Is data being giv­en enough con­sid­er­a­tion in account­ing procedures?

Dr Angel­i­ca M. Schwarz – Pho­to: Mara Truog

Europe is evolv­ing con­tin­u­ous­ly from a man­u­fac­tur­ing to a ser­vice-ori­ent­ed soci­ety. The dig­i­tal rev­o­lu­tion is also pro­gress­ing. Are cur­rent account­ing and tax laws armed and ready for these inter­re­lat­ed challenges?

In her Duet Inter­view with legal and data expert Dr Angel­i­ca M. Schwarz, Dr Cal­daro­la, author of Big Data and Law, dis­cuss­es account­ing and tax issues in con­nec­tion with data and data-dri­ven busi­ness models.

The view con­cern­ing the mar­ket cap­i­tal­iza­tion of impor­tant stock index­es illus­trates our devel­op­ment from a clas­sic indus­try to a soci­ety focussing on ser­vice, high tech and knowl­edge, a change which high­lights data’s role as an intan­gi­ble asset and strate­gic fac­tor for suc­cess. The mar­ket val­ue of equi­ty cap­i­tal in those index­es, mir­ror­ing the expec­ta­tion of investors, exceeds the statu­to­ry declared book val­ue of the equi­ty cap­i­tal by a num­ber of mag­ni­tudes. Rea­sons for this dis­crep­an­cy between mar­ket and book val­ue include the use of undoc­u­ment­ed, intan­gi­ble finan­cial assets as well as the out­look for the future by ana­lysts. There is the assump­tion that the mar­ket cap­i­tal­iza­tion of the US stock index Stan­dard & Poor 500 demon­strates a clear trend towards an increas­ing share of intan­gi­ble assets. When observ­ing the mar­ket-to-book ratio of com­pa­nies with infor­ma­tion-based busi­ness mod­els, one can expect that the dis­crep­an­cy will keep on increas­ing. What is your opinion?

Dr Angel­i­ca M. Schwarz: In my view, we should be ask­ing the ques­tion why cer­tain intan­gi­ble assets are not reflect­ed in the bal­ance sheets at their mar­ket val­ues. The answer is sim­ple: Because com­mon account­ing reg­u­la­tions do not pro­vide for their inclu­sion. Let us con­sid­er the fol­low­ing example:

The Inter­na­tion­al Finan­cial Report­ing Stan­dards (IFRS) are based on the true and fair view prin­ci­ple. What does this mean con­crete­ly? The prin­ci­ple states that the annu­al finan­cial state­ment should enable the read­er or stake­hold­er to gain an insight into the actu­al posi­tion of assets and earn­ings of the com­pa­ny in ques­tions. The finan­cial state­ment should there­fore mir­ror the trans­ac­tions in a trust­wor­thy man­ner, name­ly, to con­vey the actu­al finan­cial sit­u­a­tion of a com­pa­ny. In con­trast to Swiss account­ing reg­u­la­tions, the IFRS fol­lows a nar­row inter­pre­ta­tion of the pru­dence prin­ci­ple. In gen­er­al, pru­dence is defined as the exer­cise of cau­tion when mak­ing judge­ments under con­di­tions of uncer­tain­ty. Thus, under the pru­dence con­cept, one should fol­low a con­ser­v­a­tive approach in record­ing the amount of assets, and not under­es­ti­mate lia­bil­i­ties. Even though the pru­dence prin­ci­ple is defined dif­fer­ent­ly, both the Swiss account­ing rules as well as the IFRS require fur­ther pre­req­ui­sites when it comes to self-cre­at­ed intan­gi­bles. The rea­son for such reluc­tance is that intan­gi­ble assets gen­er­at­ed by the com­pa­nies them­selves have not passed a mar­ket test and are there­fore deemed to be uncer­tain. Even though a val­u­a­tion may indeed be pos­si­ble (e.g., based on com­par­a­tive fig­ures from sim­i­lar trans­ac­tions), such assets have not been acquired on the mar­ket and, there­fore, no direct fair mar­ket val­ue (in the sense of a price) is avail­able. Thus, indi­vid­u­al­ly gen­er­at­ed intan­gi­ble assets have to at least pass the research and to have reached the devel­op­ment phase before a cap­i­tal­iza­tion is pos­si­ble from an account­ing perspective.

Once indi­vid­u­al­ly gen­er­at­ed intan­gi­ble assets have over­come this hur­dle and can be reflect­ed in the bal­ance sheet, the ques­tion con­cern­ing under which val­ue they should be list­ed remains open. The ini­tial assess­ment of such resources is one of cost (e.g., pro­duc­tion costs). Includ­ed are all attrib­ut­able direct costs nec­es­sary for gen­er­at­ing said asset. Here­in lies the prob­lem, in my opin­ion, because for the respec­tive com­pa­ny the pro­duc­tion costs are often low­er than the actu­al intrin­sic val­ue of the intan­gi­ble asset(s). This holds espe­cial­ly true if the Big Data archi­tec­ture (once imple­ment­ed) can be used over many years to reduce the com­pa­ny’s costs. I use the term “actu­al” delib­er­ate­ly. As men­tioned in the begin­ning, does not the true and fair view prin­ci­ple require that the actu­al posi­tion of assets and earn­ing of the com­pa­ny be reflect­ed in the bal­ance sheet? In my view, there is a cer­tain dis­crep­an­cy reflect­ed in how the prin­ci­ple is adhered to.

Com­ing back to the ques­tion you posed at the begin­ning: Yes, there is a dis­crep­an­cy in rela­tion to the mar­ket-to-book ratio and the dis­crep­an­cy will con­tin­ue to increase if com­pa­nies decide increas­ing­ly on dig­i­tal busi­ness mod­els. From an account­ing per­spec­tive, dig­i­tal assets gen­er­at­ed by com­pa­nies them­selves (such as dig­i­tal data of third par­ties (i.e. data sub­jects) col­lect­ed and processed by the com­pa­ny itself) rep­re­sent intan­gi­ble assets and as long as they can “only” be priced at pro­duc­tion cost, we will con­tin­ue to observe a dis­crep­an­cy between mar­ket and book val­ue. If this is in line with the true and fair approach and if the annu­al finan­cial state­ment can still serve as a reli­able basis for cap­i­tal mar­ket par­tic­i­pants when mak­ing busi­ness deci­sions is anoth­er question.

Does this dis­crep­an­cy also apply to the account­ing of data?

This is espe­cial­ly true when talk­ing about whether or not data can be list­ed as a busi­ness asset in the bal­ance sheet. In my doc­tor­al the­sis I present the view that data – whether indi­vid­u­al­ly gen­er­at­ed or pur­chased – may ful­fil the con­di­tions as pro­vid­ed by the IFRS in order to list them on the bal­ance sheet. The imple­men­ta­tion of a Big Data project can be very expen­sive. On the one hand, the com­pa­ny requires the nec­es­sary IT-infra­struc­ture (unless a cloud infra­struc­ture is used) and, on the oth­er hand, the cru­cial know-how. Raw data needs to be trans­formed into a cer­tain for­mat so that it can be analysed. This “refine­ment“ process takes on a cer­tain sig­nif­i­cance if data come in dif­fer­ent for­mats from var­i­ous sources and enti­ties of a multi­na­tion­al and need to be reduced to a com­mon denom­i­na­tor. The cost for such a Big Data archi­tec­ture is, from what I have seen, like­ly to be far below the actu­al val­ue of the data. The ini­tial recog­ni­tion accord­ing to IFRS for self-gen­er­at­ed data is there­fore cost-ori­ent­ed and not ben­e­fit-ori­ent­ed. The recog­ni­tion of fair mar­ket val­ue will in most cas­es not apply because an active and iden­ti­fi­able mar­ket is miss­ing. Trade with data exists but it is ques­tion­able whether such trade can already be iden­ti­fied to a suf­fi­cient degree. This, of course, may change in the future and I would not exclude that at some point the trade of data sim­i­lar to mar­ketable securities.

And what about acquired data?

The ini­tial recog­ni­tion of acquired data is based on the acqui­si­tion costs. The sit­u­a­tion here is dif­fer­ent because the data in ques­tion have, in this par­tic­u­lar con­stel­la­tion, over­come a mar­ket test and, among inde­pen­dent third par­ties, the pur­chase price cor­re­sponds usu­al­ly to the fair mar­ket value.

In your opin­ion, data can ful­fil the require­ments for being list­ed on the bal­ance sheet from an IFRS per­spec­tive. Some argue, how­ev­er, that data can­not be at the dis­pos­al of a com­pa­ny and, thus, can hard­ly be sub­stan­ti­at­ed. What is your view?

One should under­stand that the IFRS (so too the Swiss reg­u­la­tion) fol­low an eco­nom­ic approach when deal­ing with the ques­tion whether or not a cer­tain asset is at the dis­pos­al of the com­pa­ny. The require­ment that data must be at the dis­pos­al of the com­pa­ny does not nec­es­sar­i­ly pos­tu­late legal own­er­ship. Instead, this pre­req­ui­site is to be under­stood as an eco­nom­ic pow­er over the data, which can be deter­mined based on cri­te­ria such as risk, oppor­tu­ni­ty, ben­e­fit, lia­bil­i­ty, etc. Fur­ther­more, in order for data to be con­sid­ered as being avail­able for exploita­tion by a com­pa­ny, the com­pa­ny needs to have phys­i­cal or vir­tu­al access to the data pool. How­ev­er, it can­not be pre­sumed that the data car­ri­er itself must also be con­trolled at some lev­el by the com­pa­ny in ques­tion; oth­er­wise, data that is stored in an exter­nal­ly rent­ed cloud could nev­er be allo­cat­ed to a data-dri­ven cor­po­ra­tion using this kind of a tool.

Since eco­nom­ic pow­er and not nec­es­sar­i­ly legal pow­er is the rel­e­vant aspect, the ques­tion whether legal own­er­ship of data is even pos­si­ble is of a sec­ondary nature. Legal own­er­ship can, how­ev­er, cer­tain­ly be used as an indi­ca­tion of control.

Data also car­ries risks. The legal pro­cess­ing of data has not been ful­ly clar­i­fied. How should com­pa­nies han­dle such risks when pro­cess­ing data and, at the same time, infring­ing upon data pro­tec­tion law? I am think­ing of era­sure claims in accor­dance with data pro­tec­tion laws.

Data is report­ed on the asset side on a bal­ance sheet where­as risk is list­ed on the lia­bil­i­ty side. In my view, it is impor­tant to under­stand that a com­pa­ny col­lect­ing and analysing data can still ben­e­fit from such activ­i­ties even though they may infringe upon data pro­tec­tion law reg­u­la­tions. That, of course, does not mean that I give a com­pa­ny carte blanche to do what­ev­er they desire with data. What I would like to point out is that the ques­tion whether or not data can be list­ed on the bal­ance sheet needs to be eval­u­at­ed inde­pen­dent­ly of whether and to what extent the com­pa­ny should make pro­vi­sions in order to cov­er pos­si­ble future lia­bil­i­ties. The claim for era­sure, how­ev­er, may be con­sid­ered when eval­u­at­ing whether and to what extent a val­ue adjust­ment seems necessary.

Multi­na­tion­als often imple­ment their big data strate­gies for the whole group. How do you assess data trans­fer among com­pa­ny subsidiaries?

With regard to affil­i­at­ed com­pa­nies, the ques­tion aris­es whether and which price should be paid.

The ques­tion of “whether” can be answered more eas­i­ly. Tax law requires that a trans­ac­tion between affil­i­at­ed com­pa­nies needs to be real­ized at arm’s length. This means that the com­pa­ny must pay the price that a third par­ty would have paid. The log­ic behind this is to pre­vent prof­it shift­ing. Oth­er­wise, a sub­sidiary in a low-tax coun­try could be tempt­ed to make a high prof­it by sell­ing its data at a price beyond mar­ket val­ue. If the con­tract­ing par­ty is a sub­sidiary in a high-tax coun­try, it could have an inter­est in pay­ing such a high price, as the com­pa­ny can expense the costs via the prof­it and loss state­ment which, in turn, reduces its tax base. The real­i­ty today is that data trans­fers among group com­pa­nies exist while, how­ev­er, usu­al­ly no price at all is being paid. If we treat data as an asset, why should a com­pa­ny not com­pen­sate the data it receives from oth­er group com­pa­nies like any oth­er asset or ser­vice as well? That might sound neg­a­tive at first glance but there are also oppor­tu­ni­ties – name­ly where tax plan­ning is con­cerned. Sur­pris­ing­ly, this top­ic has not been of great sig­nif­i­cance either for com­pa­nies or for tax author­i­ties. The ques­tion remains for how long.

If the “whether” has been set­tled, then the next ques­tion is how the price should be deter­mined. Here a mar­ket-val­ue-ori­en­ta­tion is allowed – if not required. Dif­fer­ent to the account­ing reg­u­la­tions, an actu­al val­ue needs to be iden­ti­fied. This is the point where the inter­play between tax and account­ing reg­u­la­tions becomes very inter­est­ing: A price assessed for tax rea­sons may have a direct influ­ence on the account­ing of a com­pa­ny. My view here is that any prices which are in accor­dance with the arm’s length prin­ci­ple – although they are paid among affil­i­at­ed com­pa­nies – should be accept­ed as the pur­chase price in an indi­vid­ual finan­cial statement.

My favorite quote is:

“The art of col­lect­ing tax­es is to pluck the goose with­out it screaming”.

Max­im­i­lien de Béthune, Duc de Sully

Dr Schwarz, thank you for shar­ing your insights on data and account­ing reg­u­la­tions, trans­fer pric­ing and tax.

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 Angelica M. Schwarz

Dr Angelica M. Schwarz is a lawyer at Bär & Karrer in Zurich. She specialises in accounting and tax issues related to data and data driven business models. She publishes regularly in her domain and wrote her doctoral thesis on accounting and fiscal treatment of data.

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.