A Con­ver­sa­tion With Anil Kamath, VP of Tech­nol­ogy, and David Karn­st­edt, SVP of Media and Adver­tis­ing Solutions

All catch phrases aside, algo­rithms, data and pre­dic­tive ana­lyt­ics are some of the most impor­tant parts of today’s mar­ket­ing process. Most of us seem to love the flip­ping of phrases. Take the ubiq­ui­tous and col­lo­quial phrases “50 is the new 30,” or “nerdy is the new cool.” As Mad Men has helped enforce, the debonair, quick-on-his-or-her feet mar­keters were the quin­tes­sen­tial cen­ter­pieces of adver­tis­ing for decades. They drank scotch at noon yet never ceased to pro­duce the quick­est quips on command.

Yet the dig­i­tal era has ush­ered in a need for a com­pli­men­tary coun­ter­part to the cre­ative mar­keter – one who focuses on data. Many peo­ple have rec­og­nized that data – not just the mere col­lec­tion of it but the analy­sis and pre­dic­tive impli­ca­tions of data — holds the key. How­ever, there were some early indus­try trail­blaz­ers, such as Anil Kamath, founder of Effi­cient Fron­tier, who now serves as VP of Tech­nol­ogy at Adobe in the Mar­ket­ing Cloud Dig­i­tal Mar­ket­ing busi­ness, that were quick to rec­og­nize data’s sig­nif­i­cant role in the future of the indus­try. David Karn­st­edt, SVP and GM of Media & Adver­tis­ing Solu­tions at Adobe, was another.

Anil and David recently pro­vided us with thought­ful views on the ris­ing role of the data sci­en­tist in mar­ket­ing, or what we used to call algo­rithms engineer.

What was your “A-ha” Moment for Data Science?:

Anil: What Effi­cient Fron­tier did right when we founded the com­pany in 2003, and what we are build­ing upon at Adobe now, is tak­ing the lead on build­ing algorithms-based mar­ket­ing sys­tems. There have been many com­pa­nies that aggre­gate large amounts of data and pro­vide reports and ana­lyt­ics on this data so that mar­ket­ing teams can under­stand what impact they are hav­ing and make deci­sions. But a good anal­ogy is the dif­fer­ence between a smart dash­board and fancy con­trols for a car vs. a self-driving car. Algo­rithms have been used in adver­tis­ing cam­paigns for quite some time as well, but data-influenced, end-to-end deci­sion­ing sys­tems are a recent phe­nom­e­non, and Effi­cient Fron­tier was an early pio­neer in that area. I had seen it tak­ing place on Wall Street, and my ah-ha moment was see­ing that it could be done in mar­ket­ing back in 2002.

At Adobe, we are look­ing to expand this par­a­digm to web expe­ri­ence man­age­ment, social adver­tis­ing, and more. Adobe’s mar­ket­ing prod­ucts use data and algo­rithms for many aspects of dig­i­tal mar­ket­ing. Two major ways we use it are: to make the actual mar­ket­ing deci­sions dri­ven com­pletely by data+algorithms and sec­ond by aid­ing (aug­ment­ing) mar­ket­ing pro­fes­sion­als with data+algorithms dri­ven insights and pre­dic­tions to enable them to make bet­ter deci­sions. We ana­lyze large amounts of data to extract sig­nals and make pre­dic­tions. We then use these pre­dic­tions to make deci­sions that auto­mat­i­cally opti­mize mar­ket­ing cam­paigns to drive peak mar­ket­ing per­for­mance. Adobe cus­tomers ben­e­fit from both aspects of data acti­va­tion and have a choice in which way they uti­lize us and our data.

David: I agree and can’t stress enough how impor­tant acti­vat­ing data is. It’s not sim­ply about data col­lec­tion but lever­ag­ing it and cre­at­ing insights. That’s where the real magic is. Adobe has an incred­i­ble team of diverse roles com­ing together to human­ize data, ana­lyze it, bend it and make it sing for our clients. It’s imper­a­tive to look at the field almost like physics.

What are the crit­i­cal skills of today’s data scientist?:

Anil: We look for 3 skills in a data sci­en­tist. First, they must love work­ing with data – lots of data, slic­ing and dic­ing and ana­lyz­ing data in dif­fer­ent ways to under­stand it bet­ter. Sec­ond, they must be good at math – sta­tis­tics, machine learn­ing, and opti­miza­tion. They need to con­struct math­e­mat­i­cal frame­works to solve prac­ti­cal mar­ket­ing prob­lems with the data. Lastly they need to pro­gram – write soft­ware that uses the data and the math to actu­ally solve the mar­ket­ing prob­lems and iter­ate and improve the soft­ware to deliver bet­ter mar­ket­ing results.

David: Ana­lyt­i­cal and well-versed in mar­ket­ing and mar­ket­ing trends is a must for our data exec­u­tives. Data sci­en­tists and mar­ket­ing exec­u­tives have become the per­fect com­ple­ment to one another. Our team of data sci­en­tists has dri­ven ROI that has pow­ered our fast-growing busi­ness into the lead­ing provider of dig­i­tal mar­ket­ing solu­tions in the industry.

Ten years ago, could you have imag­ined data sci­en­tists would be as impor­tant and in demand as they are becoming?

Anil: The ubiq­uity of the term is sur­pris­ing. But in hind­sight it seems inevitable that this would hap­pen. The explo­sion of data avail­abil­ity that became pos­si­ble with the Inter­net and the speed at which deci­sions can now be made meant that data analy­sis and algo­rithms dri­ven by data that can make these quick deci­sions had to be done by peo­ple versed in that sci­ence – the data scientists.

David: Yes, I was bet­ting on it! I hope that kids and young adults today who are inter­ested in tech­nol­ogy can eas­ily access the resources they need to grow into inno­va­tors of tomor­row and keep this trend on the rise.

A study from The McK­in­sey Global Insti­tute last year states that by 2018 the demand for deep ana­lyt­i­cal posi­tions in the Big Data world could exceed the sup­ply by 50–60% of those qual­i­fied. How do you think young peo­ple can be lured into this pro­fes­sion early on while in college?

Anil: It is already hap­pen­ing – the hype around Big­Data and algo­rithms has led to a big increase in peo­ple tak­ing an inter­est in Data sci­ence. A recent online arti­fi­cial intel­li­gence course offered by Stan­ford attracts an unprece­dented 160,000 stu­dents from over 190 coun­tries. There is now an incred­i­ble access to the infor­ma­tion on data sci­ence. Online courses on data­bases and machine learn­ing from the best pro­fes­sors at MIT and Stan­ford are now avail­able for free online. If you need prac­ti­cal expe­ri­ence with real world data there are large real data sets made avail­able by com­pa­nies like Net­flix on which any­one can experiment.

David: Data sci­en­tists are in high demand, and we need to har­ness the inter­ests of our youth today in the US. We need more empha­sis on this train­ing in the com­ing years in edu­ca­tion institutions.

How do you feel about the term Big Data? What does it mean to you?

Anil: The term Big Data has dif­fer­ent mean­ings to dif­fer­ent peo­ple based on what they do with the data. The part of Big Data that is mean­ing­ful to me is the pro­cess­ing and analy­sis of large amounts of data from a vari­ety of sources to do action­able ana­lyt­ics and pre­dic­tions that can then be used to by algo­rithms to drive mar­ket­ing performance.

David: Big Data is just another buzz word. When it hits Dil­bert car­toons you know the term has reached a place of almost mean­ing­less­ness. The next overused word will prob­a­bly be attri­bu­tion or media mix. But the inten­tion behind Big Data for many is pow­er­ful. One of the dri­ving forces for our announce­ment of both soft­ware and a place for mar­keters to excel in their work mov­ing to the Adobe cloud is data and how it’s dri­ving much of the inno­va­tion and evo­lu­tion of what’s hap­pen­ing in dig­i­tal mar­ket­ing forward.


Is Data a Threat to Cre­ative Pro­fes­sions and Cre­ativ­ity in Marketing?:

Anil: There are many areas of mar­ket­ing where human insights and cre­ativ­ity are crit­i­cal — like in design­ing ad copy or offers. At the same time, data-driven algo­rithms do a much bet­ter job in mar­ket­ing areas that involve ana­lyz­ing large amounts of data and using them to make quick and com­plex deci­sions. Even where human cre­ativ­ity is impor­tant, data sci­ence can aug­ment human intel­li­gence by pro­vid­ing insights that can help cre­ative peo­ple to see things that they nor­mally might not.

David: It’s just the oppo­site, really. Data ignites the power of cre­ativ­ity. It allows for automa­tion where it’s needed and pro­vides con­text around what cre­ative is work­ing, and how it inspires and engages. Our rich her­itage in the cre­ative indus­tries dri­ves us in dig­i­tal mar­ket­ing to pre­serve and strengthen the ties between the mar­keter and the cre­ative pro­fes­sional for the best busi­ness results.