{"id":526,"date":"2020-04-19T16:17:05","date_gmt":"2020-04-19T14:17:05","guid":{"rendered":"https:\/\/digitaleconomy.org\/?p=526"},"modified":"2020-04-19T16:17:11","modified_gmt":"2020-04-19T14:17:11","slug":"the-economic-characteristics-of-data","status":"publish","type":"post","link":"https:\/\/fromdatatoimpact.com\/index.php\/2020\/04\/19\/the-economic-characteristics-of-data\/","title":{"rendered":"The Economic Characteristics of Data"},"content":{"rendered":"\n

by Alexandra Ritter & Florian Stahl<\/a> <\/p>\n\n\n\n

<\/div>\n\n\n\n

\nThrough the digital transformation, a new kind of data-driven economy\nhas emerged \u201cbased on the datafication of virtually any aspect of\nhuman, social, political and economic activity\u201d \u0007(Ciuriak 2018)\b.\nDigital Data is considered to be the new capital as it has\ntransformed numerous aspects of the economy, simply owing to its\nunique and distinct characteristics that differ dramatically to\nanalog datasets \u0007(Cukier 2010)\b. For instance, real-time tracking of\nan individual\u2019s location is possible through digital GPS\ntechnologies, giving companies micro insights on consumer\u2019s\npreferences. Hence, digital data is becoming an important economic\nasset for companies\u2019 corporate performance and growth \u0007(Chase, JR.\n2013)\b. The effective transformation of data into valuable insights\ncan lead to \u201ca new type of competitive advantage\u201d \u0007(Lambrecht and\nTucker Catherine 2017)\b. In order to conduct such a transformation\neffectively, firms need to gain an understanding of the specific data\ncharacteristics. \n<\/p>\n\n\n\n

\nThis blog post intends to provide a\nconcise overview of the most important economic attributes of digital\ndata and aims to analyze the multiple trade-offs that firms face\nwithin the new digitalized world. \n<\/p>\n\n\n\n

\nDefinition of Data<\/h2>\n\n\n\n

Big Data is a concept with multiple definitions that vary depending on the context. However, it is typically described by three \u2013 or recently often by four \u2013 \u201cV\u2019s\u201d, which are considered to form the base of its definition: the volume of data, the variety of gathered data, the velocity at which data is collected and the degree of veracity \u0007(Gandomi and Haider 2015)\b. Moreover, data is also considered to be a non-rival intangible asset made of bits which is a key distinction to goods made of atoms \u0007(Goldfarb and Tucker 2019)\b. Additionally, several data definitions include the interplay of processing technology and analytical methods as necessary prerequisites to utilize data \u0007(e.g. Boyd and Crawford 2012)\b. The following figure summarizes the understanding of big data in this blog post:<\/p>\n\n\n\n

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Figure 1:<\/strong> The Four V\u2019s of Big Data\/Big Data Definition <\/em><\/figcaption><\/figure><\/div>\n\n\n\n

\nDistinction of Data,\nInformation, and Knowledge<\/h2>\n\n\n\n

\nIn everyday conversations, data, information, and knowledge are often\nused interchangeably, even though they refer to different economic\ngoods. Therefore, the purpose of this section\nis the definition and demarcation of these terms to gain a\nbetter understanding of their effect on the economy. \n<\/p>\n\n\n\n

First of all, data, information, and knowledge are all intangible assets, each having its own characteristics, creating different kinds of utility \u0007(Boisot and Canals 2004)\b. Information is \u2013 compared to physical goods \u2013 non-rival and can be divided in bit strings, such as 110001 \u0007(Jones and Tonetti 2018)\b. Data is a type of information \u201cdefined at the syntactic level\u201d \u0007(Duch-Brown, Martens, and Mueller-Langer 2017)\b, meaning that it can be interpreted as the grammar of a language or the arithmetic operators in mathematics. In turn, the term information is described by \u0007Duch-Brown, Martens, and Mueller-Langer\b \u0007(2017)\b as \u201cthe semantic content that can be extracted from data or signals.\u201d Information transforms the syntax into a meaningful context and thereby changes expectation or current knowledge. This extraction is only possible with prior structural and contextual knowledge \u0007(e.g. Boisot and Canals 2004)\b. With this knowledge of numbers and arithmetic operators, a person can, for instance, read a formula. Summarized, data becomes information when it is processed, organized, analyzed and placed in a meaningful context. Information is transformed data that can be analyzed with the help of contextual and structural knowledge. The interplay of these terms is visualized in the following figure.<\/p>\n\n\n\n

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Figure 2<\/strong>: The interplay of data, information, and knowledge \u0007(Boisot and Canals 2004) <\/em><\/figcaption><\/figure><\/div>\n\n\n\n

\nCharacteristics of Data Value<\/h2>\n\n\n\n

Data by itself is not valuable. But its characteristics, in combination with a firm\u2019s technical ability to analyze datasets by using experiments and algorithms, can generate valuable insights, create profit-enhancing opportunities and form the basis for strategic actions \u0007(Lambrecht and Tucker Catherine 2017)\b. A firm\u2019s challenge is the identification of critical pieces of data in large data pools originating from various sources. In addition, data can only be effectively utilized by accounting for its quality features and recognizing them. \u0007Janssen, van der Voort, and Wahyudi\b \u0007(2017)\b underline that the better firms integrate suitable systems to handle and transfer big data, the easier it becomes to draw valuable insights from data analysis. This leads to the identification of new market drivers, key performances indicators and consumer demand patterns. These insights maximize the efficiency of organizational processes and the alignment between demand and supply.<\/p>\n\n\n\n

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Figure 3:<\/strong> Overview of the characteristics of data value <\/em><\/figcaption><\/figure><\/div>\n\n\n\n

Volume of Data: <\/h3>\n\n\n\n

\nWhen thinking about big data, volume usually comes up first to\npeople\u2019s mind. Titles like \u201cData, data everywhere\u201d \u0007(Cukier\n2010)\b highlight the immense amount of data that is available in\ntoday\u2019s world. \n<\/p>\n\n\n\n

\nCompanies like Walmart handle over one million customer transactions\nevery hour which equals more than 2.5 petabytes of data \u0007(Cukier\n2010)\b. Rich datasets on their current or potential customers are \u201can\ninformation mine\u201d \u0007(Ciuriak 2018b)\b if data noise can be\nsuccessfully excluded from the analysis. Therefore, a large amount of\nanalyzed data improves a firm\u2019s ability to predict general trends\nas well as individual preferences \u0007(Acquisti 2014; Acquisti and\nCollege 2010)\b. However, the value density of data is falling when\nthe volume increases, which demands an increased and more\ntime-consuming analysis. Therefore, firms have to consider how to\ndeal with a massive volume of data. \n<\/p>\n\n\n\n

Economies of Scale of Data:<\/h3>\n\n\n\n

\nHowever, an advantage of large datasets are the resulting economies\nof scale created through efficiencies which are formed by volume, not\nby variety. Average production costs are falling as the volume of\noutput increases \u0007(Goldhar and Jelinek 1983)\b.<\/p>\n\n\n\n

\nEconomies of scale are steep in the data-driven market place in\nvirtue of low distribution costs of digitalized products, (near) zero\nproduction costs \u0007(Rifkin 2014)\b and near-frictionless commerce. \nAdditionally, collecting data advances data-driven services, which in\nturn may attract more customers, leading to more accessible data to\ncollect. For example, the more people use Google as a search engine,\nthe more accurate the services become as more data can be gathered.\nThis positive \u2018feed-back-loop\u2019 enables stronger companies to\ncement their market position while weakening smaller firms \u0007(OECD\n2015; Roxana Mihet and Thomas Philippon 2018)\b. \n<\/p>\n\n\n\n

Economies of Scope of Data:<\/h3>\n\n\n\n

\nEconomies of scope are the advantages that can result from producing\nor distributing multiple products within similar processes \u0007(Chandler\nJR. and Hikino Takashi 1994)\b. For example, a fashion outlet that\nsells clothes can in addition sell shoes, jewelry, and coffee and can\ntherefore achieve economies of scope through diversification\n\u0007(Goldhar and Jelinek 1983)\b.<\/p>\n\n\n\n

\nMerging related datasets may lead to extracting more insights than\ninterpreting and contextualizing the datasets separately would.\nMoreover, the costs of abstracting knowledge from the datasets\ndecrease. For example, studying Business Administration and reading\nBusiness related literature increases the complexity and knowledge of\na student as the lectures of the professors and the literature are\noverlapping. Furthermore, merging data\nfrom adjacent areas can also create economies of scope. Combining the\ndata extracted from a person\u2019s location with their shopping\nbehavior and their pay data might enable firms to use the added\ninsights to create more individualized advertising. \u0007(Duch-Brown,\nMartens, and Mueller-Langer 2017)\b Having a more diversified set of\ndata leads to a positive \u2018feed-back-loop\u2019 on which a firm can\ncapitalize.<\/p>\n\n\n\n

\nEconomies of scope and economies of scale of digital data are the\nreason why firms are \u201cdata-hungry\u201d\u0007(Duch-Brown, Martens, and\nMueller-Langer 2017)\b and explain data trade and mergers, such as\nFacebook and WhatsApp or Google and DoubleClick. By extending the\ncollection of data, firms are able to match data better and thereby\ntrack and analyze customer behavior and preferences in a way that was\nnot possible before the merger. Therefore, mergers may create entry\nbarriers because competitors cannot replicate the information derived\nfrom the merged datasets. However, at some point, economies of scope\nmight only add little value and lead to diminishing returns\n\u0007(Duch-Brown, Martens, and Mueller-Langer 2017)\b.<\/p>\n\n\n\n

Searchability of Data:<\/h3>\n\n\n\n

It is\noften tempting for firms to collect a tremendous amount of data\nbecause the cost of looking for data is near zero. Due to online\nsearch engines like Google or Yahoo, every person can search the\ninternet and find and compare information within seconds. Hence, the\nrange and quality of search are enhanced along with the ability to\ncompare prices and product variety. Firms theoretically encounter\nhigher transparency of prices but the endogenous characteristic of\nsearch costs give companies the opportunity to manipulate the web\nbrowsing to increase their surplus. Moreover, low search costs\npositively affect variety as it is easier to discover firms with\nfairly unknown niche products which increases their brand awareness\nand gives them the opportunity to increase their margin.<\/p>\n\n\n\n

Interoperability of Data:<\/h3>\n\n\n\n

\nDigital technology has changed how information is produced, stored,\ndistributed and transported. The storage, replication,\ntransportation, search and verification costs of data have decreased\nto a minimum, enabling high connectivity between and interoperability\nof digital datasets \u0007(Duch-Brown, Martens, and Mueller-Langer 2017)\b.<\/p>\n\n\n\n

\nInteroperability is \u201cthe ability of two or more systems to exchange\n(\u2026) info and\/ or data and subsequently, be able to use it\u201c\n\u0007(Duch-Brown, Martens, and Mueller-Langer 2017)\b. For instance,\ntaxpayers\u2019 data can be combined with their social security number\nor social media accounts to detect fraudulent tax payers \u0007(Janssen,\nvan der Voort, and Wahyudi 2017)\b.<\/p>\n\n\n\n

Data as an Information Good:<\/h3>\n\n\n\n

\nBig data is an information good that is non-rival and partly\nexcludable \u0007(Jones and Tonetti 2018)\b, raising debates about privacy\nprotection laws and welfare. \n<\/p>\n\n\n\n

Non-rivalry of data:<\/h3>\n\n\n\n

\n. Non-rivalry refers to the characteristic of a good that can be\nutilized by multiple parties at the same time without loss of utility\nfor anyone \u0007(Lambrecht and Tucker Catherine 2017; OECD 2015)\b. An\nanalogy for a rival good is a cup of coffee. If one party takes a sip\nof it, the utility of the coffee is diminished for other parties. In\ncontrast, a common example of a non-rival good is that a \u201cperson\ncan start a fire without diminishing another\u2019s fire\u201d \u0007(e.g.\nGoldfarb and Tucker 2019)\b. If a firm is using cookies to track a\nperson\u2019s online behavior, other firms can still use this data to\ninterpret this person\u2019s online traffic. A number of firms or\nalgorithms can use data simultaneously without diminishing its\namount. The costs of replicating digital goods are zero. Therefore,\ndigital data has been found to be non-rival by nature \u0007(e.g.\nDuch-Brown, Martens, and Mueller-Langer 2017)\b .<\/p>\n\n\n\n

Excludability and accessibility of data:<\/h3>\n\n\n\n

\n. Several authors (e.g. \u0007Acquisti and College 2010\b) have discussed\nexcludability, privacy issues, and ownership of data and whether\ngovernmental restrictions lead to an increase or decrease of welfare\nfor the society. The recent academic debate has especially focused on\nthe question if non-personal data should be\nprotected through a new legal framework and how to manage access to\nsuch data.<\/p>\n\n\n\n

\nNon-personal data cannot be backtracked to individuals \u0007(Surblyte\n2016)\b, for example traffic data. Semi-personal\ndata is data that is anonymized but can be traced back to individuals\nthrough technological tools. Analyzing semi-personal data can reveal\npatterns of behavior or even preferences of particular products of\nindividuals \u0007(Surblyte 2016)\b.\nTherefore, semi-personal data is considered to be the most\ninteresting data for enterprises.\nHowever, before discussing access to non-personal data, the\nquestion of whether or not data can be excluded has to be answered\nfirst.<\/p>\n\n\n\n

\n\u201cA good or an asset is excludable if you can prevent somebody from\nusing it\u201d \u0007(Roxana Mihet and Thomas Philippon 2018)\b. Physical\nassets are excludable. For instance, whenever you close your\nrestaurant, you exclude other people from entering it. Contrary, once\ndata has been published it cannot be protected from the usage by\nother people. Many firms fear that they encounter creative\ndestruction if they share their data as privileged access to data is\nregarded to be a source of competitive advantages \u0007(Ciuriak 2018a)\b.\nAdditionally, if data is not a by-product of transactions, its\ncreation is often costly \u0007(Duch-Brown, Martens, and Mueller-Langer\n2017)\b. However, \u0007Jones and Tonetti\b \u0007(2018)\b argue that not sharing\ndata leads to an inefficient use of it.<\/p>\n\n\n\n

\n\u0007Easley et al.\b \u0007(2018)\b discuss the two fundamental questions\nregarding data ownership: \u201cWho gains and who loses if consumer data\nis shared and what happens to the total surplus? Second, if there is\nprivate ownership of consumer data how does the initial ownership of\ndata affect sharing and thus consumer welfare, firm profit, and\nsurplus?\u201d \n<\/p>\n\n\n\n

\nThe foundation of legal ownership rights lay in the Arrow Information\nParadox \u0007(Arrow 1972)\b which shows that once data is seen by a buyer\nthe valuable information of that data may be fully exposed. However,\naccording to \u0007Duch-Brown, Martens, and Mueller-Langer\b \u0007(2017)\b firms\ncan, especially on platforms, commercialize the value of their data\nwithout revealing them completely and therefore open up the\nopportunity for data trade. Additionally, the non-rival nature of\ndata indicates that firms can bundle a large number of goods without\nsubstantially increasing costs and thereby reduce competition.\nRelevant examples of this phenomena are Netflix, Spotify and Apple\nMusic \u0007(Goldfarb and Tucker 2019)\b. Summarized due to its\ncharacteristic of being non-rival and having near zero cost of\nproduction and distribution, big data is imitable, which raises the\nquestion of whether or not ownership protection laws are needed.<\/p>\n\n\n\n

\nA monopolistic approach eliminates data sharing which could lead to\nlarge power inequalities and block access to downstream users. Hence,\nfirms would be able to charge high prices for users. In addition, it\nhas been shown that excessive protection of data cuts down innovation\nand increase an imbalance in knowledge. In contrast, data sharing\ninduces economies of scope and increases welfare \u0007(Duch-Brown,\nMartens, and Mueller-Langer 2017)\b. For that reason, economists\n\u0007(OECD 2015)\b argue that datasets should be a public good, much like\nWikipedia, in order to improve market outcomes. Providing data for\nfree signals the firm’s skills and quality to potential employees and\ncustomers. Moreover, offering the core product for free attracts new\ncustomers while allowing firms like Spotify to sell their add-ons at\na premium \u0007(Goldfarb and Tucker 2019)\b. \n<\/p>\n\n\n\n

Even though sharing data always increases total surplus, firms may be better off in an economy with undisclosed data. They may maximize their gains in markets where they are monopolists. However, they have to face losses in global markets when their private information\u2019s become public. The market structure determines who stands on the winning side. If a firm does not own enough local markets, data sharing could result in a decrease of its profits. \u0007(Easley et al. 2018)\b. Nevertheless, \u0007Easley et al.\b \u0007(2018)\b point out that excluding data to protect competitive advantages results in efficiency loss and might lead to a prisoner\u2019s dilemma. In their opinion mechanisms like data sales or governance are needed to ensure \u201csocially optimal sharing\u201d \u0007(Easley et al. 2018)\b. <\/p>\n\n\n\n

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Figure 4:<\/strong> Trade-Off: Data Sharing vs. Data Protection<\/em><\/figcaption><\/figure><\/div>\n\n\n\n

\nEasley et al.\b \u0007(2018)\b and \u0007Jones\nand Tonetti\b \u0007(2018)\b\nconclude by saying that data property rights matter. Depending\non whether firms, consumers, a social planner or the government own\ndata, a different degree of welfare is reached. \u0007Jones and Tonetti\b\n\u0007(2018)\b show through a numerical example of their framework that\nwelfare is maximized when data is owned by a social planner who\nshares data without thinking about creative destruction or privacy.\nIf firms own data, they extensively investigate it and refuse to\nshare a larger amount of their data with others which results in\nlower welfare (89.17%). The costs of forced sharing might be a\ndisincentive for firms to create data or to add noise to diminish the\nvalue for the public. To prohibit sharing is particularly harmful as\nit only leads to a welfare of 34.29% compared to the perfect\nallocation. The authors infer that the initial ownership of data\nshould be given to consumers as they adequately balance data sharing\nand privacy, leading to a near perfect allocation. Sharing data leads\nto a scale effect and increases the consumption and variety of\nconsumer goods. Seeing increasing returns of scale associated with\ndata and the non-rival characteristic of data may engage firms to\nmerge into a \u201csingle-economy-wide firm\u201d \u0007(Jones and Tonetti\n2018)\b to exploit the scale effect. \n<\/p>\n\n\n\n

\nSummarized, several authors have empirically determined that sharing\ndata leads to an increase in welfare for firms and consumers.\nHowever, their opinions differ regarding the perfect amount of\nsharing and how valuable data can be for firms.<\/p>\n\n\n\n

Trackability of Microdata:<\/h3>\n\n\n\n

\nThrough digital technologies, large amounts of individual\u2019s surfing\nand click behavior on the internet can be costlessly recorded and\nsaved in databases. Marketers often use a combination of different\nweb beacons like web tags or page tags on consumers\u2019 past internet\nactivities to derive their current needs and detect common trends\n\u0007(Tucker 2010)\b. As a consequence, firms are able to advertise\nsuitable products at customized prices to individuals. For instance,\nmicro-tracking empowers marketers to notice when a couple decides to\nbecome pregnant. Subsequently, they might decide to show them\nweb-banners with buggies or cribs \u0007(Acquisti 2014; Easley et al.\n2018)\b. Personalization and one-to-one markets are possible and lead\nto an increase in advertising effectiveness. However, micro-tracking\nalso results in an asymmetric distribution of information and might\ninfringe on consumers\u2019 privacy \u0007(Gandomi and\nHaider 2015)\b.<\/p>\n\n\n\n

\nTracking improves the ability to target specific markets or customers\nwith reduced advertising costs because the likelihood of only\naddressing receptive customers is increased \u0007(Acquisti 2014; Acquisti\nand College 2010)\b. \n<\/p>\n\n\n\n

\nLow tracking costs enable the differentiation of products through for\nexample price discrimination in a new way \u0007(Goldfarb and Tucker\n2019)\b. \u0007Shiller\b \u0007(2016)\b shows that personalized price\ndiscrimination using individual-level tracking technologies to track\nweb browsing behaviors raises profits by 14.55%. Some consumers pay\ntwice as much for a product than others. In contrast, when targeting\na consumer offline, companies have to rely on \u201cnoisy signals based\non media demographics\u201d\u0007(Goldfarb and Tucker 2019)\b , while the\nconsumer\u2019s digital footprint can be used to directly target that\nparticular person, allowing higher revenues for firms \u0007(e.g. Acquisti\nand College 2010)\b. In turn, higher revenues allow firms to invest in\nnew services and business models. Moreover, tailored advertising may\nalso be beneficial for customers. Targeting gives consumers useful\ninformation and insights on items they are interested in and reduces\ntheir cost of acquiring valuable information. \u0007(Goldfarb and Tucker\n2019)\b. \n<\/p>\n\n\n\n

\nAll in all, one can say that \u201cbig data is equivalent to an upward\nshift in the matching function between firms and customers\u201d\n\u0007(Roxana Mihet and Thomas Philippon 2018)\b. To maximize advertising\neffectiveness, firms need to conduct experiments on the influence of\nbehaviorally targeted advertisement and then develop advanced\nalgorithms and data processing tools to exploit microdata on website\nvisitors and therefore maximize advertising effectiveness.<\/p>\n\n\n\n

Verifiability of Data:<\/h3>\n\n\n\n

\nEven\nthough the reduction in tracking costs facilitates verification of\nfirms\u2019 reputation, consumers still prefer face-to-face transactions\nand are more risk-averse towards online purchases. The difficulty for\nfirms lays in establishing a system of trust in sole digital\ntransactions. \u0007Goldfarb\nand Tucker\b\n\u0007(2019)\b\nsummarize that several\npapers empirically prove that better rated and thereby more trusted\nsellers can demand higher prices. (\u0007Houser\nand Wooders 2006; Lucking-Reilley et al. 2007\b).\nConsumers trust trademarks and user reviews as a source of\ninformation about product quality. To achieve higher sales, firms can\nprovide information on product quality on for example Amazon to\ninform their customers through positive reviews that their product is\nthe best product available \u0007(Chevalier\nand Mayzlin 2006)\b.\nOverall, it has become easier \u201cto establish an online reputation\n(\u2026) but the mechanisms for damaging that reputation in form of\nconsumer complaints have also become easier\u201d \u0007(Goldfarb\nand Tucker 2019)\b\nas\nwell. Social media enables a rapid widespread of information not only\non customers but also on firms. Jeff Bezos, the founder of Amazon,\nsummarized the widespread effect of the internet on reputation\nperfectly: \u201cIf you make customers unhappy in the physical world,\nthey might each tell six friends. If you make customers unhappy on\nthe Internet, they can each tell 6,000 friends\u201d \u0007(Newman\n2015)\b.\nFirms can use the internet in favor of their reputation. However,\nthere is also a ubiquitous\ndanger of losing esteem within seconds. \n<\/p>\n\n\n\n

\nCostly Characteristics of Data<\/h2>\n\n\n\n

\nSeveral characteristics of data which make the creation of value from\ndata challenging. The existence of incomplete and noisy datasets,\ncollected at different points in time, challenges firms to exploit\nthe value of data.<\/p>\n\n\n\n

Understanding and dealing with costly characteristics of data is of the utmost importance for companies, as they otherwise might extract wrongful insights from their collected data. <\/p>\n\n\n\n

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Figure 5:<\/strong> Overview of the costly characteristics of Data<\/em><\/figcaption><\/figure><\/div>\n\n\n\n

Volume and Variety of Data:<\/h3>\n\n\n\n

\nAs previously discussed, the world is filled with a vast amount of\ndata which is continuously growing. Data-driven firms are eager to\ncollect all available data because they are unable to determine the\nvalue of the data beforehand \u0007(Janssen, van der Voort, and Wahyudi\n2017)\b. However, competitive advantage is not obtained by possessing\nthe highest amount of data but rather by having the organizational\ncapabilities and technologies to transform data into valuable\ninformation \u0007(Duch-Brown, Martens, and Mueller-Langer 2017)\b.<\/p>\n\n\n\n

\nCaptured data is subject to a high variety and therefore highly\nheterogenic, dividing data into structured and unstructured data.\n\u201c90% of generated data is unstructured\u201d \u0007(IBM 2019)\b including\ntweets, images, videos, and audios. Consequently, traditional\nprograms, for instance linear modeling approaches are futile \u0007(Cai\nand Zhu 2015; Varian 2014)\b. \u0007Varian\b \u0007(2014)\b describes several new\nbig data analyzing tools that account for the complex and flexible\nrelationships between datasets. He highlights the growing importance\nof machine learning techniques as they allow to effectively combine\nand analyze big data. Furthermore, he advises economists to enhance\ntheir knowledge of machine learning. \n<\/p>\n\n\n\n

\nData analysis creates a trade-off between the study of all available\ndata and focusing on a subset of data. The essential key to success\nis to figure out which data should be filtered out as erroneous and\nwhich subset of data a firm should focus on. Hence, firms need to\nestablish reliable tools to analyze and interpret data. \n<\/p>\n\n\n\n

Velocity of Data:<\/h3>\n\n\n\n

\nVelocity is the third \u201cV\u201d of most data definitions and refers to\nthe speed \u201cat which data is generated and at which it should be\nanalyzed and acted upon\u201c \u0007(e.g. Cai and Zhu 2015)\b. \u0007IBM\b \u0007(2019)\b\nestimates a rate of 50,000 gigabytes per second of global internet\ntraffic. Especially smartphones and sensors have \u201clead to an\nunprecedented rate of data creation\u201d \u0007(Gandomi and Haider 2015)\b.\nEvery 60 seconds, two hours of footage are uploaded to YouTube and\n216,000 Instagram pictures are posted \u0007(IBM 2019)\b. Uncountable\namounts of data are generated within seconds, leading to a large\nsource of information. But data is highly subjected to perishability.\nTherefore it needs to be analyzed in real-time \u0007(Gandomi and Haider\n2015)\b.<\/p>\n\n\n\n

\nTheoretically, a quick analysis of data enables immediate feedback.\nSearch engines like Google constantly optimize their search\nalgorithms and online product services by analyzing their user’s\nclickstream data. Online data can be easily used to improve product\nofferings, optimize user experiences on websites \u0007(Tucker 2010)\b and\nenables interactive relationships with individual customers. But only\nthrough the utilization of advanced big data technologies, firms are\nable to analyze a high volume of data timely and effectively to\ncreate \u201creal-time intelligence\u201d \u0007(Gandomi and Haider 2015)\b.\nOtherwise, data becomes outdated and useless, ultimately leading to\ndecision-making mistakes \u0007(Cai and Zhu 2015; Gandomi and Haider\n2015)\b.<\/p>\n\n\n\n

Veracity of Data:<\/h3>\n\n\n\n

\nVeracity is probably the most important and simultaneously most\nchallenging characteristic of data. It deals with the degree to which\nfirms can trust and rely on data and the outcome of the analysis of\nthat data. Data can be incomplete, out-of-date, fake and noisy \u0007(e.g.\nGandomi and Haider 2015)\b. According to \u0007IBM\b \u0007(2019)\b, $3.1 trillion\nper year is lost in the US economy due to poor data quality. Data\nitself is never valuable until it is translated into relevant\ninformation. Yet low-quality data will always result in low-quality\ninsights. To extract truthful, objective, and credible information,\nthe collected data needs to be truthful, objective, and credible as\nwell. Subjective data can be valuable to companies but it is crucial\nthat firms are aware of its subjectivity \u0007(Lukoianova and Rubin\n2014)\b.<\/p>\n\n\n\n

\n\u0007Lukoianova and Rubin\b \u0007(2014)\b came up with a big data veracity\nindex which measures the degree to which collected data is objective,\ntruthful and credible (OTC) and normalizes them at the (0,1) interval\nwith 1 referring to the maximum OTC. In contrast \u201cbig data of low\nquality is subjective, deceptive and implausible\u201d \u0007(Lukoianova and\nRubin 2014)\b, expressed by an index of 0.\nDeception is the intentional creation of false content with the aim\nof leading readers to make wrong conclusions. The tremendous amount\nof textual content on the internet has led to a rise in deceptive\ncontent which may lead to detrimentals results. The 2016 U.S\npresidential election shows how data can be manipulated and used to\npsychologically exploit people \u0007(Kauflin\n2018)\b.<\/p>\n\n\n\n

Undisclosed Data:<\/h3>\n\n\n\n

The decision to disclose or protect data leads to several trade-offs. Undisclosed data that is not shared with other parties but protected, can impose entry barriers and limitations for competition. Protected data can be seen as opportunity costs. Therefore, firms might choose to forgo potentially valuable data gathering in order to not conflict with privacy protection laws and keep a good reputation. Furthermore, firms lose money if they overinvest in data security and protection \u0007(Acquisti and College 2010)\b. According to \u0007Acquisti and College\b \u0007(2010)\b, data restrictions hinder innovation because firms are lacking customer data and consequently welfare is diminished. The quality of available information about economic operators in the market may be diminished by privacy protection laws. Consequently, the signals needed for the market to analyze and efficiently use data to decide on production and pricing could be denied \u0007(Acquisti 2014)\b. Consumers, on the other hand, are often more likely to buy products from firms that protect their customers\u2019 data. As a result, several trade-offs for firms and consumers regarding the decision of how much data should be shared, have risen.<\/p>\n\n\n\n

Interoperability of Data:<\/h3>\n\n\n\n

Portability\nrefers to the ability to move data between different parties.\nTherefore, competition and services become closer substitutes, which\nforces firms to offer better quality and\/or superior customer service\n\u0007(Duch-Brown, Martens, and Mueller-Langer 2017)\b.<\/p>\n\n\n\n

\nInteroperability of data constitutes one of the main reasons for\ndata-driven mergers. The incentive behind a merger is to stop\ncompetition from entering the market by preventing data portability\nand interoperability. This is especially relevant for firms in\nmulti-sided markets that use their superior access to data to\nstrengthen their market position. Platforms like Opodo or Momondo\ngain from a large amount of interoperable data. However, airlines\nlike Lufthansa are exposed to more transparency and competition in\ntheir industry, putting them under price pressure \u0007(Duch-Brown,\nMartens, and Mueller-Langer 2017)\b. Nevertheless, data sharing\nincreases the consumption and maximizes total surplus. \n<\/p>\n\n\n\n

\nSummary<\/strong><\/h2>\n\n\n\n

Data will impact the entire economy, and not only behavioral tracking and price discrimination. Therefore, managers\u2019 new obligations include giving data a higher share of their attention, as underestimating data could lead to the downfall of a company. <\/p>\n\n\n\n

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Figure 6:<\/strong> Managerial implications <\/em><\/figcaption><\/figure><\/div>\n\n\n\n

First, managers need to make their firms data-ready. To do so, they may establish a new organizational structure and develop new data analyzing technologies. Data scientists are an essential part of extracting the right data from a tremendous pool of sets. They should work together with marketers to tackle consumers\u2019 preference and predict trends. It is the obligation of managers to hire the right people and provide special training for example with a focus on machine learning to harness the power of data and improve decision making. Second, due to the decrease of veracity in data, a systematic approach to analyze large datasets has to be established. To ensure high quality decisions datasets have to be truthful, objective and credible. Third, managers should be aware that sharing data within and outside their firm does not harm, but benefits their firm in generating valuable insights from their collected data. Nevertheless, they should never betray their customers\u2019 trust. In the fast-growing data-driven economy, reputation can be damaged quickly and newcomers can easily steal customers from incumbents due to the interoperability of data. Fourth, capital should be invested in detailed research on the characteristics of data. Data is a valuable economic input and every decision builds upon the foundation of a good database. Therefore, investing in research on the characteristics and effects of data will result in increased returns, as superior performance in big data analytics will result in a competitive edge. However, it should not be forgotten that even if data is timely analyzed and correctly processed, a competitive advantage is derived from the interplay of data insights, innovative ideas, commercial strategies, service and especially a good customer relationship. <\/p>\n\n\n

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<\/span>References<\/div>
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