On what resources do management strategists need to focus, if they want to leverage Artificial Intelligence? To
provide an answer from business academia, a framework for leveraging Artificial Intelligence in a business context
is presented in this 5-part article series. In Part 2, the relevance of data, or even Big Data, is elaborated. The
first part of this series can be found here.
Data to Internalize Complexity
For the application of AI, the first and very basic raw material is data. ML especially, is by definition
(inductive) learning from data by machines, thus inevitably depends on data. But also, AI in general needs data to
be of use. For example, expert systems need data in form of formalized facts and rules in a knowledge database, in
order to use an inference engine to deduct new conclusions. Even in reinforcement learning, an artificial agent
needs data or information in the form of feedback, to learn when certain actions should be rewarded or punished.
Note that this data need not necessarily come from the real-world. In simple environments like chess or Go, the
rules of the game can be easily formalized. Hence, the AI is able to learn from simulations of games, for example
against another instance of itself. However, for most real-world problems, the environment in which the AI acts and
the “rules of the game” are very complex, in fact too complex to formalize. Consequently, you need real-world data
to catch this complexity, in the hope that machines can detect and generalize the inherent rules and structures.
Big Data = Volume + Velocity + Variety
In business, data is indisputably one reason why AI and ML are successful and popular. “Data is the new oil.” is a
famous phrase (first coined by Clive Humby in 2006), widely used to emphasize the immense value of information
extracted out of data. Especially the concept of Big Data triggered a literal hype in the business world and related
research fields. But what is Big Data actually? Several definitions exist, often simply listing vague
characteristics of the data. Here, the consensual definition, proposed by Mauro et al. (2015, p. 103) is used:
“Big Data represents the Information assets characterized by such a High Volume, Velocity
and Variety to require specific Technology and Analytical Methods for its transformation into Value”.
The three “V’s”, namely Volume, Velocity and Variety, are the most common characteristics stated in definitions of
Big Data. Volume reflects the challenging large size of the datasets for storing and processing. Velocity represents
the high speed at which the data is generated, collected and analyzed. Finally, Variety refers to the unstructured
nature of Big Data. Besides these three “V’s”, the stated definition also specifies the concept of Big Data by
emphasizing the significant technological and analytical requirements, and by highlighting the economic impact
through generating insights (Mauro et al., 2015, p. 103).
How to Create Value from (Big) Data
This transformation from Big Data to Value is exactly the purpose of BDA and can be conceptualized in six steps with
the “DIKW hierarchy” (Lamba & Dubey, 2015, p. 5) and the “Information Value Chain” (Abbasi et al., 2016, p. 3ff.):
(1) Raw Data in form of symbols and figures needs to be collected, often from different sources. (2) This data must
be aggregated and organized in a useful structure to form Information. (3) The gained information should be analyzed
and contextualized to extract Knowledge about the past. (4) This knowledge needs to be extrapolated to the future,
incorporating the goals and strategy of the company. (5) Based on this Wisdom, decisions need to be made and actual
actions must be taken. (6) The impact of these actions should be measured, to generate data again, so that the whole
process can start over. The presented conceptual transformation can be applied for several purposes in practice. It
can support human decision-making by revealing hidden problems, patterns or variability, automate processes for more
efficiency, or discover new needs and create innovations like new business models, products and services (Manyika et
al., 2011, p. 97ff.). Two socio-technical features of Big Data also influence this value creation: “portability”
represents the possibility of reusing existing data in another context (Günther et al., 2017, p. 200ff.). On the
other side, “interconnectivity” refers to the possibility of creating synergy effects by aggregating multiple,
heterogeneous data sources, to extract more valuable insights from their combination (ibid., p. 200ff.). Another
chance to create more value is simply to take more data. Junqué de Fortuny et al. (2013, p. 223) showed that even
for already large datasets, increasing the sample size to a massive scale can make predictive modeling substantially
more accurate. Note that bigger can be better – but need not be, which will be discussed later in Subsection 2.3.1.
Data as a Source of Competitive Advantage
Consequently, access to Big Data, and the ability to handle it can potentially result in CA (ibid., p. 223). This
fact challenges the theory of RBV, since data itself is not rare and thus does not meet the VRIN criteria (Braganza
et al., 2017, p. 335). Nevertheless, it has been shown that BDA capability has significant positive effects on
market and operational performance (Gupta & George, 2016, p. 1059). Also, “the more systematic the analysis,
utilization and management of Big Data, the more extensive the utilization of Big Data in the organization”, and
“the higher the top management understanding of the importance of Big Data use, the higher the contribution of Big
Data to organizational competitive advantage” (Kamioka & Tapanainen, 2014).
Ransbotham and Kiron (2017) claim that due to this potential for CA, ownership of valuable data is altering power
relationships within industries and even within companies, which is why data governance will be an increasingly
important organizational capability. They especially encourage data sharing or trading between organizations, even
between competitors, since it still yields win-win-situations. However, trading can be especially difficult since
data as information asset is naturally “nonrivalrous” and only “partially exclusive”, which means it can be used by
multiple parties simultaneously and intellectual property rights may be defined but only incompletely enforced
(Pantelis & Aija, 2013, p. 39). Finding a fair price is already hard, because as “experience goods”, it is hard to
justify the value of the data to a potential buyer without revealing the intrinsic information, but once it is
revealed, the value is significantly reduced because of nonrivalry and partial exclusivity (ibid., p. 40).
Additionally, data is often expensive (financially and time-wise) to collect, but nearly cheap to copy or
disseminate (ibid., p. 41). All in all, these are the reasons why data privacy, security and ownership remain major
challenges for data governance and management in general (Sivarajah et al., 2017, p. 274f.). But despite the
intimidating challenges with Big Data, Brynjolfsson&McAfee (2017) point out that for realizing significant
performance improvements with analytics, you may not need all that much data to start with. Also Ross et al. (2013,
p. 98) suggest, to rather use “little” data more effectively throughout the organization, instead of solely focusing
on Big Data.
In the third part of this 5-part series, the relevance of IT infrastructure is elaborated.
- Abbasi, A., Sarker, S., & Chiang, R. H. L. (2016): Big Data Research in Information Systems:
Toward an Inclusive Research Agenda. Journal of the Association for Information Systems, 17(2),
- Braganza, A., Brooks, L., Nepelski, D., Ali, M., & Moro, R. (2017): Resource management in big
data initiatives: Processes and dynamic capabilities. Journal of Business Research, 70, 328–337.
- Brynjolfsson, E., & McAfee, A. (2017): The Business of Artificial Intelligence. Retrieved from
Harvard Business Review website:
hbr.org/cover-story/2017/07/the-business-of-artificial-intelligence on 07/06/2018.
- Günther, W. A., Rezazade Mehrizi, M. H., Huysman, M., & Feldberg, F. (2017): Debating big data:
A literature review on realizing value from big data. The Journal of Strategic Information
Systems, 26(3), 191–209.
- Gupta, M., & George, J. F. (2016): Toward the development of a big data analytics capability.
Information & Management, 53(8), 1049–1064.
- Junqué de Fortuny, E., Martens, D., & Provost, F. (2013): Predictive Modeling With Big Data: Is
Bigger Really Better? Big Data, 1(4), 215–226.
- Kamioka, T., & Tapanainen, T. (2014): Organizational Use of Big Data and Competitive Advantage –
Exploration of Antecedents. In PACIS (Ed.), Proceeding of the 19th Pacific Asia Conference on
Information Systems (PACIS 2014) (Vol. 372).
- Lamba, H. S., & Dubey, S. K. (2015): Analysis of Requirements for Big Data Adoption to Maximize
IT Business Value. In B. Shukla (Ed.), 2015 4th International Conference on Reliability, Infocom
Technologies and Optimization (ICRITO) - Trends and Future Directions. 2 - 4 Sept. 2015, Amity
University Uttar Pradesh, Noida, India (pp. 1–6). Piscataway, NJ: IEEE.
- Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011):
Big data: The next frontier for innovation, competition, and productivity. Retrieved from
- Mauro, A. de, Greco, M., & Grimaldi, M. (2015): What is Big Data? A Consensual Definition and a
Review of Key Research Topics. AIP Conference Proceedings, 1644(1), 97–104.
- Pantelis, K., & Aija, L. (2013): Understanding the value of (big) data. In X. Hu (Ed.), IEEE
International Conference on Big Data, 2013. 6-9 October 2013, Santa Clara, CA, USA (pp. 38–42).
Piscataway, NJ: IEEE.
- Ransbotham, S., & Kiron, D. (2017): Analytics as a Source of Business Innovation. MIT Sloan
Management Review. (February 2017).
- Ross, J. W., Beath, C. M., & Quaadgrad, A. (2013): You May Not Need Big Data After All. Harvard
Business Review. (December 2013), 90–98.
- Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017): Critical analysis of Big Data
challenges and analytical methods. Journal of Business Research, 70, 263–286.
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