Metamorphosis of Auto into Mobility

By Prof. Dr. Chris Schlue­ter Lang­don

The well-being and vita­li­ty of the auto­mo­ti­ve sec­tor is of utmost impor­t­ance for wealth, inno­va­ti­on and poli­ti­cal cloud for a Euro­pean Uni­on in bet­ween the USA and Chi­na. Main­tai­ning and gro­wing it’s near­ly 14 mil­li­on jobs in Euro­pe (ACEA 2019) requi­res suc­cess with the industry’s meta­mor­pho­sis into new, future, next mobi­li­ty – and in turn, sol­ving many data issu­es.

A big­ger pie but shift to ser­vice reve­nue

Indus­try obser­vers, invest­ment ban­kers and con­sul­tants agree: The auto indus­try is caught up in a shift away from the tra­di­tio­nal busi­ness of sel­ling vehi­cles & parts to pro­vi­ding mobi­li­ty centric ser­vices to impro­ve how peop­le and goods can move from point A to point B (see Figu­re 1, Schlue­ter Lang­don 2018). The shift is epic, becau­se first­ly, ser­vices are a very dif­fe­rent type of busi­ness – with sub­scrip­ti­ons and incre­men­tal annui­ty cash flows, new ope­ra­ti­ons and soft­ware auto­ma­ti­on (Ray­po­rt & Jawor­ski 2004, Cha­se & Dasu 2001, Cha­se & Gar­vin 1989). Second­ly, the shift appears to be rapid – in less than two vehi­cle genera­ti­ons – and enor­mous in size. For incumbents – ori­gi­nal equip­ment manu­fac­tu­rers (OEMs), sup­pliers, and chan­nel part­ners ali­ke – con­ti­nued suc­cess requi­res buil­ding up a dif­fe­rent busi­ness sys­tem while “kee­ping the wheels on the bus.”

Figu­re 1: Shift of auto­mo­ti­ve reve­nue share from making things to ser­vices

The auto-less auto com­pa­ny: Powers­hift to data

Inves­tors agree: Data-dri­ven ser­vices beat making things big time. Power – and pro­fit – seem to be shif­ting from the vehi­cle to data (see Figu­re 2, Schlue­ter Lang­don 2019). It sounds pro­vo­ca­ti­ve – par­ti­cu­lar­ly to auto peop­le – but it’s neit­her magic nor a sur­pri­se. We have seen it befo­re in con­tent busi­nes­ses ran­ging from news­pa­pers to music (Mee­ker 2018). News­pa­pers felt well pro­tec­ted behind prin­ting pres­ses and last mile deli­very only to be out­flan­ked by digi­tal and data ([digi­tal ent­rants] “explo­it indi­vi­du­al con­su­mer pro­files and beha­vi­or data to incre­a­se cus­to­mer satis­fac­tion, loyal­ty, repeat purcha­ses, and cross-sel­ling oppor­tu­nities,” Schlue­ter Lang­don 2002, p. 52). While paper-based publis­hers went from top to bot­tom, data hogs like Goog­le and Face­book aro­se to usurp mar­ke­ting bud­gets with next genera­ti­on data-dri­ven ser­vices. The switch did not hap­pen over­night, and the­re are always excep­ti­ons but even sto­ried brands like Time Maga­zi­ne, News­week and The Washing­ton Post had to be res­cued by phil­an­thro­pists (by Salesforce’s Marc Benioff, Harman’s Sid Har­man, and Amazon’s Jeff Bezos, respec­tively).

Figu­re 2: Power shift from hard­ware and ser­vices to data mone­tiz­a­ti­on

Data: A big misun­derstan­ding … puz­zle hea­ded­ness … neglect

In 2006 Cli­ve Hum­by, a mathe­ma­ti­ci­an and archi­tect of UK retailer Tesco’s club and loyal­ty card (and co-foun­der of con­su­mer data sci­ence pioneer Dunn­hum­by, spo­ke of “data as the new oil” at the Asso­cia­ti­on of Natio­nal Adver­ti­sers’ marketer’s sum­mit at Chicago’s Kel­logg School of Manage­ment (Arthur 2013). The one part of his oil ana­lo­gy, that data may be as valu­able as oil has caught on – alt­hough data is not even used up in con­sump­ti­on like oil. The other part about the refi­ning effort … well, no so much. Humby’s ana­lo­gy sug­gests that in order to pre­pa­re raw data into a refi­ned data pro­duct for ana­ly­tics or rea­dy for arti­fi­cial intel­li­gence, so cal­led AI-rea­dy data, it may take (a) exten­si­ve refi­ning (for examp­le, “fil­ling in” or impu­ting mis­sing data) and (b) at an indus­tri­al sca­le with lar­ge plat­forms com­pa­ra­ble to mas­si­ve refi­ne­ries for oil. For plain data sto­rage and pro­ces­sing this refi­ne­ry ana­lo­gy appears to cor­re­spond very well with obser­va­tions in the field, spe­ci­fi­cal­ly the explo­si­ve growth of the cloud busi­ness. Laun­ched with Amazon’s Elastic Com­pu­te Cloud in 2006 the busi­ness is alrea­dy high­ly con­cen­tra­ted at an ear­ly age with only three hypersca­lers domi­na­ting most of the busi­ness: Amazon’s Web Ser­vices (AWS), Microsoft’s Azu­re, and Google’s Cloud Plat­form (GCP). As of end of 2018 the­se top three ven­dors accoun­ted for 60% of the busi­ness, the top 10 for near­ly 75% (Mil­ler 2019).

If you can’t mea­su­re, you can­not mana­ge

The 2020 Coro­na­vi­rus tra­ge­dy unex­pec­ted­ly put data in the spot­light and tur­ned it into front-page news. All of us were sud­den­ly deba­ting new varia­bles, such as infec­tion rates. And sud­den­ly key con­cepts to eva­lua­te the qua­li­ty of data sci­ence, name­ly relia­bi­li­ty (con­sis­ten­cy of mea­su­res), and vali­di­ty (accu­ra­cy of mea­su­res) made sen­se to ever­yo­ne. The cri­sis pul­led data and its com­ple­xi­ty out of the shadow and into the lime­light. In the past, if I asked an audi­ence who had taken a sta­tis­tics class, all hands would go up. But no one would vol­un­teer to quick­ly exp­lain a t‑test. If I asked about data, nobo­dy had taken a class, but ever­y­bo­dy would be eager to exp­lain it. So, it was easy to pedd­le ste­reo­ty­pes, omit­ting deeper issu­es, such as data har­mo­niz­a­ti­on (e.g., Schlue­ter Lang­don & Siko­ra 2019), sharing and poo­ling archi­tec­tures, and data rights manage­ment and gover­nan­ce (e.g., Otto 2011). Even a sup­po­sed­ly strai­ght­for­ward task, such as sizing data, how to mea­su­re the quan­ti­ty of data, has remai­ned sur­pri­sin­gly tri­cky to this day. We know to buy eggs by the dozen, a pound of but­ter, and a liter of milk. But how do we buy data? Figu­re 3 illus­tra­tes the dilem­ma. And how to mone­ti­ze data, or use it as “fuel” to “power” new “smart” ser­vices, such as seam­less mobi­li­ty, if we can’t even quan­ti­fy data pro­per­ly?

Figu­re 3: Data mea­su­re­ment dilem­ma

Data issu­es: Sci­ence, pro­duc­tiz­a­ti­on, busi­ness stra­te­gy and regu­la­ti­on

Data is wide­ly seen as a key enab­ler of new, future, next mobi­li­ty: “Open flow of data across infra­st­ruc­tu­re and user domains is an important enab­ler for smart mobi­li­ty ser­vices and sys­tems inno­va­ti­on” (Euro­pean Com­mis­si­on 2017, p. 15). Howe­ver, the chal­len­ges are for­mi­da­ble. They inclu­de:

  1. Data sci­ence issu­es, such as the afo­re­men­tio­ned “metrics gap” with regard to data quan­ti­ty, qua­li­ty and infor­ma­ti­on con­tent.
  2. Data indus­tria­liz­a­ti­on (Wal­ter et al. 2007) or pro­duc­tiz­a­ti­on to make data afford­a­ble and data ana­ly­tics scala­b­le (“data as a pro­duct,” Cros­by & Schlue­ter Lang­don 2019; “data fac­to­ry for data pro­ducts,” Schlue­ter Lang­don & Siko­ra 2019).
  3. Busi­ness stra­te­gy con­si­de­ra­ti­ons: “The smart inte­gra­ti­on of tariff struc­tures, data and user inter­faces as well as the dis­po­si­ti­on of rol­ling stock […] requi­res new busi­ness models and sche­du­ling, boo­king, navi­ga­ting, ticke­ting and char­ging solu­ti­ons” (Euro­pean Com­mis­si­on 2017, p. 11).
  4. Regu­la­to­ry updates: “Indi­vi­du­al data pri­va­cy rights and the owners­hip of mobi­li­ty and city data will need to be addres­sed and regu­la­ted to ensu­re both com­pe­ti­ti­on and free­dom from ille­gal govern­men­tal or com­mer­cial sur­veil­lan­ce” (Euro­pean Com­mis­si­on 2017, p. 13–14).
Figure: Architectural concept of GAIA‑X federated services

Con­nec­ting the dots: IDSA for data, NPM for mobi­li­ty, GAIA‑X for both?

Luck­i­ly, the­re are several initia­ti­ves that address various aspect of the many data issu­es, such as NPM for mobi­li­ty in Ger­ma­ny, IDSA for data sov­er­eig­n­ty in Euro­pe and GAIA‑X for both.

  1. NPM, the Natio­nal Plat­form Future of Mobi­li­ty, was con­ve­ned by the Ger­man government in 2018 in order to find a way for­ward for the auto busi­ness, the most important indus­try in Ger­ma­ny. It was laun­ched by the Federal Minis­ter of Trans­port, Andre­as Scheu­er, and is hea­ded by the for­mer CEO of SAP, Prof. Dr. Kager­mann, “to deve­lop mul­ti-modal and inter­mo­dal paths for [… an] envi­ron­ment­al­ly friend­ly trans­port sys­tem” (NPM 2020).
  2. The Inter­na­tio­nal Data Spaces Asso­cia­ti­on (IDSA) counts 110+ mem­ber orga­niz­a­ti­ons and has defi­ned an IDS refe­rence archi­tec­tu­re and a for­mal stan­dard to be used for crea­ting secu­re and trus­ted data spaces (Otto et al. 2019), in which com­pa­nies of any size and from any indus­try can mana­ge their data assets in a sov­er­eign fashion. In order to cater to the spe­ci­fic requi­re­ments of a mobi­li­ty data space a dedi­ca­ted IDSA com­mu­ni­ty is being crea­ted.
  3. GAIA‑X was unvei­led in June 2020 by Ger­man Federal Minis­ter of Eco­no­mic Affairs and Ener­gy Peter Alt­mai­er and the French Minis­ter of the Eco­no­my and Finan­ce Bru­no Le Mai­re to kick off the crea­ti­on of a next genera­ti­on, secu­re infra­st­ruc­tu­re for shared data or data pools or “spaces” in Euro­pe. Why bother? Well, if AI is the future and some of the most suc­cess­ful AI needs lots of data – Big Data – then only big com­pa­nies with big data will have a future. And small com­pa­nies won’t. This is whe­re GAIA‑X comes in, enab­ling data sharing with sov­er­eig­n­ty using IDS among other tech­ni­ques (BMBF 2019).

How to con­nect the dots? Well, stay tun­ed. NPM is said to be working on a real life labo­ra­to­ry to pre­sent results on new, future, next mobi­li­ty for the ITS World Con­gress in 2021 in Ham­burg (NPM 2020). Watch for announ­ce­ments soon.

Refe­ren­ces

Arthur, C. 2013. Tech giants may be huge, but not­hing matches big data. The Guar­di­an (August 23) (link)

BMWI, Federal Minis­try for Eco­no­mic Affairs and Ener­gy. 2020. GAIA‑X: The Euro­pean pro­ject kicks off the next pha­se. Public Rela­ti­ons (June), Ber­lin (link)

Cha­se, D., and S. Dasu. 2001. Want to Per­fect Your Company’s Ser­vice? Use Beha­vio­ral Sci­ence. Har­vard Busi­ness Review (June): https://hbr.org/2001/06/want-to-perfect-your-companys-service-use-behavioral-science

Cha­se, D., and A. Gar­vin. 1989. The Ser­vice Fac­to­ry. Har­vard Busi­ness Review (July-August): https://hbr.org/1989/07/the-service-factory

Cros­by, L., and C. Schlue­ter Lang­don. 2019. Data is a Pro­duct – Mana­gers who con­cep­tua­li­ze data as a pro­duct can maxi­mi­ze its mul­ti-func­tio­n­al poten­ti­al. Mar­ke­ting News, Ame­ri­can Mar­ke­ting Asso­cia­ti­on (April 24) (link)

Euro­pean Com­mis­si­on. 2017. Smart mobi­li­ty and ser­vices – Expert group report. Direc­to­ra­te-Gene­ral for Rese­arch and Inno­va­ti­on, Direc­to­ra­te H Trans­port, Unit H.1 Trans­port stra­te­gy (EUR KI-01–17-928-EN‑N): Brussels

Mee­ker, M. 2018. Inter­net Trends 2018. Code Con­fe­rence. Klei­ner Per­kins Cau­field & Byers (May 30): https://www.kleinerperkins.com/perspectives/internet-trends-report-2018/

Mil­ler, R. 2019. AWS and Micro­soft reap most of the bene­fits of expan­ding cloud mar­ket. Tech­crunch (Febru­a­ry 1st) (link)

Otto, B. 2011. Orga­ni­zing data gover­nan­ce: Fin­dings from the telecom­mu­ni­ca­ti­ons indus­try and con­se­quen­ces for lar­ge ser­vice pro­vi­ders. Com­mu­ni­ca­ti­ons of the AIS 29(1): 45–66

Otto, B., S. Stein­buß, A. Teu­scher, and S. Loh­mann. 2019. Refe­rence Archi­tec­tu­re Model Ver­si­on 3.0. Inter­na­tio­nal Data Spaces Asso­cia­ti­on (April), Dort­mund (link)

Ray­po­rt, J, and B. Jawor­ski. 2004. Best Face For­ward. Har­vard Busi­ness Review (Decem­ber): https://hbr.org/2004/12/best-face-forward

Schlue­ter Lang­don, C. 2019. The Auto Power Shift to Data – From Aste­ro­id to Data Sand­wi­ches and Exch­an­ges (Auto 2). Working Paper (WP_DCL-Dru­cker-CGU_2019-01), Dru­cker Cus­to­mer Lab, Dru­cker School of Manage­ment, Cla­re­mont Gra­dua­te Uni­ver­si­ty, Cla­re­mont, CA (link)

Schlue­ter Lang­don, C. 2018. The Auto Ser­vice Shift and 7 Gaps (Auto 1). Working Paper (WP_DCL-Dru­cker-CGU_2018-05), Dru­cker Cus­to­mer Lab, Dru­cker School of Manage­ment, Cla­re­mont Gra­dua­te Uni­ver­si­ty, Cla­re­mont, CA (link)

Schlue­ter Lang­don, C., and R. Siko­ra. 2019. Crea­ting a Data Fac­to­ry for Data Pro­ducts. Pro­cee­dings of the 18th Work­shop of E‑Business at ICIS Munich (Decem­ber)

Schlue­ter Lang­don, C., and M. J. Shaw. 2002. Emer­gent Pat­terns of Inte­gra­ti­on in Elec­tro­nic Chan­nel Sys­tems. Com­mu­ni­ca­ti­ons of the ACM 45(12): 50–55

Wal­ter, S.M., T. Böh­mann, and H. Krcmar. 2007. Indus­tria­li­sie­rung der IT — Grund­la­gen, Merk­ma­le und Aus­prä­gun­gen eines Trends. HMD Pra­xis der Wirt­schafts­in­for­ma­tik 44(4) (August): 6–16