Prof. Yan Li, ESSEC Asia-Pacific, and her fellow researchers explore the influence of Big Data Analytics (BDA) in shaping strategic decision-making. Could it spell the end of pragmatic but imperfect human short cuts in arriving at a choice? It depends, say the researchers, pointing to the role that complex, dynamic and uncertain environments play.  

The Algorithm and the Compass: Big Data Analytics and Decision-Making by CoBS editor, Antonin Delobre. Related research: How Does Big Data Analytics Shape Human Heuristics Adaptation in Strategic Decision-Making? Jin Chen, Cheng-Suang Heng, Yan Li et Xi Chen, Journal of the Association for Information Systems, 2024.

The duel is age-old: on one side, cold, methodical and exhaustive ‘calculation’; on the other, the decision-maker’s dazzling intuition, that often indefinable ‘flair’. Since the advent of big data analytics (BDA), many have predicted the end of human heuristics—those cognitive shortcuts that allow us to navigate uncertainty—in favour of purely algorithmic rationality.

Yet the history of organisations is not one of wholesale replacement, but of a new alchemy. Between the surgical precision of the machine and the plasticity of the mind, a major study conducted by Prof. Yan Li and her colleagues explores how BDA, far from stifling our intuition, becomes the chisel that sculpts it to better face the unpredictable.

The Mirror of Literature: Heuristics vs. big data

To fully understand the issue, we need to go through what the literature refers to as ‘strategic decision-making’. This is characterised by a lack of structure and radical uncertainty, made even more difficult by the limited processing capacity of leaders. This is where heuristics come in: simple rules that are easy to remember and quick to adapt.

• Alternatives: The available decision options (e.g., which market to enter?).
• Cues: The key information considered when evaluating these options (e.g., labour costs).
• Relationships: The logical links, often linear in the human mind, that connect cues to alternatives (e.g., ‘if costs fall, profits rise’).

In contrast, Big Data Analytics promised to uncover the ‘truth’ in data and correct human biases. Some authors went so far as to suggest that BDA should replace judgement rather than complement it. However, this study highlights three critical limitations of the algorithm: its dependence on predefined objectives, its tendency to favour short-term success, and the opacity of its ‘black box’.

In reality, the value lies not in choosing between humans and machines, but in their interplay: how does the abundance of data qualitatively transform our mental shortcuts?

The 3 Modes of Sculpture: How BDA shapes intuition

Prof. Yan Li et al’s research reveals that BDA does not replace our heuristics, but rather causes them to mutate through three distinct modes of adaptation.

First, we have alternative reorientation where Big Data Analytics replaces invalid options or broadens the scope of possibilities. The example of the T-Video app is striking: the company initially targeted the music market by analogy with the video game market. However, data analysis revealed poor retention. At the same time, a segmentation analysis showed massive potential in video, an unsuspected ‘Blue Ocean’. Within a week, the data validated this new alternative, prompting executives to abandon their initial intuition in favour of a more robust option.

Secondly, BDA allows something called cue-patching. It permits us to revise the information we rely on, removing misleading indicators or adding new ‘signals’. By analysing correlations, S-Social discovered that ‘address book synchronisation’ was a much more powerful predictor of future retention than traditional industry indicators. They ‘patched’ their heuristics by incorporating this specific indicator, thereby improving their ability to anticipate.

Finally, there is relationship conditioning. In this instance, BDA modifies the understanding of the links between indices and alternatives. Where humans see linear and simple relationships, the machine reveals multidirectional and non-linear causalities. The company X-Payments has thus learned to adjust the ‘weights’ of its risk indices in real time, understanding that the interaction between geolocation and the type of bank card cannot be summarised by a simple arithmetic sum, but by a complex integration algorithm.

One of the most innovative discoveries of this research is what the authors call the pinning mechanism. For adaptation to be effective in chaos, the human mind needs reference points. Big Data Analytics provides this clarity by ‘pinning’ (holding fixed) one component of the heuristic (e.g., the alternatives) to serve as a basis for comparison while adjusting the others (the indices or relationships). Without this anchor point, the decision-maker would be faced with an explosion of information, unable to discern whether the change in outcome is due to new data or a change in logic.

The Compass of Uncertainty: Complexity vs. dynamism

The effectiveness of these modes of sculpting depends radically on the nature of the environment, a crucial distinction between complexity and dynamism.

• In high complexity: The environment is teeming with elements in non-linear interaction. Here, BDA acts as a microscope on relationships. The study shows that adaptation then takes place through a hybrid mode (C&R): indices are corrected and relationships are reconditioned simultaneously to break down causal entanglement.
• In high dynamism: Everything changes quickly and unpredictably. Alternatives that are valid at time T become obsolete at time T+1. BDA then serves as an expiry detector, prompting a reorientation of alternatives. E-Navigator thus realised that its discrete ‘Bookmark’ option was not generating enough data because users had become indifferent to small changes; the BDA invalidated this option and suggested a more daring one.

However, there is a threshold where the algorithm loses its way. When complexity and dynamism are both high, BDA can reach its limits. Data may be too scarce, or the correlations detected by the machine may be purely superficial and devoid of strategic meaning. This is where the study reintroduces the concept of business acumen.

For example, T-Video executives had to override their own algorithm, which recommended short videos because they were widely shared, but business acumen reminded them that the strategic objective was not sharing, but long-term retention, which was better served by longer formats. This is a form of phronesis—the practical wisdom dear to Aristotle—which allows the computing power of BDA to be realigned with the deeper logic of the business. In these extreme environments, humans no longer follow the machine; they intervene to ensure that the algorithm does not optimise an immediate objective (such as click-through rate) at the expense of overall, long-term satisfaction.

Implications and Limitations of Big Data Analytics: Towards symbiotic governance

Yan Li et al’s research does not merely theorise – it offers a survival guide for the modern leader with significant implications. The data expert must no longer be a mere provider of retrospective reports, but integrated from the outset of the decision-making process to help formulate heuristics.

Helping to redefine the role of the expert, Hub & Spokes structures (data centres serving departments) must evolve towards closer collaboration, with analysts actively participating in strategic thinking. This will allow an improved organisational architecture.

In short, BDA fundamentally changes the way we learn. It teaches us to break down our own thoughts and accept that our cognitive shortcuts are working hypotheses, always ready to be revised. The researchers’ study may have limitations as it focused on new product development, but it leads one to wonder whether other types of strategic decisions would respond in the same way.

The Civilisation of Constraint

Ultimately, Prof. Yan Li et al’s research teaches us that artificial intelligence is not the death of human intuition, but its armour. It allows us to navigate uncertainty with a more accurate compass, capable of distinguishing weak signals in the noise of complexity.

Modern leaders must learn to collaborate with their algorithms without becoming their slave. In the end, true strategic performance lies in this ability to marry episteme (knowledge), technè (technical invention) and phronesis (practical wisdom).

Useful links:

• Link up with Prof. Yan Li on LinkedIn
• Read a related article: How Big Data Will Impact the Role of the Management Controller
• Discover ESSEC Business School Asia-Pacific in Singapore
• Apply for the ESSEC Asia-Pacific Master in Data Sciences & Business Analytics (DSBA) – ESSEC & CentraleSupélec.

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