Data science news

Big Data means Advanced Data Visualization

Firms are real­iz­ing the im­port­ance of visu­al­iz­a­tion tools be­cause em­ploy­ees are un­able to pick out im­port­ant pat­terns in data without data visu­al­iz­a­tion. Forrester points out that “num­bers on a grid of­ten does not con­vey the whole story and in the wor­st case, it can even lead to a wrong con­clu­sion.” Ever tried to cram a bunch of data on a single screen in the hopes that it will be easi­er to read? You can only fit so much. Visualizations help cure that prob­lem. How about data over­load? You know, rows and rows of ex­cel columns; it can get pretty daunt­ing and quite frankly dif­fi­cult to ana­lyze. “Fitting in and ana­lyz­ing hun­dreds of thou­sands of columns of at­trib­utes is an enorm­ous chal­lenge.”

We’re see­ing the de­vel­op­ment of new data visu­al­iz­a­tion tech­niques, what Forrester is re­fer­ring to as Advanced Data Visualization (ADV).

Is the BI Market Opportunity Understated?

According to Gartner, Business Intelligence (BI) was the second-fastest grow­ing en­ter­prise soft­ware mar­ket in 2011, with 16.4 per­cent year-over-year sales growth (from $10.5 bil­lion to $12.2 bil­lion). IDC pro­jects the busi­ness ana­lyt­ics soft­ware soft­ware mar­ket will reach $33.9 bil­lion in 2012.

But are mar­ket siz­ing pro­jec­tions un­der­stated? Potentially miss­ing from growth fore­casts is that the end-user base for BI tools is ex­plod­ing, from a re­l­at­ively ex­clus­ive group of IT pro­fes­sion­als and data sci­ent­ists to a mar­ket of mil­lions of every­day busi­ness users. The reas­on: BI tools are be­com­ing easi­er to use, mak­ing them ac­cess­ible to the masses. In short, BI tools are be­com­ing con­sumer­ized.

“In the fu­ture, ana­lyt­ics and caring about data will be­come a part of everyone’s job,” says Caleb Poterbin, head of mar­ket­ing at ana­lyt­ics soft­ware pro­vider Chartio.

Local Governments: Ready for Big Data?

Alistair Croll (prin­cip­al ana­lyst at Bitcurrent) singled out re­cently the pub­lic sec­tor or what he called “civil en­gin­eer­ing” as one of three spaces to watch for a big data im­pact. Said Croll: “I think mu­ni­cip­al data is one of the big three for sev­er­al reas­ons: it’s a good tie break­er for par­tis­an­ship, we have new in­ter­faces every­one can un­der­stand, and we fi­nally have a mostly-connected cit­izenry.”

For the fed­er­al level, Mark Weber is­sued a real­ity check: “As is the case with most emer­ging tech­no­lo­gies, rhet­or­ic of­ten out­paces ad­op­tion. A re­cent sur­vey of more than 150 fed­er­al IT pro­fes­sion­als con­duc­ted by Meritalk on be­half of NetApp high­lights en­thu­si­asm with­in the fed­er­al gov­ern­ment to lever­age big data to sup­port gov­ern­ment mis­sion outcomes.‘The Big Data Gap’ sur­vey re­veals that just 60 per­cent of IT pro­fes­sion­als say their agency is ana­lyz­ing the data it col­lects and a mod­est 40 per­cent are us­ing data to make stra­tegic de­cisions. All of this des­pite the fact that a whop­ping 96 per­cent of those sur­veyed ex­pect their agency’s stored data to grow in the next two years by an av­er­age of 64%.”

There are of course some ini­tial suc­cess stor­ies, for ex­ample the use of big data to as­sist law en­force­ment agen­cies in fight­ing crime. But be­fore we see the prom­ised po­ten­tial real­ized, a solid IT found­a­tion has to be built and the spe­cific ques­tions big data could an­swer or the op­tim­al gov­ern­ment sec­tor activ­it­ies it can im­prove must be defined.

Patching the Leaky Sales Pipeline with Data Analytics

Insurers em­ploy­ing out­bound mar­ket­ing pro­grams must ef­fect­ively man­age lead gen­er­a­tion costs to suc­cess­fully main­tain con­sist­ent prof­it­ab­il­ity. In the past, in­sur­ance com­pan­ies util­iz­ing out­bound lead gen­er­a­tion and cus­tom­er man­age­ment pro­grams of­ten found them­selves lack­ing the level of real-time data re­quired to stra­tegic­ally de­term­ine the next steps for their busi­ness. Sales should flow around real-time data ana­lyt­ics based on con­ver­sion rates by pro­duct, rep­res­ent­at­ive, source, day and time as well as team and in­di­vidu­al agent ex­e­cu­tion. To an­swer that need, in­sur­ance com­pan­ies should im­ple­ment an ana­lyt­ic­al sys­tem to cre­ate the ac­tion­able in­tel­li­gence that can in­crease lead con­ver­sion.

Today, in­sur­ance com­pan­ies can arm their sales team with con­ver­sion and agent per­form­ance stat­ist­ics that can in­crease the speed and suc­cess of the en­tire sales pipeline. Implementing data ana­lyt­ics soft­ware could be the glue needed to en­sure that no stel­lar sales leads fall through the cracks.

Changing the World: Big Data and the Cloud

We are in an age when jobs like “data sci­ent­ist” are not far from real­ity. The con­ver­gence of two key tech­no­lo­gic­al areas cloud com­put­ing and big data are hav­ing far reach­ing im­plic­a­tions that in­deed are chan­ging the world.

It’s lead­ing to the dis­cov­ery of new drugs to cure dis­eases; pre­dict­ing weather pat­terns more ac­cur­ately (even pre­dict­ing earth­quakes?), find­ing bet­ter ways to use and save wa­ter, and so on. These are many of the ideas and pro­jects that IBM has ad­vanced with its Smarter Planet ini­ti­at­ive, which has cloud com­put­ing and big data tech­no­lo­gies at its core.

The Cloud com­put­ing mod­el is a per­fect match for big data since cloud com­put­ing provides un­lim­ited re­sources on de­mand. Just two years ago I was de­liv­er­ing present­a­tions to in­tro­duce pro­fes­sion­als and stu­dents to this new mod­el. Today, cloud is a given, most IT people un­der­stand what it is, and many are us­ing it in their jobs.

Leave a Reply