Digital Maturity

Whether their data are stored in a cloud or lake, most companies have a respectable volume of digital information at their disposal. At first glance, that is. For all too often, their stockpiles turn out to be minimally structured and largely unprocessed masses of raw material, says AI expert Kevin Lin in the interview.


When companies want to use artificial intelligence, expert Kevin Lin first intensively analyzes the quality of the data. The engineering graduate dispels a misconception: “The quantity of data alone is not an asset.” Porsche Consulting/Marco Prosch

Intro­duc­ing our expert: Kevin Lin is a fas­ci­nat­ing guide for any­one wish­ing to explore the world of arti­fi­cial intel­li­gence. He can whisk peo­ple away on an excit­ing vir­tu­al jour­ney into the dig­i­tal life of the future. The voy­age is as cap­ti­vat­ing as a night at the movies. But sci­ence fic­tion is not his thing. Lin’s exam­ples of AI appli­ca­tions are real and reflect main­stream busi­ness life. He takes a mat­ter-of-fact approach and seeks con­crete solu­tions that truly help com­pa­nies move for­ward. And he con­sis­tent­ly main­tains a crit­i­cal dis­tance to the hype about an El Dora­do of data.

Born in Tai­wan in 1978, Lin stud­ied mechan­i­cal engi­neer­ing and semi­con­duc­tor tech­nol­o­gy in his home­land before com­ing to Ger­many to com­plete his engi­neer­ing stud­ies at the pres­ti­gious RWTH Aachen Uni­ver­si­ty in North Rhine-West­phalia with a focus on avi­a­tion and aero­nau­tics. A 2004 trainee­ship in sus­pen­sion devel­op­ment brought him into con­tact with Porsche AG for the first time. Short­ly there­after he began work­ing as a test engi­neer at the Stuttgart-based sports-car maker’s devel­op­ment cen­ter in Weissach.

While pur­su­ing his doc­tor­ate at the Tech­ni­cal Uni­ver­si­ty of Darm­stadt, Lin worked inten­sive­ly on AI-sup­port­ed causal analy­ses of busi­ness strate­gies and deci­sions. And in 2015 he joined Porsche Con­sult­ing. Now the tech­ni­cal direc­tor for a ten-mem­ber expert team, he is respon­si­ble for the man­age­ment consultancy’s Arti­fi­cial Intel­li­gence and Data Ana­lyt­ics field togeth­er with Asso­ciate Part­ner Fabi­an Schmidt. In his per­son­al life—perhaps as a coun­ter­bal­ance to his work—Lin has a fond­ness for ana­logue tech­nolo­gies. With great atten­tion to detail, he is restor­ing a Beetle—or to be pre­cise, a 1973 Volk­swa­gen 1303 S with 50 horse­pow­er, a 1.6‑liter capac­i­ty, and a Raven­na Green paint job. “It’s near­ly fin­ished,” he says. “Now all that’s need­ed is a com­plete over­haul of the orig­i­nal interior.”

In this inter­view Kevin Lin dis­cuss­es whether busi­ness and indus­try are pay­ing suf­fi­cient atten­tion to the poten­tial of arti­fi­cial intel­li­gence, and how data ana­lyt­ics can help them succeed.

Every second company with more than 2,000 employees in Germany now uses artificial intelligence (AI). This was the result of a broad survey by the digital association Bitkom. It also included small companies (20 or more employees). The overall picture is sobering: Just ten percent of all surveyed companies, irrespective of size, use this rising technology. There is a general lack of trained specialists and qualified data, but also, willingness to invest. 64 percent of the 600 survey participants have not yet invested in AI and have no future budget plans for AI. This is acting as a brake on the pace of innovation. Porsche Consulting/Clara Philippzig

How important are artificial intelligence and data analytics for our future?

“On the one hand, we’re work­ing with ever greater vol­umes of data. Yet on the other, we’re expect­ing ever more detailed answers to com­plex ques­tions. Excel spread­sheets and sim­ple math­e­mat­ics are not up to the job when it comes to things like spot­ting trends or iden­ti­fy­ing sources of error in com­plex con­texts, for exam­ple. For that we need dig­i­tal tools like machine learn­ing and arti­fi­cial intel­li­gence. Those who don’t mas­ter the use of dig­i­tal tools will find it hard to inter­pret the phys­i­cal world in the future. The future via­bil­i­ty of a com­pa­ny or orga­ni­za­tion will depend on how effi­cient­ly it is able to use this set of tools. I’m con­vinced of that.”

Analytics consultants Dana Ruiter and Kevin Lin evaluate data at the Frankfurt airport office of management consultancy Porsche Consulting. The ten-strong team have all the necessary skills and expertise.Porsche Consulting/Marco Prosch

Are business and industry making sufficient use of the potential offered by AI and data analytics?

“No, not at all. AI and data ana­lyt­ics appli­ca­tions are still in the very early stages, espe­cial­ly in Ger­many. As I see it, there are three major rea­sons for this. First of all, a lot of com­pa­nies aren’t focus­ing on the right issues. Many are eager to put arti­fi­cial intel­li­gence on their agen­das, but in actu­al prac­tice they often over­look the need to lay solid foun­da­tions for it. By that I mean cre­at­ing the basic pre­req­ui­sites like the infra­struc­ture need­ed for AI appli­ca­tions. Sec­ond, the qual­i­ty of their exist­ing data is fre­quent­ly not good enough. Vol­ume alone doesn’t make data valu­able, even though that’s a com­mon assump­tion. More­over, many com­pa­nies don’t define the respon­si­bil­i­ties of their data own­ers, mean­ing the peo­ple in charge of data man­age­ment and qual­i­ty, in clear and uni­form terms. Effec­tive man­age­ment would be help­ful here, with over­ar­ch­ing data gov­er­nance. It sets guide­lines, checks adher­ence, and there­by ensures that the oper­a­tions ben­e­fit. The third fac­tor act­ing as a brake on progress has to do with a high­ly sen­si­tive and com­pli­cat­ed mat­ter, name­ly the way in which data pro­tec­tion is under­stood. Legal con­sid­er­a­tions often pre­vent avail­able infor­ma­tion from being used. It’s like hav­ing a buried trea­sure but not being allowed to dig it up.”

Since beginning his doctorate program, one of Lin’s areas of expertise has been the use of artificial intelligence in the causal analysis of corporate strategies and decisions. This makes it possible to calculate prospects for success.Porsche Consulting/Marco Prosch

Which sectors are already putting AI and data analytics to good use? And which ones have a lot of catching up to do?

“Retail is out in front. Com­par­a­tive­ly speak­ing, sales and logis­tics start­ed using dig­i­tal sys­tems very early on—in e‑commerce, for instance. The data in this area allow us to gen­er­ate trans­paren­cy and rec­og­nize pat­terns. By con­trast, in pub­lic sec­tors like trans­porta­tion, reg­u­la­tions or per­haps also a reluc­tance to invest are hold­ing back the intro­duc­tion of AI and data ana­lyt­ics solu­tions. Impor­tant use data are often not even com­piled, even though other coun­tries with sim­i­lar data pro­tec­tion require­ments have long been devel­op­ing prac­ti­cal solu­tions. That means the foun­da­tions aren’t there for use-based main­te­nance or for infra­struc­ture expan­sion plans.”

Kevin Lin sees untapped opportunities in artificial intelligence and data analytics. However, he also states: “The final decisions still have to be made by humans.”Porsche Consulting/Marco Prosch
Mechanical engineering, semiconductor technology, aerospace, Porsche test engineer—the management consultant has a wealth of industrial experience to draw on.Porsche Consulting/Marco Prosch
Resting on the sofa? Only for the photographer. “Once you start dealing intensively with data, you’ll never stop. It’s because of the seemingly limitless options,” says Lin. Porsche Consulting/Marco Prosch

How hard is it for companies or public agencies to introduce AI solutions?

“It depends. The amount of effort need­ed for a given com­pa­ny is a func­tion of what’s known as its dig­i­tal matu­ri­ty. That has to be eval­u­at­ed. As man­age­ment con­sul­tants, we there­fore start by check­ing whether viable foun­da­tions are already part of the company’s cul­ture. We look at key fac­tors like data qual­i­ty and data literacy—meaning the abil­i­ty to eval­u­ate data and uti­lize them in thought-out ways. Anoth­er fac­tor is the added value that AI-sup­port­ed deci­sions can give the busi­ness. That’s the basis for decid­ing how much to invest in devel­op­ing AI solu­tions that will ben­e­fit the bot­tom line. Spe­cial­ists known as data sci­en­tists are also need­ed. They pos­sess the cru­cial abil­i­ty to com­bine human cre­ativ­i­ty and AI ser­vices in ways that achieve the best pos­si­ble out­put. That’s why there’s been so much com­pe­ti­tion for tal­ent­ed AI peo­ple over the years, with tech giants in par­tic­u­lar offer­ing exor­bi­tant salaries.”

From the album, photographed in August 2017: this green Volkswagen has taken up a good part of Kevin Lin’s spare time for a while now...Porsche Consulting/Jörg Eberl piece at a time, the graduate engineer from Taiwan is restoring a symbol of Germany’s economic miracle. If he feels like taking it for a spin, the Beetle is still registered and, if he’s not working on it, roadworthy...Porsche Consulting/Jörg Eberl
...Lin spends the stops in the same way (apart from his smartphone): ignition off, handbrake on, feet up. Autonomous driving isn’t planned for this classic car... Porsche Consulting/Jörg Eberl’s all about pure nostalgia. In the final step, the entire interior is being restored—everything should look as brand-new as when it was first delivered five decades ago... Porsche Consulting/Jörg Eberl
...and now the technical data (for the connoisseurs): Volkswagen 1303 S, built in 1973, 1.6 liter engine, 50 hp, color: Ravenna Green. The historic car is in its original condition—and therefore officially classified as a culturally interesting example of motor vehicle technology. Porsche Consulting/Jörg Eberl

What does the introduction of AI mean for the workforce? Does it change the company’s culture, and does it undermine collaboration?

“New tech­nolo­gies have always meant hav­ing to learn and relearn. Right now what’s impor­tant is to under­stand what pos­si­bil­i­ties AI and data ana­lyt­ics can open up. And to learn how to use these tools as suc­cess­ful­ly as pos­si­ble. There’s no doubt that when­ev­er you intro­duce new things into life, and espe­cial­ly at work­places, there will almost always be an ele­ment of fear or anx­i­ety. We con­sul­tants take these gen­er­al­ly fore­see­able reac­tions very seri­ous­ly. In our expe­ri­ence, the best way to encour­age accep­tance in this type of project is for every­one affect­ed by the change to con­tribute their spe­cial­ized knowl­edge and help shape the tran­si­tion, start­ing with the AI mod­el­ling phase. AI mod­els that have been assessed by spe­cial­ists from dif­fer­ent dis­ci­plines yield qual­i­ta­tive­ly bet­ter deci­sions. And the employ­ees all gain the sense that they still have their hands on the wheel. They then per­ceive AI as a help­ful tool that can be of ben­e­fit to them in their indi­vid­ual jobs. There’s a lot of room to help shape this process. Because one thing is clear: the role that peo­ple play in intro­duc­ing arti­fi­cial intel­li­gence is ulti­mate­ly deter­mined by the peo­ple themselves.”

More about arti­fi­cial intel­li­gence: Ten Hur­dles Slow­ing the Transformation 

Direct con­tact with Porsche Con­sult­ing, Arti­fi­cial Intel­li­gence and Data Ana­lyt­ics depart­ment: 

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