Automation without accurate data: A common mistake in companies

Why automation is often misapplied

In recent years, process automation has become one of the top priorities in the Portuguese business world. The pressure to reduce manual tasks, speed up operations, and free up teams for higher-value tasks is very real.

This is important, but it also reveals a risk: that many companies are investing in automation before they have resolved a more fundamental problem. When the database is unstructured - where information is inconsistent, duplicated, or circulates uncontrollably between systems - automation does not eliminate existing problems; it silently exacerbates them by spreading errors throughout the system before anyone realizes what is happening.

The most common mistake: automating isolated tasks

In most situations, the decision to automate stems from the need to reduce manual work for teams. Someone spends hours exporting data from one system and entering it into another; processes are slow because they rely on repetitive steps that no one has yet questioned, so the “obvious” solution is to automate these tasks.

What often happens is that this automation is implemented at the task level, where each solution visually solves the problem for the person who requested it, but does not address the underlying cause. An automated routine is created that copies contacts between platforms, synchronizes records between systems, and eliminates the need for manual entry in a specific step.

The problem is that automating a task is different from structuring a process, because a task is a single step, while a process is a sequence of steps, with its dependencies, data sources, and operating rules.

When you automate a task without understanding the process it is part of, what you get is a faster solution to something that perhaps shouldn’t exist in that form—and that is exactly how so-called “operational shortcuts” arise: automations that solve problems without addressing the root causes, and that often make those causes harder to identify and correct later on.

The role of data in process automation

Process automation is the automatic execution of defined logic based on available information, and for it to function reliably, that information must meet three basic conditions: it must be accurate, it must have a clear and defined source, and it must be consistent across the systems that use it.

When these conditions are not met, automation loses the control it was supposed to have; decisions are made based on incomplete data, and processes proceed without any real validation of their status. Errors occur the moment the information is processed, but they are only detected much later, when their effects have already spread.

There is a fundamental difference between manual work and automated work that is rarely considered before implementing automation: in manual work, there is always some form of implicit verification; that is, whoever performs a task sees what they are doing and has the ability to detect inconsistencies, even if only informally.

Automation executes without question, because if the information is wrong, the process proceeds anyway, and that is precisely why data quality ceases to be a secondary issue when automating and becomes a prerequisite for operation.

When automation increases the problem

There are situations in which automation not only fails to solve a problem, but transforms it into a different kind of problem that is harder to address—such as the automatic propagation of data between systems.

For example, a company stores its customer data in a CRM and a billing system, with manual synchronization between the two. This manual process is slow and prone to errors, so the decision is made to automate it. As a result, a routine is established that automatically replicates data from one system to the other whenever an update occurs. The result seems immediate: less work and greater speed. However, if the source data is inconsistent—for example, if a contact is duplicated, an address is incomplete, or fields have different formats—automation does not resolve this inconsistency; it spreads it. This causes the error that was previously confined to one system to now exist at multiple points in the operation, making it significantly more complex to track down.

Another equally common situation is the automatic updating of statuses in workflows. If the logic determining when a process should move forward is not well-defined, or if the data feeding that logic is unreliable, a process may complete steps it should not have completed, without anyone receiving any alert.

When the problem was manual, there was always a moment when someone noticed it, but with automation, this problem travels through the system before being detected, and by the time it is detected, it may have already produced effects that are difficult to undo.

Why automation seems to work, but doesn’t solve the problem

One of the most misleading aspects of poorly implemented automation is the perception of improvement it creates in the early stages:

• some tasks are no longer performed manually;
• the execution time of certain processes decreases;
• operations appear to run more smoothly.

These improvements are real, but they are superficial, and they tend to mask problems that are simply piling up at another level.

Many companies move toward automation without having the internal capacity to assess whether the basic conditions are in place for that automation to produce the expected results. This often results in an operation that is more dependent on automated rules that no one fully understands, where errors are harder to trace because they are no longer visible at the point where they occur, and where technical maintenance consumes time and resources that were not anticipated. Thus, the complexity has not disappeared, and automation has come to function as a layer that hides problems rather than solving them.

What needs to be solved before automation

The starting point is a clear definition of the data source, because until this definition exists, any automation will operate on data whose accuracy cannot be guaranteed. Each type of information—customer data, process statuses, financial records, operational information—must have a defined source system, where it is created and maintained as the single source of truth.

Added to this is the need to clean and consolidate existing records by eliminating duplicate information across systems. When the same data exists in two places with potentially different values, any automated process that depends on that information will be subject to update conflicts, where each system attempts to impose its own version, and the result is a loss of consistency precisely where automation was supposed to ensure it.

It is necessary to ensure that the flow of information between systems is defined in a controlled manner through system interoperability. This ensures that each piece of data has a defined path, that systems receive it from the correct source, and that updates propagate in a predictable and traceable manner; without the interoperability, automation lacks a solid foundation on which to operate.

Automation as a consequence, not as a starting point

Process automation is a natural consequence of a well-structured operation, not a substitute for a structure that does not yet exist. When processes function consistently, when data has a clear origin and flows in a controlled manner between systems, automation makes perfect sense: it reduces tasks without introducing inconsistencies, accelerates processes without losing control, and improves efficiency without increasing operational complexity.

For most Portuguese SMEs, which operate with limited resources and where every investment decision carries significant weight, it is essential to understand that automation without consistent data does not eliminate manual work; rather, it transforms it into something more difficult to control for those managing the operation and more difficult to correct.

The priority, before automating, is to ensure that what is to be automated is already functioning correctly, and only then can the technology produce the expected impact.