New technology is usually implemented with the best of intentions. An extra dashboard to gain more insight. A new data source for better analyses. An additional application to solve a specific problem. These are logical choices in themselves. However, over time, an environment often arises in which systems, data sources, and processes become increasingly interdependent.

The increasing complexity isn't always immediately visible. Yet, it has an influence on virtually everything: from resolving incidents to cybersecurity, from information provision to AI. The question is therefore not *whether* complexity arises, but how you deal with it.

Complexity is rarely found in a single system

When organisations talk about a complex IT environment, they often look at individual applications or technologies. In practice, the challenge usually lies elsewhere.

Complexity arises mainly in the connections between systems. Data is stored in different applications. Teams use their own dashboards. Processes span multiple platforms. Information is present but scattered.

This is often only noticeable when employees are looking for answers to seemingly simple questions:

• Where does this message come from?
• Which application is causing this delay?
• Is this a security incident or an innocent anomaly?
• Which information is the most current version?

At that moment, it appears that information is available, but not easily accessible or interpretable.

The hidden costs of scattered information

In many organisations, gathering information from different systems, applications, and data sources takes longer than desired. Not because the information is missing, but because it is scattered across various locations. This might seem like a minor problem, but the impact quickly escalates.

When employees regularly have to switch between systems, dashboards, documents, and data sources, not only is time lost. The chance of errors also increases and decision-making slows down. Consider incidents that take longer to investigate, duplicate reports because data is stored in multiple places, or employees who interpret the same information in different ways.

More tooling isn't always the solution

When complexity increases, the temptation to add new tooling is obvious. An extra dashboard. An additional monitoring tool. An AI solution. A new reporting environment.

Although such solutions can be valuable in themselves, they do not always solve the underlying problem. In fact, they sometimes add an extra layer of complexity. The challenge often lies not in a lack of data, but in a lack of coherence between existing systems and information sources.

At PuurData, we regularly come across organisations that have excellent tools for monitoring, security, analytics and search, yet still struggle to find answers quickly. Not because data is lacking. Not because the technology is inadequate. But because information is scattered across different systems, processes and teams.

Why observability is becoming increasingly important

Observability This clearly shows why cohesion is so important. In the event of a failure or performance issue, the necessary information is usually present. Logs, metrics and events are often already collected. Yet, it sometimes takes a lot of time to ascertain the cause of a problem. Not because data is missing, but because signals are spread across different systems.

According to the State of Observability 2025 Report from Splunk Organisations with a mature observability approach are better able to detect and resolve problems faster. Therefore, observability is not just about monitoring. It's about understanding connections.

A practical example: Municipality of ‘s-Hertogenbosch

A concrete example of this is the municipality of ’s-Hertogenbosch. The municipality wanted to gain more control over monitoring and logging within the IT environment. By bringing this information together in one central platform, better insight was gained into performance, availability, and incidents.

This made it easier to spot anomalies, perform analysis, and react more quickly when necessary. The challenge was not a lack of monitoring or logging, but in bringing together information from various sources. It was precisely this coherence that made it possible to gain faster insight into performance, availability, and incidents.

The impact on cybersecurity

Complexity also plays an increasing role in cybersecurity. A failed login attempt is usually not a problem. An unusual network connection by itself often isn't either. But when multiple signals come together, context is created. It is precisely that context that determines whether an event is innocent or part of an attack.

The annual IBM Cost of a Data Breach Report Demonstrate that the speed of detection and response remains an important factor in limiting damage from security incidents. To achieve this, coherence between systems and data sources is essential.

Why AI depends on context

The rise of AI makes the issue even more relevant. Many organisations are experimenting with AI solutions, copilots, and generative search functionality. The focus is often on models, prompts, and technology.

But the quality of AI is ultimately determined by the quality of the available context. When information is scattered across systems, documents, and data sources, it becomes difficult to generate consistent and reliable answers. AI is therefore not only a technology issue, but also a data issue.

The solution doesn't lie in less technology

It is not realistic to completely avoid complexity. Organisations grow. New applications emerge. Laws and regulations change. Technology is constantly developing.

The solution therefore lies not in less technology, but in the smarter connection of data, systems and processes. When information can be viewed cohesively, more overview is created. Incidents are resolved faster. Security becomes more effective. AI becomes more reliable. And perhaps more importantly: the environment remains manageable.

The greatest costs of complexity are often not technical or financial. They lie in lost time, missed insights, longer analyses, and an environment that becomes increasingly difficult to understand. That's why a modern data environment isn't just about collecting data, but primarily about creating coherence. Because only when systems, processes, and information are connected can true insight emerge.

Want to know more?

At PuurData, we help organisations get to grips with complex data environments. From observability and search to security, AI and data strategy.

👉 Ontdek hoe wij organisaties helpen more coherence, insight and control to create.