Is data the cheap­est asset available?

Dr Andreas Zech­mann – Pho­to: Peter Ruggle

The phrase “data- the fuel of the 21st cen­tu­ry” has often been bandied about and has become a catchy slo­gan for the dawn of the dig­i­tal rev­o­lu­tion. A huge amount of data is being gen­er­at­ed, col­lect­ed, and processed every sec­ond with every sin­gle click. But what is the data even worth? Who is pay­ing for such data and who receives remu­ner­a­tion? Or has data even become a “cur­ren­cy” for ser­vices per­ceived to be “free of charge”? 

In her Duet Inter­view with Dr Andreas Zech­mann, a spe­cial­ist in cor­po­rate finance, Dr Cal­daro­la, author of Big Data and Law, dis­cuss­es the val­u­a­tion of data and its cur­rent and future mar­ket price.

With the rise of the Inter­net of Things and the incor­po­ra­tion of sen­sors in objects, com­pa­nies are in a posi­tion to col­lect more and more data. Con­se­quent­ly, the amount of data is increas­ing. Back in 2018, the total data vol­ume col­lect­ed world­wide was about 33 zettabytes. Many data ana­lysts cau­tious­ly pre­dict that this data vol­ume may rise to a lev­el of 175zb by the end of 2025. So, one could say that there is an inter­est, to put it mild­ly, in data. Is data per­ceived as an asset col­lect­ed from data sub­jects and/or com­pa­nies? If so, is that asset being iden­ti­fied in account­ing regulations?

Dr Andreas Zech­mann: Due to dig­i­tal­i­sa­tion, the role of data has changed in both the pri­vate and eco­nom­ic life of human beings since data has per­me­at­ed all areas of life and industry.

You men­tioned the devel­op­ment of the world­wide data vol­ume. In my opin­ion, this trend shows the increas­ing impact of data on all of us.

The added-val­ue chain of com­pa­nies and their result­ing busi­ness suc­cess depend more and more on data. This is not only true for mod­ern dig­i­tal busi­ness mod­els, but also applies to tra­di­tion­al areas, such as, for exam­ple, infra­struc­ture com­pa­nies. I would even go so far as to say that data has become a strate­gic suc­cess fac­tor for companies.

To answer your ques­tion whether data is today per­ceived as an asset, I unfor­tu­nate­ly have to reply in the neg­a­tive: No, not to the degree that it should be. There is a strong imbal­ance, when look­ing at data as an asset. Why is that so? The answer lies in the nature of data.

When we speak of dig­i­tal data, we have to remem­ber that data has no phys­i­cal sub­stance which in turn means that data is intan­gi­ble. Data rep­re­sents infor­ma­tion on objects and sub­jects – for exam­ple, cus­tomers, ven­dors, mate­ri­als, machines etc. and fol­lows a life cycle (col­lec­tion – use – dele­tion). The ques­tion of how well data rep­re­sents infor­ma­tion on objects and sub­jects can be answered by look­ing at the qual­i­ty of its data.

The dis­cus­sion con­cern­ing the sta­tus of data as an asset is dom­i­nat­ed by the future prospects of cus­tomer and con­sumer data, because I think com­pa­nies are well aware that their cus­tomer and con­sumer data are undoubt­ed­ly assets, on which their future mon­e­tary suc­cess depends. Trade secrets, such as recipes like the for­mu­la for Coca-Cola, or pro­duc­tion meth­ods to cre­ate drugs, are per­ceived in the same way. But, when it comes to cor­po­rate data oth­er than cus­tomer data or trade secrets, my opin­ion is that com­pa­nies, for exam­ple, do not con­sid­er inven­to­ry data in their enter­prise resource plan­ning sys­tem as an (intan­gi­ble) asset – despite the inven­to­ry itself being con­sid­ered as an (tan­gi­ble) asset. I believe that the per­cep­tion of data as an intan­gi­ble asset is more pro­nounced in dig­i­tal indus­tries than in tra­di­tion­al (pro­duc­tion) indus­tries. It will be excit­ing to observe whether data is per­ceived in the same way as pro­duc­tion halls, machines and facil­i­ties in the years to come.

I def­i­nite­ly still need to answer your ques­tion as to whether the cur­rent account­ing rules per­ceive data as an asset and, once again, I must reply in the neg­a­tive. The rea­son is a his­tor­i­cal one, since cur­rent account­ing reg­u­la­tions were estab­lished in the ear­ly 20th cen­tu­ry when there was a strong empha­sis on tan­gi­ble assets, such as machines or prop­er­ties, and intan­gi­ble but “trace­able” assets, like licens­es, rights, con­ces­sions and bonds. The con­cept of data, as we know it now, was not yet con­ceived of with­in struc­tures which had evolved much before the dig­i­tal revolution.

In addi­tion, the Inter­na­tion­al Finan­cial Report­ing Stan­dards (IFRS), that came into effect much lat­er, name­ly, in the ear­ly 21st cen­tu­ry, bal­anced intan­gi­ble goods only as intan­gi­ble assets when a com­pa­ny could prove that the asset would pro­vide a future eco­nom­ic ben­e­fit stem­ming from its use. And that is the crit­i­cal point as far as the top­ic of data is con­cerned: to eval­u­ate and quan­ti­fy pro­duc­tion and acqui­si­tion costs. Con­se­quent­ly, a com­pa­ny’s data is not viewed as an intan­gi­ble asset either because its future eco­nom­ic ben­e­fits can­not be proved or because the mon­e­tary pro­duc­tion cost of self-gen­er­at­ed data can­not be quan­ti­fied, as is the case of com­put­er-gen­er­at­ed mass data, e.g., cre­at­ed by the use of sen­sors. I think can is rel­a­tive because it is sim­ply not done in practice.

In my PhD dis­ser­ta­tion, I con­sid­ered how to assess the val­ue of data gen­er­at­ed by a com­pa­ny in order to ren­der it into a con­crete asset for the com­pa­ny in ques­tion. My main argu­ment was that only data that is actu­al­ly used by the rel­e­vant com­pa­ny and is of high data qual­i­ty and pro­vides a pos­i­tive val­ue propo­si­tion for the com­pa­ny at the end of the added-val­ue chain, can thus be said to have a finan­cial value.

This then sets the stage for data becom­ing intan­gi­ble fixed assets which can be bal­anced and depre­ci­at­ed just like oth­er (tan­gi­ble) assets. I delib­er­ate­ly refer to “self-gen­er­at­ed” data because acquired data has a pur­chase price.

The mere pres­ence of data does not auto­mat­i­cal­ly ensure its finan­cial worth. Only data that is found to be of some use in the future can meet the cri­te­ria of being a data asset and be worth some­thing. A good exam­ple would be data con­cern­ing a cus­tomer who hasn’t pur­chased any­thing for 10 years, where the invoice address is incor­rect, or where the last pur­chased prod­uct is already out­dat­ed. I think this thought is con­sis­tent with the IFRS because, from the company’s point of view, this data will most like­ly not gen­er­ate any future mon­e­tary ben­e­fits; per­haps we can com­pare such data to per­ish­able goods from the food industry.

My opin­ion is:


“The mere pres­ence of data does not insure their finan­cial val­ue. Only data, which are in some way and at some time found to be use­ful, ful­fill the require­ments of being data assets and thus can be said to be worth some­thing, finan­cial­ly speaking.”

Dr Andreas Zechmann

In order to define data as an asset in accor­dance with account­ing reg­u­la­tions, data needs to have a mar­ket val­ue. How is or how can data (be) eval­u­at­ed and do com­pa­nies’ con­trol­ling depart­ments active­ly val­u­ate and mon­e­ta­rize the data? Is the process either tied to obtain­ing addi­tion­al prof­its or to cost savings?

As men­tioned ear­li­er, the acqui­si­tion or pro­duc­tion costs are rel­e­vant for the finan­cial val­ue of an intan­gi­ble asset. That means that the val­u­a­tion either results from the price that has been paid when the good was pur­chased or the cost that accrued when the good was man­u­fac­tured, for exam­ple, per­son­nel costs involved.

Let us con­sid­er soft­ware as an exam­ple and the ratio­nale behind this pro­ce­dure becomes clear: either it is the price of soft­ware when pur­chased or the cost for the pro­gram­ming of in-house gen­er­at­ed soft­ware; such things can be quan­ti­fied because the pro­gram­ming start­ed and end­ed at a cer­tain point.

The dis­cus­sion becomes more dif­fi­cult when data is involved: The eas­i­est and most objec­tive data val­ue would be the mar­ket price of a non-monop­oly mar­ket with sev­er­al sup­pli­ers. This would be fea­si­ble for cus­tomer and con­sumer data since such mar­kets exist, so that pur­chased cus­tomer data could be val­ued in line with the pur­chase price, as with any oth­er tan­gi­ble good and, of course, could depre­ci­ate over its life cycle. One could even eval­u­ate cus­tomer data in accor­dance with a poten­tial pur­chase price that a third per­son would have paid (so-called “mar­ket test”).

But what hap­pens with oth­er data that is typ­i­cal­ly self-gen­er­at­ed by the com­pa­ny and not acquired from a mar­ket, such as screws of a plant man­u­fac­tur­er? For self-gen­er­at­ed data a mar­ket test is not applic­a­ble, so that we must find alter­na­tive ways of deter­min­ing a finan­cial data val­ue. One pos­si­bil­i­ty is to quan­ti­fy the costs in the process used by a com­pa­ny to col­lect and main­tain the data in question.

But, at this point we leave the realm of objec­tive val­u­a­tion and enter into sub­jec­tive val­u­a­tion with the result that there is no plau­si­ble mon­e­tary val­ue for that data. It is obvi­ous that, for exam­ple, “cus­tomer base” data sets can be appraised based on their cost by dis­sect­ing the col­lec­tion process of a data set. For exam­ple, how many fields are includ­ed in a cus­tomer data set? How much time is need­ed to fill in these fields? How much is the hourly rate for the employ­ee in charge of fill­ing in the cus­tomer data? I would like to add this thought: if we have obtained a cost-based val­u­a­tion in this way, we still don’t have a con­tin­u­ous­ly increas­ing finan­cial asset just by col­lect­ing data; mean­ing that, even if a data set is copied a mil­lion times, the finan­cial val­ue of the data set and its copies would not increase by a fac­tor of a mil­lion. On the con­trary, it remains con­stant. In my opin­ion, cost-based finan­cial val­u­a­tion is only par­tial­ly applic­a­ble for data.

Con­se­quent­ly, if a mar­ket-price-based val­u­a­tion of data does not seem to be suit­able, the com­pa­ny should then, in my opin­ion, attach a val­ue that is in line with the advan­tages aris­ing from the data use. Sim­ply stat­ed, data that is often used and that there­fore sub­stan­tial­ly con­tributes to the added val­ue has a high­er worth than data that is not or only used sporadically.

In my PhD the­sis, I com­pared dif­fer­ent val­u­a­tion con­cepts and I came to the con­clu­sion that the finan­cial val­ue of data should arise out of a use-based val­u­a­tion approach con­sid­er­ing the rev­enues that a com­pa­ny could earn and the invest­ments that a com­pa­ny could save on through effi­cient process­es. Use-based means in this con­text that a com­pa­ny focuss­es explic­it­ly on the use of data in select­ed busi­ness process­es, e.g., the role of cus­tomer address data in an invoic­ing process of a com­pa­ny. High-qual­i­ty data has greater poten­tial to be used and be prof­itable through its effi­cien­cy than low­er qual­i­ty data. It must be admit­ted, how­ev­er, that data val­ue derived in this way is sub­jec­tive and not com­pre­hen­sive because of the spe­cif­ic process being focussed on. By look­ing at finan­cial data val­ues in this way, we have the advan­tage of the val­ue now being strong­ly linked to its use and thus being sep­a­rat­ed from the dis­cus­sion that mere­ly col­lect­ing data results in an intan­gi­ble asset because of accru­ing pro­duc­tion costs.

I would like to add that assess­ing the finan­cial data val­ue is not a means to an end. Val­u­a­tion is only a first step lead­ing to post-pro­cess­ing activ­i­ties. All meth­ods for eval­u­at­ing data should sup­port any post-pro­cess­ing val­u­a­tion pur­pose. At the moment, data val­u­a­tion has­n’t real­ly become com­mon in the finan­cial com­mu­ni­ty which is cur­rent­ly oper­at­ing accord­ing to tra­di­tion­al finan­cial Key Per­for­mance Indi­ca­tors. There are a num­ber of rea­sons for this, includ­ing, for exam­ple, the impor­tance of account­ing in cap­i­tal mar­ket communication.

Per­son­al data stems from data sub­jects and non-per­son­al data from objects owned by a pro­pri­etor or a group there­of. How­ev­er, only com­pa­nies are col­lect­ing data – whether of a per­son­al or an imper­son­al nature. They are invest­ing in infra­struc­ture for col­lect­ing and pro­cess­ing data and they are using the data of oth­ers for their inter­nal and exter­nal pur­pos­es. Is there any mon­e­tary exchange going on between the “data-own­er”, on the one hand, and the data-col­lec­tor and data-exploiter, on the oth­er. After all, we are famil­iar with trade of this nature for oth­er raw mate­ri­als that com­pa­nies purchase.

Your com­par­i­son between data and raw mate­r­i­al for busi­ness process­es is, in my opin­ion, very apt. Data has devel­oped from sim­ply being a raw mate­r­i­al to becom­ing an enabler of process­es and, from there, devel­op­ing into an enabler of prod­ucts and ser­vices and, even beyond that, to data as a prod­uct. This is a new dimen­sion which does not exist for tra­di­tion­al tan­gi­ble raw materials.

This devel­op­ment leads to an increas­ing­ly large debt imbal­ance with regard to mon­e­tary com­pen­sa­tion between data col­lec­tor, data own­er and data exploiter. In my view, when we omit social media plat­forms, where pri­vate per­sons offer their data free of charge, and there is a strong diver­gence among these three roles, then the prob­lem does not real­ly arise in a cor­po­rate con­text. Here the com­pa­nies them­selves occu­py, in my opin­ion, all three roles, so that the prob­lem of finan­cial com­pen­sa­tion does not arise. Nev­er­the­less, with regard to social plat­forms, such as, for exam­ple, Face­book, the data own­er / data sub­ject could be com­pen­sat­ed finan­cial­ly, even if we are only talk­ing about tri­fling amounts.

Basi­cal­ly, when dis­cussing data, the chal­lenge is that data can eas­i­ly be copied with­out accru­ing addi­tion­al costs. There­fore, deter­min­ing data con­trol is more dif­fi­cult than is the case for tan­gi­ble goods that can be secured (secure in the sense of not let­ting third par­ties into a ware­house) and can­not be copied. An essen­tial fea­ture of tan­gi­ble assets is their short­age: they are “valu­able” because they are not acces­si­ble to every­one and are some­how scarce. If one looks, for exam­ple, at freely acces­si­ble data, such as “open data”, the ques­tion aris­es as to who, if any­one, owns such data. To be sure, this type of data has a data sub­ject, but it still rep­re­sents, in my opin­ion, a free asset avail­able to all and belong­ing to no one. There­fore, I am not sur­prised that there is no mon­e­tary com­pen­sa­tion being dis­trib­uted among the dif­fer­ent roles.

Per­haps an exam­ple will make my answer clear: A pho­tog­ra­ph­er tak­ing a prizewin­ning pho­to of the Mat­ter­horn in Switzer­land does not pay the Can­ton of Wal­lis com­pen­sa­tion for the rev­enues he makes because his pho­to has been includ­ed in a com­mer­cial pho­to cal­en­dar. Every­body could take such a pho­to if s/he wants to. I view the inter­net in the same way: A com­pa­ny mar­kets data which every­one has access to. Every­body could do the same thing, but only a few actu­al­ly do, there­by earn­ing mon­ey with its usage.

Assum­ing that data has a val­ue attached to it, what are the actu­al mar­ket prices for data? Do spe­cial cat­e­gories and respec­tive mon­e­tary ranges exist? Are they sta­ble or do they increase over time? Can you pro­vide some examples?

I think the top­ic whether data is trad­ed on mar­kets is only rel­e­vant for data, for which suf­fi­cient prospec­tive buy­ers exist, such as, for exam­ple, for cus­tomer and con­sumer data. In my opin­ion, the going mar­ket prices for cus­tomer data sets offered by large infor­ma­tion ser­vice providers are in the range between € 0.5 and € 2.0, to name one exam­ple. But let me give you a coun­terex­am­ple with respect to our mar­ket dis­cus­sion: Only a few prospec­tive buy­ers exist for data relat­ed to load-bear­ing strength of con­struc­tions. Con­se­quent­ly, this data is unlike­ly to be sold or pur­chased. How­ev­er, for rail­way com­pa­nies this data is cru­cial for main­tain­ing their sys­tems and, there­fore, for their added val­ue chain. No train would move if infor­ma­tion from con­struc­tion data were miss­ing. My point here is that the dis­cus­sion on mar­ket price of data is only part of the whole dis­course con­cern­ing finan­cial data valuation.

With regard to finan­cial data val­ues, I would like to add that I have applied cost-based and usage-based approach­es for com­pa­nies. The cost-based approach gen­er­at­ed around 38 CHF for each mate­r­i­al mas­ter data at Swiss indus­tri­al com­pa­nies and between 11 and 30 CHF for each cus­tomer mas­ter data. When apply­ing the usage-based approach, the finan­cial val­ue amount­ed to about 36 CHF for cus­tomer data of a pow­er tool sup­pli­er and rough­ly 5 CHF for con­struc­tion data of a rail­way com­pa­ny for each data set. It is inter­est­ing to note that the val­ues for each data set quick­ly reach an asset val­ue in the range of dou­ble-dig­it mil­lions in cas­es of 100 000+ cus­tomer data sets. These are sig­nif­i­cant val­ues in the bal­ance sheet of an enterprise.

But com­ing to your ques­tion about price devel­op­ment, one must be clear about the fact that data is strong­ly con­nect­ed to its time­li­ness, mean­ing that obso­lete data, such as incor­rect infor­ma­tion about real objects, isn’t worth any­thing. Nobody would buy data that has “expired”. But I also believe that, as the data trade becomes more impor­tant, more mar­kets will grow, and the top­ic will receive more atten­tion, as dig­i­tal busi­ness mod­els become more essential.

Let me add the fol­low­ing thought with regard to the dis­tinc­tion between price and val­ue. The val­ue cor­re­sponds to the price only when the lat­ter cor­re­sponds to a mul­ti­tude of expec­ta­tions of the poten­tial buy­ers. If this is not the case, and, if only a few buy­ers exist, then the mar­ket price is one for a prod­uct offered on the mar­ket, but this val­ue is not a reli­able indi­ca­tor for an objec­tive mar­ket price.

Many dig­i­tal busi­ness mod­els work in the fol­low­ing way: a data sub­ject allows access and pro­cess­ing of his/her data and receives, in return, a “free” ser­vice. For exam­ple, a lot of peo­ple use “free” mes­sag­ing, email ser­vices or cloud space and, in return, let the provider use their data. Has data become a cur­ren­cy to “pay” for cer­tain ser­vices? Or is data the cheap­est, if not the smartest, “stolen” asset?

The idea that data is a stolen asset sim­ply mud­dies the water in terms of what the val­ue of data actu­al­ly is and what should be paid for it.

The fact is, data can be copied an unlim­it­ed num­ber of times if enough stor­age space is avail­able. Repro­duc­tion costs remain neg­li­gi­ble because capac­i­ties are so big.

If users pro­vide their data with­out remu­ner­a­tion and make use of a ser­vice at no charge to them­selves, an exchange has tak­en place, with the provider hav­ing got­ten the bet­ter deal since s/he can gen­er­ate rev­enues on his/her web­site through adver­tis­ing. In the strictest sense of the word, it is a fair “exchange” and not a theft. But if I go back to your ques­tion on data being the “smartest” stolen asset, then I have to agree that the user has most like­ly been enticed into pro­vid­ing his/her data at no charge for a “free” or dis­count­ed ser­vice. I assume the user is not aware of the fact that s/he has agreed to a barter trans­ac­tion or, if s/he is cog­nizant of the impli­ca­tions, that s/he has con­scious­ly accept­ed the deal.

An own­er of a mine usu­al­ly sells his/her raw mate­r­i­al or leas­es his/her mine to some­body so that the lease­hold­er can exploit the mine/raw mate­r­i­al. The own­er of a mine, there­fore, receives mon­ey either for the raw mate­r­i­al or for the lease. If we trans­fer this exam­ple to data, are data sub­jects receiv­ing mon­ey for their data? Or dif­fer­ent­ly put, should a data sub­ject con­scious­ly put a price on his/her data? Are there any busi­ness mod­els or cur­rent trends where data sub­jects can active­ly mon­e­ta­rize their data? Or do only com­pa­nies – data col­lec­tors – makes these sorts of com­mer­cial deals?

In my opin­ion, there is no con­scious data mon­e­ta­riza­tion tak­ing place on the part of data sub­jects / con­sumers / cus­tomers. We can­not even observe a trend lead­ing to any change in this sit­u­a­tion because our herd behav­iour always seems to be on the alert for free dis­pos­able inter­net-based ser­vices (e.g., mail­ing, YouTube), for which the provider earns adver­tise­ment revenues.

I could imag­ine con­sumers receiv­ing cred­it vouch­ers for tri­fling amounts for their data being fur­ther processed beyond the orig­i­nal trans­ac­tion. Also pos­si­ble is that con­sumers delib­er­ate­ly offer their data on plat­forms for mar­ket­ing and adver­tise­ment pur­pose, but also here we would still be talk­ing about pock­et change. In my opin­ion, data trade remains a bulk busi­ness where a huge amount of data is pur­chased with the knowl­edge that a cer­tain per­cent­age is use­less because it is obsolete.

For this rea­son, the pro­fes­sion­al mon­e­tari­sa­tion of data will prob­a­bly remain in the B2B sec­tor, even if the data own­er / data sub­ject does not par­tic­i­pate in the rev­enues accrued.

Not only pri­vate com­pa­nies col­lect and process data. In some coun­tries, gov­ern­ments are also col­lect­ing and using data – and not just to ful­fil their ser­vices and their secu­ri­ty oblig­a­tions. Do gov­ern­ment also “pay” or should they be offer­ing some form of remu­ner­a­tion for data?

I imag­ine you are allud­ing to the Social Cred­it Sys­tem in Chi­na. The sys­tem serves, in my opin­ion, to mon­i­tor the pop­u­la­tion and to reward some peo­ple for loy­al behav­iour. This is a spe­cial case that works in a “micro­cosm” of a total­i­tar­i­an state, such as Chi­na. The Chi­nese gov­ern­ment does not wish to gen­er­ate rev­enue through the trade of data.

Oth­er­wise, I can­not think of any oth­er exam­ples where gov­ern­ments delib­er­ate­ly col­lect data to mon­e­tarise or trade data. In my view, gov­ern­men­tal activ­i­ties should focus on ser­vices, such as edu­ca­tion, safe­ty, infra­struc­ture, main­te­nance of pub­lic life as well as med­ical and san­i­ta­tion services.

Dur­ing the pan­dem­ic, we have expe­ri­enced in a pos­i­tive way how gov­ern­ments can become a sort of data octo­pus that spreads its ten­ta­cles and sucks up all the data it can find for a con­crete pur­pose, in this case, for the Coro­na-Warn­ing-App. My point is that even this exam­ple is based on cit­i­zens being will­ing to pro­vide their data, which they do par­tial­ly because they believe their data is in safe gov­ern­men­tal hands. I don’t think the Coro­na-Warn­ing-App would work if cit­i­zens thought gov­ern­ments gen­er­at­ed trade by using per­son­al data. A breach of trust between cit­i­zen and gov­ern­ment would put the sys­tem of vol­un­tary data pro­vi­sion at risk and lead to gov­ern­men­tal data trade no longer functioning.

My final thought on this mat­ter is that gov­ern­ments should not trade data so that the ques­tion of pay­ing data sub­jects nev­er even comes up.

Yuval Noah Harari stat­ed in 21 Lessons for the 21st Cen­tu­ry that: “Big Data algo­rithms might cre­ate dig­i­tal dic­ta­tor­ships in which all pow­er is con­cen­trat­ed in the hands of a tiny elite while most peo­ple suf­fer not from exploita­tion, but from some­thing far worse – irrel­e­vance.” and “It is much hard­er to strug­gle against irrel­e­vance than against exploita­tion.” Should a data sub­ject, there­fore, start to mon­e­tarise his/her data in order to active­ly shape his/her par­tic­i­pa­tion in the dig­i­tal mar­ket and to fight against the exploita­tion and irrel­e­vance of his/her data?

The ques­tion seems, in my opin­ion, to be rather sen­sa­tion­al­ist. You stat­ed, first of all, that there was a mar­ket for data sub­jects to mon­e­tarise their data and, fur­ther­more, that data sub­jects have access to this mar­ket. In my view, this sit­u­a­tion does not hold true due to the nature of inter­net-based ser­vices, since they are formed in a huge radius involv­ing reg­is­tered users, vis­i­tors and clicks. I do not think data sub­jects can par­tic­i­pate in this bulk busi­ness- par­tic­u­lar­ly because data sub­jects prof­it from this mech­a­nism, for exam­ple, post­ing of What­sApp at no charge as opposed to pay­ing for tex­ting. The spir­it of the inter­net – name­ly speed, sim­plic­i­ty and flex­i­bil­i­ty (e.g., Uber)- has its price: name­ly, the end-user pro­vid­ing his/her data at no cost; on the plus side for the end-user: ser­vices pro­vid­ed at no charge. Mean­while, what does the provider get out of it? Pos­si­ble rev­enues from the data trade minus the cost for the ser­vice provided.

Dr Zech­mann, thank you for shar­ing your insights on the val­ue of data.

Thank you, Dr Cal­daro­la, and I look for­ward to read­ing your upcom­ing inter­views with rec­og­nized experts, delv­ing even deep­er into this fas­ci­nat­ing topic.

About me and my guest

Dr Maria Cristina Caldarola

Dr Maria Cristina Caldarola, LL.M., MBA is the host of “Duet Interviews”, co-founder and CEO of CU³IC UG, a consultancy specialising in systematic approaches to innovation, such as algorithmic IP data analysis and cross-industry search for innovation solutions.

Cristina is a well-regarded legal expert in licensing, patents, trademarks, domains, software, data protection, cloud, big data, digital eco-systems and industry 4.0.

A TRIUM MBA, Cristina is also a frequent keynote speaker, a lecturer at St. Gallen, and the co-author of the recently published Big Data and Law now available in English, German and Mandarin editions.

Dr Andreas Zechmann

Dr Andreas Zechmann obtained his Master's degree in Business Administration from Augsburg University (Germany), having specialised in Finance and Information Management. After having obtained his degree, Dr Zechmann collected practical experience as a consultant for one of the "Big Four" auditing companies as well as being employed at a consulting company, with a particular focus on corporate finance. Having written a practice-oriented dissertation entitled «Use-Based Data Valuation - Developing and Applying a AHP-Based Concept for the Financial Valuation of Data Assets». Dr Zechmann was awarded his doctorate (Phd) by the University of St. Gallen (Switzerland). He has been employed as a controlling expert for Lechwerke AG (an Energy Supply company in Germany) since 2017.

Dr Maria Cristina Caldarola

Dr Maria Cristina Caldarola, LL.M., MBA is the host of “Duet Interviews”, co-founder and CEO of CU³IC UG, a consultancy specialising in systematic approaches to innovation, such as algorithmic IP data analysis and cross-industry search for innovation solutions.

Cristina is a well-regarded legal expert in licensing, patents, trademarks, domains, software, data protection, cloud, big data, digital eco-systems and industry 4.0.

A TRIUM MBA, Cristina is also a frequent keynote speaker, a lecturer at St. Gallen, and the co-author of the recently published Big Data and Law now available in English, German and Mandarin editions.