Organisations have never had so much data to drive their business. Dashboards, KPIs, analytics tools, Business Intelligence platforms, and artificial intelligence solutions are constantly producing new indicators intended to inform decisions.
Yet, a paradoxical situation arises in many organisations: the more data there is, the more complicated decision-making seems to become. So, why do decisions sometimes become harder to make when there is more data? Why doesn't data abundance guarantee better decisions? How can we regain a management approach where data informs decision-making without replacing it?
In this article, discover why the abundance of data does not guarantee better decisions, what the limitations of data are, and how to regain the capacity for arbitration in an increasingly information-rich context.
Why are businesses investing so heavily in data?
In many organisations, data has progressively become a way to secure decisions. A decision supported by figures appears more objective, more legitimate, and easier to defend than a choice of conviction.
Yet, this impression of neutrality is misleading. Any data depends on a method of calculation, an analytical scope, assumptions, and an interpretation. This search for security gradually modifies the role of data. In some organisations, data is no longer used solely to understand a situation or to inform a decision. It can also be mobilised to justify a decision already made, to postpone a choice, to avoid disagreement, or to share responsibility for a decision. The debate then no longer concerns the decision itself but the indicators that accompany it.
This evolution explains the increasing importance placed on measurement. Dashboards are multiplying, new KPIs are emerging, and each project produces its own indicators. However, measuring more does not necessarily mean deciding better. When an indicator does not confirm the expected results, it is sometimes simpler to create a new one than to question the pursued objectives or the choices made. Measurement then ends up taking precedence over action.
The data can also become a means of delaying a decision. Requesting further analysis, waiting for a new set of results, or seeking additional statistical evidence are sometimes relevant steps. They can also postpone a judgment that will, in any case, have to be faced. No organisation has all the information before acting.
Data reduces some uncertainty but never eliminates the need to make a decision. Every decision involves an element of judgment, responsibility, and risk. It is this capacity for arbitration that distinguishes a data-driven organisation from one that merely accumulates data.
What are the limitations of data?
Data constitutes a valuable decision-making tool. However, it cannot address all situations. Certain limitations become apparent when it is assigned a role it is unable to fulfil.
The first is due to its very nature: data describes what has happened, sometimes what is happening, but it never allows us to know the future with certainty. This limitation becomes particularly apparent when an organisation launches a new service, explores an unknown market, or faces disruption. In innovative contexts, historical data loses some of its value, and decisions rely as much on strategic vision as on available indicators.
All important dimensions also cannot be translated into figures. The quality of a user experience, the consistency of a digital journey, the trust placed in a brand, or the relevance of content are partly based on a qualitative assessment. Metrics provide useful benchmarks, but they do not replace the observation of usage, user feedback, or human analysis. Yet, it is these more difficult-to-measure dimensions that are often the most discussed within organisations.
Another limitation appears when indicators directly influence behaviours. As soon as a KPI becomes a target to be achieved, teams may be tempted to optimise the figure rather than the desired outcome. This phenomenon is also known as Goodhart's Law: when a measure becomes a target, it ceases to be a good measure. The data ceases to be a tool for observation and becomes a constraint that modifies the reality it is meant to measure.
How to regain arbitration capacity?
Regaining an arbitration capability isn't about producing less data, but about putting it back in its rightful place. Indicators should inform a decision, not replace it. When an organisation multiplies dashboards or systematically waits for further analysis before acting, the risk is that measurement becomes an obstacle to action.
This evolution begins with a clarification of priorities. Following a few truly strategic indicators is often more effective than accumulating dozens of KPIs. Defining moments when a decision must be made, even in the presence of uncertainties, also helps to prevent the search for additional information from becoming a permanent delaying tactic.
Finally, clear governance remains essential. Data can be shared, discussed, and interpreted collectively. The responsibility for the decision, however, must always be assumed by a clearly identified person or body.
Here's how to tell if an organisation has become too reliant on data: * **When gut feelings are ignored:** If decisions are made purely on data without considering experience, intuition, or qualitative insights, it can be a sign of over-reliance. * **When data paralysis sets in:** An organisation might get stuck in a loop of collecting more data, analysing endlessly, and delaying decisions because the data isn't "perfect" or clearly dictates a single path. * **When data is seen as the only truth:** If data is treated as infallible and unchallenged, even when it might be flawed, incomplete, or misinterpreted, it's a red flag. * **When the "why" is lost:** Focusing solely on what the data says without understanding the underlying reasons or the broader context can lead to poor strategic choices. * **When experimentation is stifled:** If the fear of not having enough data or the need for absolute certainty prevents innovation and trying new things, it's a problem. * **When the wrong metrics are tracked:** An organisation might become obsessed with vanity metrics or indicators that don't truly reflect success or impact, simply because they are easy to measure. * **When data silos are not broken down:** If data is siloed within departments and not shared or integrated effectively, it might lead to incomplete pictures and conflicting insights. * **When the cost of data outweighs the benefit:** If significant resources (time, money, personnel) are poured into data initiatives without a clear return on investment or a tangible improvement in outcomes. * **When human interaction and relationships suffer:** If data-driven customer service or internal communications become impersonal and neglect the human element, it can signal an imbalance. * **When questions are framed by available data, not by business needs:** Instead of asking "what do we need to know to solve this problem?", the question becomes "what does the data we have tell us?".
Several weak signals serve to identify an organisation where measurement is beginning to replace decision-making rather than inform it. Taken in isolation, they are not enough to characterise a data dependency. Their accumulation, however, constitutes a telling indicator of an imbalance in management.
The first one appears when the arbitrations are systematically postponed on the grounds that supplementary data is missing. When this pattern repeats without new information actually altering the decision, it often reflects a difficulty in making choices in an uncertain context.
Delegating decision-making responsibility to tools This is also a worrying sign within organisations. When decisions are systematically attributed to an algorithm, a predictive model, or a scoring system, it often reflects a shirking of responsibility that allows people to avoid facing choices and consequences.
The continual addition of KPIs, dashboards and tracking tools is also a sign of the company's commitment to its customers.’confusion between monitoring and steering. The more indicators are multiplied, the more the hierarchy of priorities becomes illegible. This proliferation often masks a lack of clear vision of what really counts.
Continuous experimentation with minor variants of an interface or a flow is a legitimate practice, but when any design decision, even a fundamental one, is subject to experimental validation, it reveals an inability to accept a bias editorial or strategic.
A mature organisation regularly questions the validity of its measurement conventions and challenges established metrics. When an indicator becomes sacred, when it structures management rituals without ever being questioned as to its relevance or its induced effects, it ceases to be a tool and becomes a dogma.
The initial question of whether data and intuition should be opposed poses a false dilemma. Data and intuition do not oppose each other; they complement each other in a coherent decision-making process. Data illuminates, experience interprets, and decision-making resolves. The problem lies not in the use of measurement but in its transformation into an organisational refuge that allows for the avoidance of commitment. Decision-making maturity is not measured by the volume of data mobilised, but by the ability to make decisions in a context of imperfect information and to assume the consequences of one's choices.