Q&A: It’s time to level the data playing field

From a tech­ni­cal sense, what has enabled the big data move­ment to infil­trate com­pa­nies so quick­ly?

When we think about the tech­nol­o­gy advance­ments that coa­lesce in the big data move­ment, the biggest one is cloud. It’s the most advanced way we’ve come up with to-date to pro­vide real­ly strong and inno­v­a­tive tech­nol­o­gy that the vast major­i­ty of com­pa­nies could­n’t have afford­ed to pur­chase, build or admin­is­ter them­selves. With­out open-source soft­ware, there would be no Google, as there would not have been a licens­ing con­tract struck with Win­dows to fund some­thing like Google. It need­ed Lin­ux and it need­ed open source. Cloud is not dis­sim­i­lar. It has pro­vid­ed a dif­fer­ent and crit­i­cal­ly impor­tant way of approach­ing data by enabling a small num­ber of very large and sophis­ti­cat­ed com­pa­nies to pro­vide a com­mod­i­ty that you can sign up to with a cred­it card and access amaz­ing things.

Tech­nol­o­gy is only part of the puz­zle in unlock­ing data val­ue. How have his­tor­i­cal­ly rigid cor­po­ra­tion struc­tures become more open to change?

Tra­di­tion­al­ly, inno­va­tion has been very slow in some large organ­i­sa­tions because of a cul­ture of block­ing things from being done dif­fer­ent­ly. But when peo­ple saw these mul­ti-tril­lion-dol­lar enter­pris­es like Google and Face­book emerg­ing, they want­ed to jump on the big data band­wag­on. Sec­tors such as retail had lit­tle choice because of the threat posed by Ama­zon, and it cas­cad­ed across oth­er indus­tries too. Embrac­ing big data in a mean­ing­ful way required cul­tur­al change and that came from a board and exec­u­tive lev­el man­date to do what’s nec­es­sary to bring data togeth­er. With­out that man­date we’d still have these mas­sive silos and fic­ti­tious excus­es to avoid doing what was nec­es­sary to make data use­ful. Big data forced every­body to change their behav­iour and how they man­aged data.

These are fun­da­men­tal changes, but what’s still miss­ing in many com­pa­nies are the skills to do bet­ter ana­lyt­ics. How do we over­come this chal­lenge?

If you reflect back on soft­ware oper­at­ing sys­tems in the ear­ly 80s, you’ll see the orig­i­nal Sil­i­con Val­ley CEOs were basi­cal­ly hob­by­ists. They ordered chips online and built them­selves com­put­ers. That’s how things got start­ed, but it was nev­er going to be scal­able. Then the likes of IBM and Apple came along to build off-the-shelf PCs and democ­ra­tise access to com­put­ing. We now need to see the same hap­pen in how we put togeth­er data. It has been a hob­by­ist’s jour­ney led by peo­ple who under­stand cer­tain pro­gram­ming lan­guages, and we’ve been cry­ing out for tech­nol­o­gy to advance in a way that democ­ra­tis­es the abil­i­ty of all organ­i­sa­tions to make data use­ful. That’s what we’re build­ing at Matil­lion: a data oper­at­ing sys­tem that lev­els the play­ing field. We’re devel­op­ing tools for use not only by skilled and hob­by­ist pro­gram­mers but by the great mass­es of peo­ple out there. We exploit cloud, AI and all these pow­er­ful tech­nolo­gies, but we wrap them in a beau­ti­ful, sim­ple user inter­face that lets every organ­i­sa­tion become a suc­cess­ful data com­pa­ny.

How is Matillion’s plat­form real­is­ing the data oper­at­ing sys­tem?

The Matil­lion plat­form solves a major prob­lem: it’s real­ly hard to con­nect all your data; stitch­ing it all togeth­er is immense­ly com­pli­cat­ed. We have a unique capa­bil­i­ty that lets either our­selves, part­ners or end users – in a no code, drag-and-drop inter­face – con­nect to any­thing. And then the fun begins because you can begin to refine the data, get insights and enrich those insights over time. All those capa­bil­i­ties get you to a point where you have some­thing, visu­alised sim­ply in an ana­lyt­ics dash­board, that you can tru­ly make use of. Some busi­ness­es might be able to build and deploy that once, but how do you main­tain it and make sure if it breaks at 3am it can be fixed? The intel­li­gence of a data oper­at­ing sys­tem is such that if any pipelines fail, it can recov­er, alert or offer an alter­na­tive. That’s what takes it from just being a stan­dard solu­tion to some­thing intel­li­gent and self-cor­rect­ing.

What is the future of data in the enter­prise?

We know the val­ue you pro­vide to your cus­tomer is every­thing. In the enter­prise of the future, acquir­ing a cus­tomer and retain­ing a cus­tomer are vast­ly dif­fer­ent things. The inter­sec­tion of data and inno­va­tion is crit­i­cal to fos­ter­ing bet­ter cus­tomer rela­tion­ships. But there’s a step fur­ther. The real­ly great com­pa­nies today take their data and share it with their part­ners because they recog­nise they’re not just stand­alone busi­ness­es. They’ve got to com­bine with an ecosys­tem of part­ners to pro­vide a mod­ern dig­i­tal expe­ri­ence. Ama­zon does this real­ly effec­tive­ly, shar­ing insights and data about cus­tomers and buy­ing behav­iour, and that’s why they have an unpar­al­leled ser­vice and their part­ners come back for more. So if you’re going to pro­vide amaz­ing first-class cus­tomer expe­ri­ences, it’s the inter­sec­tion of data, inno­va­tion and part­ners. That’s what the mod­ern enter­prise is about.

Learn more about Matil­lion at matillion.com/demo

Pro­mot­ed by Matil­lion