With the rapid development of the internet, mobile phones, sensors, along with the increase of storage capacity and computer technologies, the concept of big data made its appearance.

The notion of big data was coined in the 90’s and relates to large sets of data. Both big data and data refer to the same concepts, namely a quantity of samples, numbers, criteria, words, transactions, internet clicks or tweets, which can be categorized into 5 key sources [1]: Public Data, Private Data, Data Exhaust, Community Data, and Self-Quantification Data. However, the line between these two notions is unclear and often misunderstood. Dependent on the authors, the definition may differ. Usually, big data are defined by the 3V concepts: Variety, Volume, and Velocity. The words big data mainly come from the high volume of data which implies a different way of storage. Big data cannot be stored in a classical hardware disk or a local server where the capacity is too small; this means that dedicated structures of storage have to be employed to respond to their continuously increasing growth. Appropriate solutions to access, analyze, categorize or process these large amounts of data must therefor be designed to insure better accessibility to the databases, as for example the MapReduce model developed by Google to archive, process and index the web. Two other concepts may also be mentioned: Variability for defining the change of behavior (dependent on consumers, forecast, … ) and Veracity, how accurate and/or trustful these data are?


Big data are everywhere, and are generally created unintentionally by everyone of us, for instance by using an application on our smartphone, accessing a website, informing our age or location on Facebook. They are also present, for example, in research domains such as astronomy, genetics (in the human genome) [2]. This wide range of applications entails different unit measures. The use of these considerable volumes of data may have strong consequences on our daily life and our behavior [3]. Because of this diversity of the data, research related to big data and their influences are currently gaining momentum in academic and professional spheres. Not only are big data under scrutiny by academia, the general public has also demonstrated interest and concern on the subject. In the worse scenarios, big data are seen as a tool to manipulate people like Big Brother controlling our life and our thoughts, predicting our behaviors and our desires. And in the best case, they may be involved for greater innovations, as for example the fight against illegal deforestation, illegal fishing, climate change or predicting epidemic outbreaks [4]. More generally, big data are used as tools by companies to generate greater profits.

This article does not attempt to draw up an inventory of all literature related to big data in general. Instead, the focus is on how big data innovation changes managerial decision within a large company.


We cannot talk about data and management without mentioning the notion of data-driven decisions (DDD). As it is explained on techpedia.com website, this term means «making decisions that are backed up by hard data rather than making decisions that are intuitive or based on observation alone».

A store can be a good example to illustrate. A storekeeper does not have many points of reference concerning a customer’s behavior. He/she may know which article is a bestseller and create customer loyalty with a personal account. However, concerning consumer behavior, it is difficult to track the eye navigations on the different products, or predict the customer’s movements inside the shop. With the development of internet, online shops are now able, in addition to creating customer loyalty, to follow the cursor, and therefor visualize which articles are successful or the points of interest of the customer. It is then possible for online retailer to propose individual promotions and predict the next purchase from the last ones.

Based on high volumes of data and complex models, it is now possible to make the best possible strategic decisions instead of intuitive decisions. In other words, assisted by mathematics tools, such as data processing and machine learning, Data Science is able to improve automated decision-making which will help the firm to make the best profits at the right moment [5]. Studies have shown that, based on DDD making, companies may increase their productivity by 5% while rising the profits by 6%.


In the last decade, the relation between data and companies has changed and altered the traditional economic concept into digital. It has led to the creation of new job profiles, namely data analyst, data scientist and data engineer. Together, these three professions’ purpose is to solve business problems from a data perspective and to help the manager making the right decision. They are able to handle the volume, the variety and the velocity of the data and to answer efficiently to the market by generating value. Independently, the data analyst may be seen as the junior version of the data scientist. His/her objective is to process and to analyze unstructured data, using existing methodology such as machine learning algorithm, statistics and data mining. The data analyst also has to manage and insure the good quality of data sets. The role of the data scientist is similar to the data analyst, with one difference: in addition, the data scientist is able to develop new tools adapted to several sets of data. He/she interprets the data and delivers predictions based on trends and previous results. These two functions are supported by the data engineer. The data engineer may be seen as a software engineer. He/she is responsible for generating new data bases, using SQL-based software or Hadoop-based technologies. His/her work renders the data analysis easier for the other two data professionals.


Big data revolution changes the way important decisions are taken and therefore impact a manager’s tasks. Besides the required classical leadership and market knowledge, the manager is now exposed to new challenges. Paying attention to data flows, he/she must now be able to take new decisions based on these data flows and not on his/her instincts or/and previous experiences. Working with new expertise areas such as big data, the manager must understand the language of the data scientist with whom he/she closely works. Both have the objective to transform digital information into business processes to create new values.

Julien Schorsch


[1] George, G., Haas, M. R., & Pentland, A. (2014). Big data and management. Academy of management Journal, 57(2), 321-326.

[2] Data, data everywhere. The Economist, (http://www.economist.com/node/15557443), Feb 2010.

[3] John Walker, S. (2014). Big data: A revolution that will transform how we live, work, and think.

[4] Chandy, R., Hassan, M., & Mukherji, P. (2017). Big data for good: insights from emerging markets. Journal of Product Innovation Management, 34(5), 703-713.

[5] McAfee, A., Brynjolfsson, E., & Davenport, T. H. (2012). Big data: the management revolution. Harvard business review, 90(10), 60-68.