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Role of Data and Intuition in Decision Making Processes

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The growing volume of data, new methods of analysing it, and usability discussions require the right integration into decision making processes to be set up. The purpose of this short communication paper is to highlight the role of data and intuition in decision making processes and highlight its current trends. The main questions are as follows: Is data becoming the brain of companies? Are we approaching a state where data takes leadership, so that human intuition will no longer be needed? The conclusions reached by this paper are stressing the necessity to study data-driven and data-informed decisions, their impacts within particular subject areas (based on decision making processes) and focused on education systems to integrate data and business analytics.
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JOURNAL OF SYSTEMS INTEGRATION 2019/3 31
Role of Data and Intuition in Decision Making Processes
Martin Potančok
Faculty of Informatics and Statistics, University of Economics, Prague, Czech Republic
martin.potancok@vse.cz
DOI: 10.20470/jsi.v10i3.377
Abstract: The growing volume of data, new methods of analysing it, and usability discussions require
the right integration into decision making processes to be set up. The purpose of this short
communication paper is to highlight the role of data and intuition in decision making processes and
highlight its current trends. The main questions are as follows: Is data becoming the brain of
companies? Are we approaching a state where data takes leadership, so that human intuition will no
longer be needed? The conclusions reached by this paper are stressing the necessity to study data-
driven and data-informed decisions, their impacts within particular subject areas (based on decision
making processes) and focused on education systems to integrate data and business analytics.
Key words: data, data-driven, data-informed, decision, intuition
1. Introduction
As the volume of data produced increases, its impact within companies increases dramatically.
According to statistics 2.5 quintillion bytes of data is created every day (Marr, 2018) and the number is
growing rapidly due to the Internet of Things (IoT), smart technologies and 4.0 trends. Data is
quantities, characters or symbols; when context or description is added, information is generated;
applying experience leads to knowledge (Ackoff, 1989).
This creates new possibilities for data processing, evaluation and use during decision making within
society and different industries. Below, the examples from different industries can be found.
Banking “New data, technologies, and IT developments significantly alter production processes and
distribution channels in the economy.” (Buch, 2018) Agriculture “Agriculture is undergoing a
tremendous transformation in the collection and use of data to inform smarter farming decisions.
Precision agriculture has brought a heightened degree of competition for input supply firms, forcing
greater interactions among friends and foes.” (Pham & Stack, 2018) Healthcare “Experimental results
are presented which illustrate the ability of the systems to detect and predict Parkinson’s based on
medical imaging information.” (Kollias, Tagaris, Stafylopatis, Kollias, & Tagaris, 2018) Education ”The
daily activities of schools and universities—from taking attendance to assessing students—can leave
a trail of data that, under the right conditions, can be used to explore teaching and learning like never
before.” (Krumm, Means, & Bienkowski, 2018) Public services “Engagement in dashboards, with
citizens having the opportunity to provide data and discuss results, plays a crucial role in achieving the
benefits.” (Matheus, Janssen, & Maheshwari, 2018)
Talking about decisions, people can make decisions based purely on intuition, or data (data-driven
decisions), or a mixture of factors, such as intuition and data (data-informed decisions). There are
changes in the functioning of entire companies and a new relationship with the customer, as in the
case of a digital transformation. For digital transformation, data is a necessary prerequisite. But it
needs to be said that data is not the only condition. Organizations must incorporate digital
environment into their culture, processes, products etc. As emphasized by Dremel, “Companies need
to fundamentally transform their decision making processes…In particular, the adoption of evidence-
based decision making, not only in sales but in most organizational functions, is improving
organizational performance.” (Dremel, Wulf, Herterich, Waizmann, & Brenner, 2017)
The purpose of this short communication paper is to highlight the role of data and intuition in decision
making processes and highlight its current trends. The main questions are as follows: Is data
becoming the brain of companies? Are we approaching a state where data takes leadership, so that
human intuition will no longer be needed? As stated above, the context of this paper is the growing
ROLE OF DATA AND INTUITION IN DECISION MAKING PROCESSES
32 JOURNAL OF SYSTEMS INTEGRATION 2019/3
amount of data, but also new possibilities of data and business analytics and their proper integration
into companies from decision point of view. The structure of this paper corresponds to the above.
2. Data-driven and data-informed decisions
Decisions help solve specific problems. They include a range of activities, input data (including their
analysis), factors influencing decisions and other intangible elements. It is also important to mention
the weight of the elements in decisions, too much does not mean a better result (Saaty, 2008). The
example of the analytic network decision process (Saaty, 2008) taking into account benefits (B),
opportunities (O), costs (C) and risks (R) is illustrated in Fig. 1.
Figure 1: Steps followed in the analytic network decision process (Saaty, 2008)
The above-mentioned model and lots of other decision models do not take into account the amount of
data and intuition. To do this, it is necessary to extend traditional decision model by data, specifically
data-driven or data-informed views.
For data-driven decisions, data is a fundamental (and in many cases the only) factor affecting decision
making processes. In many cases, data-driven decision makers integrate data into their corporate
cultures and share data across the organization. (Barlaskar, 2018)
They even require their employees to back up all decisions with data to take advantage of the results
in many cases of automated analysis and also automated decisions (without human inputs). (Mosier &
Skitka, 2018) The use of such systems can be seen in smart buildings to reduce electricity
consumption or in banking to decide on the granting of a loan. Such decisions are appropriate under
certain conditions and specific situations. A frequently mentioned advantage by companies is the
traceability and verifiability of decisions, which, however, are based only on data and thus depend on
its quality and analysis.
Conversely, data-informed decisions can be simply described as the use of data during decision
making processes. Here, data is only one of the factors on the basis of which management makes
decisions. (Maycotte, 2015)
ROLE OF DATA AND INTUITION IN DECISION MAKING PROCESSES
JOURNAL OF SYSTEMS INTEGRATION 2019/3 33
In such cases, however, data constraints are taken into account and additional resources are used to
provide greater added value. Moreover, intuition and personal responsibilities are drawn into the
decision. “Being data-informed is about striking a balance in which your expertise and understanding
of information plays as great a role in your decisions as the information itself. It’s like flying an
airplane. No matter how sophisticated the systems onboard are, a highly trained pilot is ultimately
responsible for making decisions at critical junctures.” (Maycotte, 2015) Companies can benefit from
results combining data and knowledge of their employees.
4. Discussion
Appropriate setting of data analytics and above all its correct use yields results to companies. For
example, it is possible to analyse customer behaviour and potential, predict project risks, perform
predictive maintenance, set up supply chains appropriately, or optimize the operation of production
lines. But it is important to realize the role that intuition plays in this.
Figure 2: Intuition, data-informed and data-driven decisions, based on (Cortney, 2018)
Optimally, intuition is conditioned by experience in the fields of data science, advanced analytics and
artificial intelligence with a business orientation, as illustrated in Fig 2. At the same time, it is advisable
to support the setting of corporate culture and an experienced analytical team.
5. Conclusion
On the one hand, data is becoming an important (sometimes even essential) part of the decision
making process; on the other hand, intuition is still necessary and it is not possible to say we are
approaching a state where data takes leadership, so human intuition is not needed anymore.
For further development of the findings of this short communication paper, it is necessary to study
data-driven and data-informed decisions, their impacts within particular subject areas (based on
decision making processes) and focused on education systems to integrate data and business
analytics. That is why the Faculty of Informatics and Statistics of the University of Economics, Prague
prepares graduates for work in a dynamic environment and shows the current possibilities of using
data analytics in practice within the set of activities called Data & Business. Specifically, it is the Data
Festival, a semester course of the Data Science & Business Intelligence Academy of the University of
Economics, Prague and, above all, a new MBA study program called Data & Analytics for Business
Management (first MBA of this kind in central Europe).
6. Acknowledgement
This work was supported by the Institutional Support for Long-Term and Conceptual Development of
Research and Science at the Faculty of Informatics and Statistics, University of Economics, Prague.
ROLE OF DATA AND INTUITION IN DECISION MAKING PROCESSES
34 JOURNAL OF SYSTEMS INTEGRATION 2019/3
7. References
Ackoff, R. L. (1989). From data to wisdom. Journal of Applied Systems Analysis, 16(1), 3–9.
Barlaskar, U. (2018). Why you should be data-informed and not data-driven - By Uzma Barlaskar.
Retrieved October 3, 2019, from https://hackernoon.com/why-you-should-be-data-informed-and-
not-data-driven-76079d187989
Buch, C. (2018). Claudia Buch: Can technology and innovation help? New data generating
possibilities. Retrieved from https://www.bis.org/review/r180723c.htm
Cortney, C. (2018). Data Informed Design - Good Tech Test - May 2018. Retrieved October 3, 2019,
from https://www.slideshare.net/CourtneyClark7/data-informed-design-good-tech-test-may-2018
Dremel, C., Wulf, J., Herterich, M. M., Waizmann, J.-C., & Brenner, W. (2017). How AUDI AG
Established Big Data Analytics in Its Digital Transformation. MIS Quarterly Executive, 16(2).
Kollias, D., Tagaris, A., Stafylopatis, A., Kollias, S., & Tagaris, G. (2018). Deep neural architectures for
prediction in healthcare. Complex & Intelligent Systems, 4(2), 119–131.
https://doi.org/10.1007/s40747-017-0064-6
Krumm, A., Means, B., & Bienkowski, M. (2018). Learning analytics goes to school: A collaborative
approach to improving education. Routledge.
Marr, B. (2018). How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should
Read. Retrieved October 2, 2019, from
https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-
the-mind-blowing-stats-everyone-should-read/#2aa86a2b60ba
Matheus, R., Janssen, M., & Maheshwari, D. (2018). Data science empowering the public: Data-driven
dashboards for transparent and accountable decision making in smart cities. Government
Information Quarterly. https://doi.org/10.1016/J.GIQ.2018.01.006
Maycotte, H. O. (2015). Be Data-Informed, Not Data-Driven, For Now. Retrieved October 2, 2019,
from https://www.forbes.com/sites/homaycotte/2015/01/13/data-informed-not-data-driven-for-
now/#6bd02bd7f5b7
Mosier, K. L., & Skitka, L. J. (2018). Human decision makers and automated decision aids: Made for
each other? In Automation and human performance (pp. 201–220). Routledge.
Pham, X., & Stack, M. (2018). How data analytics is transforming agriculture. Business Horizons,
61(1), 125–133. https://doi.org/10.1016/J.BUSHOR.2017.09.011
Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of
Services Sciences, 1(1), 83–98.
JEL Classification: D89
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Cortney, C. (2018). Data Informed Design -Good Tech Test -May 2018. Retrieved October 3, 2019, from https://www.slideshare.net/CourtneyClark7/data-informed-design-good-tech-test-may-2018