audi­ence and reach out to the so-called “si­lent ma­jor­ity.”

Big Data Analytics the Ultimate Solution for HR Woes?

With a tough glob­al eco­nomy, and high un­em­ploy­ment rates, em­ploy­ers are lit­er­ally de­luged with stacks and stacks of re­sumes. That’s where Big Data ana­lyt­ics comes in­to play. Machines are in­creas­ingly read­ing and scor­ing ap­plic­ants for call-backs and in­ter­views. And per­son­al­ity tests are chock full of data, which are then used to pre­dict the suit­ab­il­ity of can­did­ates for a spe­cific job based on how they an­swer a bat­tery of ques­tions.

Supply chain ex­ecs see be­ne­fits in pre­dict­ive soft­ware

Seventy-five per­cent of the 191 top sup­ply chain of­ficers who took part in a June 2012 Aberdeen Group sur­vey said their de­cision mak­ing could be im­proved with the use of prop­er ana­lyt­ics, defined as spe­cial soft­ware tools built to dis­cern pat­terns or trends in sup­ply chain and lo­gist­ics op­er­a­tions. Aberdeen Senior Research Analyst Bob Heaney de­tailed the sur­vey res­ults in a present­a­tion at Dematic’s 27th Annual Material Handling and Logistics Conference in Park City, Utah, where more than 400 people gathered in early September to hear present­a­tions on the latest sup­ply chain and ma­ter­i­al hand­ling de­vel­op­ments and trends.

Respondents to the re­search firm’s sur­vey said pre­dict­ive ana­lyt­ic­al soft­ware would help them to achieve cost sav­ings, in­crease prof­it­ab­il­ity, and dif­fer­en­ti­ate their cus­tom­er ser­vice from that of com­pet­it­ors.

44 per­cent of the sur­vey re­spond­ents are cur­rently us­ing ana­lyt­ics to im­prove in­tern­al pro­cesses for fore­cast­ing, pri­cing, and plan­ning pro­mo­tions as well as for mak­ing mid-course cor­rec­tions. In ad­di­tion, 37 per­cent said they are us­ing ana­lyt­ics to op­tim­ize in­vent­ory based on pre­dict­ive ana­lyt­ics for cus­tom­er de­mand or ser­vice levels. Another 35 per­cent are us­ing ana­lyt­ics to “trans­form” their sup­ply chains.

The Big Value In Big Data: Seeing Customer Buying Patterns 

A real world ex­ample of how lever­aging Big Data can solve the com­plex­ity around pro­duct pro­lif­er­a­tion by help­ing com­pan­ies align pro­duct of­fer­ing and sup­ply chain based on customer-buying pat­terns.

Big Data: More Than Just A Trend

According to Gartner, un­struc­tured and struc­tured data held by en­ter­prises con­tin­ues to grow at ex­plos­ive rates. However, volume and ve­lo­city of data – what the busi­ness world is be­gin­ning to un­der­stand as the “Big Data Problem” – are be­com­ing less of an is­sue than the vari­ety of data. Each silo with­in the en­ter­prise – op­er­a­tions, sup­ply man­age­ment, sales, mar­ket­ing – faces its own data vari­ety chal­lenges, where bits ex­ist in a mul­ti­tude of formats and types.

Due to the vari­ab­il­ity of data across silos, sys­tems can’t “speak” to one an­other, and gain­ing an ac­cur­ate, enterprise-wide view of de­mand and per­form­ance seems im­possible. In fact, most busi­ness and IT man­agers ac­cept the lack of inter-system col­lab­or­a­tion as a given, an in­ev­it­able lim­it that must be worked around.

There is a bet­ter way to tackle this chal­lenge in vari­ety and cap­ture the op­por­tun­ity posed by Big Data.

Patterns And Connections

This Big Data chal­lenge re­quires solu­tions that can har­ness the in­tel­li­gence from the data and de­liv­er ac­tion­able in­tel­li­gence to the busi­ness user. Conventional busi­ness in­tel­li­gence and data ware­house tools aren’t de­signed to ana­lyze, identi­fy and sur­face crit­ic­al data link­ages and caus­al­ity.

Freeing the data to re­veal con­nec­tions and caus­a­tion through pattern-based ana­lyt­ics solu­tions will paint a big­ger pic­ture – one that can bet­ter man­age pro­duct vari­ants and stream­line sales by shed­ding light on what cus­tom­ers are buy­ing, when, where and how. Currently com­pan­ies pour their non-standard data in­to spread­sheets that then re­quire teams of data ana­lysts to in­ter­pret and de­rive mean­ing from it. This is not scal­able and of­ten mis­ses the mark. Big data de­mands ap­plic­a­tions that can in­ter­pret and de­liv­er im­me­di­ate ac­tion­able in­tel­li­gence to busi­ness users.

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