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Why Data Silos Kill Collaboration

Chris Cooney
Head of Developer Advocacy
Coralogix

A silo is, by definition, an isolated component of an organization that doesn't interact with those around it in any meaningful way. This is the antithesis of collaboration, but its effects are even more insidious than the shutting down of effective conversation. To paraphrase Wittgenstein, in the modern economy, "The limits of data are the limits of my world."

Removing these limits is an essential step in maximizing the value of your data. In a world where 60% of organizations report over half of their data is considered "dark data," this is a huge challenge.

Why Do Silos Form?

There are a number of situations that drive the creation of a data silo, but the most common are:

■ Departments acting in isolation, hoarding data in pursuit of their own local optimizations.

■ Mergers and acquisitions, poorly stitching together two organizations with separate tools and systems.

■ Inter-departmental politics, driven by a pathological culture that doesn't favor collaboration (more on this later).

These are just a few of the many scenarios that drive the growth of data silos, but why are data silos so bad, and what do they have to do with collaboration?

The Traditional Arguments Against Data Silos

When writing about data silos and their associated impact, we almost always discuss things like server costs, wasteful licenses, a lack of economy of scale and more. These are very real, serious problems that are directly linked with the growth of silos, but the cost to collaboration is far more insidious.

The Hidden Impact to Collaboration

Collaboration requires a few things to really flourish:

■ Free movement of information between teams.

■ A culture of psychological safety, that won't punish people for surfacing their mistakes.

■ An environment free of the often political impulse to prioritize personal objectives over organizational outcomes.

Without all 3 of these components, honest collaboration is going to struggle. Silos directly attack point 1, free movement of information, and indirectly encourage the sorts of suboptimal behaviors that prevent the realization of 3, an environment free of political impulse. How does this happen?

The Impact to the Free Movement of Information

Silos are the obvious antithesis of the free movement of information. This is often driven by a technological barrier. For example, a large volume of valuable information is stored in an unparseable format, or is held in a legacy database without an easy querying mechanism, but it's also a collaboration barrier.

Teams develop habits. If they grow accustomed to their own data, in their own infrastructure, with all of the flexibility and freedom that entails, the idea of sharing, or indeed the idea of using another data format that is managed by another team, will require a lot more effort for initially small gains. As this vicious circle repeats, teams become more tribal, more entrenched in their own processes and techniques.

The Growth of a Culture of Confrontation

As teams become more tribal, trust disappears. In larger organizations, this manifests itself in "othering", where teams begin to treat colleagues as enemies, with uncertain ambitions. They begin to view the organization as a battleground. Every visitor from outside their team is treated as potentially hostile. This culture, identified by Westrum as Pathological, is self-fulfilling and, without strong and enlightened leadership, will continue to feed itself to catastrophic effect.

All of this, by hiding data and not encouraging cross-team pollination. It's that serious.

How to Break Down the Walls

Attacking this problem takes time, persistence and effort, but it is undoubtedly worth it.

Cross-Departmental Dialogues

Initiate open discussions among teams to share data needs and challenges, fostering trust and understanding. This step is essential to identify existing data silos and understand the barriers to collaboration.

API Standardization

Develop a standardized API framework to enable seamless data integration and interoperability across different systems. This allows for efficient data sharing and reduces fragmentation.

Data Governance Policies

Implement clear data governance policies that promote data sharing while ensuring security and compliance. Define data ownership, access rights, and quality standards to maintain consistency and trust in the shared data.

Foster Collaborative Culture

Cultivate a culture that values collaboration over competition. Recognize and reward efforts to break down silos and encourage data sharing. Leadership should exemplify collaborative behavior and emphasize the importance of working together to achieve common goals.

By implementing these strategies, organizations can dismantle data silos, enhance collaboration, and fully leverage the value of their data.

Let Your Data Roam Free

Free, accessible data can be correlated, compared, explored and refined. Teams can make data driven decisions, even if the data is halfway across the company. These internal API calls turn into collaboration sessions that form teams and steering groups and shared ambitions and goals which are the bedrock of a learning organization and, undoubtedly, some very long lasting friendships.

The elimination of silos is not just a cost optimization exercise. It is a cultural imperative, to ensure that you're not falling victim to an accidental Inverse Conway Maneuver and building a culture, and software, that will stand the test of time.

Chris Cooney is Head of Developer Advocacy at Coralogix

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Why Data Silos Kill Collaboration

Chris Cooney
Head of Developer Advocacy
Coralogix

A silo is, by definition, an isolated component of an organization that doesn't interact with those around it in any meaningful way. This is the antithesis of collaboration, but its effects are even more insidious than the shutting down of effective conversation. To paraphrase Wittgenstein, in the modern economy, "The limits of data are the limits of my world."

Removing these limits is an essential step in maximizing the value of your data. In a world where 60% of organizations report over half of their data is considered "dark data," this is a huge challenge.

Why Do Silos Form?

There are a number of situations that drive the creation of a data silo, but the most common are:

■ Departments acting in isolation, hoarding data in pursuit of their own local optimizations.

■ Mergers and acquisitions, poorly stitching together two organizations with separate tools and systems.

■ Inter-departmental politics, driven by a pathological culture that doesn't favor collaboration (more on this later).

These are just a few of the many scenarios that drive the growth of data silos, but why are data silos so bad, and what do they have to do with collaboration?

The Traditional Arguments Against Data Silos

When writing about data silos and their associated impact, we almost always discuss things like server costs, wasteful licenses, a lack of economy of scale and more. These are very real, serious problems that are directly linked with the growth of silos, but the cost to collaboration is far more insidious.

The Hidden Impact to Collaboration

Collaboration requires a few things to really flourish:

■ Free movement of information between teams.

■ A culture of psychological safety, that won't punish people for surfacing their mistakes.

■ An environment free of the often political impulse to prioritize personal objectives over organizational outcomes.

Without all 3 of these components, honest collaboration is going to struggle. Silos directly attack point 1, free movement of information, and indirectly encourage the sorts of suboptimal behaviors that prevent the realization of 3, an environment free of political impulse. How does this happen?

The Impact to the Free Movement of Information

Silos are the obvious antithesis of the free movement of information. This is often driven by a technological barrier. For example, a large volume of valuable information is stored in an unparseable format, or is held in a legacy database without an easy querying mechanism, but it's also a collaboration barrier.

Teams develop habits. If they grow accustomed to their own data, in their own infrastructure, with all of the flexibility and freedom that entails, the idea of sharing, or indeed the idea of using another data format that is managed by another team, will require a lot more effort for initially small gains. As this vicious circle repeats, teams become more tribal, more entrenched in their own processes and techniques.

The Growth of a Culture of Confrontation

As teams become more tribal, trust disappears. In larger organizations, this manifests itself in "othering", where teams begin to treat colleagues as enemies, with uncertain ambitions. They begin to view the organization as a battleground. Every visitor from outside their team is treated as potentially hostile. This culture, identified by Westrum as Pathological, is self-fulfilling and, without strong and enlightened leadership, will continue to feed itself to catastrophic effect.

All of this, by hiding data and not encouraging cross-team pollination. It's that serious.

How to Break Down the Walls

Attacking this problem takes time, persistence and effort, but it is undoubtedly worth it.

Cross-Departmental Dialogues

Initiate open discussions among teams to share data needs and challenges, fostering trust and understanding. This step is essential to identify existing data silos and understand the barriers to collaboration.

API Standardization

Develop a standardized API framework to enable seamless data integration and interoperability across different systems. This allows for efficient data sharing and reduces fragmentation.

Data Governance Policies

Implement clear data governance policies that promote data sharing while ensuring security and compliance. Define data ownership, access rights, and quality standards to maintain consistency and trust in the shared data.

Foster Collaborative Culture

Cultivate a culture that values collaboration over competition. Recognize and reward efforts to break down silos and encourage data sharing. Leadership should exemplify collaborative behavior and emphasize the importance of working together to achieve common goals.

By implementing these strategies, organizations can dismantle data silos, enhance collaboration, and fully leverage the value of their data.

Let Your Data Roam Free

Free, accessible data can be correlated, compared, explored and refined. Teams can make data driven decisions, even if the data is halfway across the company. These internal API calls turn into collaboration sessions that form teams and steering groups and shared ambitions and goals which are the bedrock of a learning organization and, undoubtedly, some very long lasting friendships.

The elimination of silos is not just a cost optimization exercise. It is a cultural imperative, to ensure that you're not falling victim to an accidental Inverse Conway Maneuver and building a culture, and software, that will stand the test of time.

Chris Cooney is Head of Developer Advocacy at Coralogix

Hot Topics

The Latest

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...

We just surveyed 300 frontend and mobile engineers across 16 countries, and the finding that keeps sticking with me isn't the one about AI. It's this: 74% of engineering teams rate themselves in the "middle" of the observability maturity scale. Not reactive, not strategic. Stuck in the middle. They have dashboards, they have tracing, they have alerts. And yet when something goes wrong, they still can't tell you why ...

In MEAN TIME TO INSIGHT Episode 25, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses  AI's impact on the Wide Area Network (WAN) ... 

Application performance monitoring (APM) dashboards are only as useful as what they are configured to measure. The default setup covers obvious failure modes such as downtime, error spikes, and latency breaches, but it does not cover everything. Some failures produce no alerts or anomalies. The dashboard stays green while users experience a broken product. Here are six signs that is happening ...

The race to deploy AI is largely over. Most enterprises have entered it. The question now is not whether artificial intelligence is running inside the organization. The question is whether anyone is genuinely responsible for what it does. That is not a technical question. It is a leadership one. And most organizations are not yet structured to answer it honestly ...

A new analysis of 250 real-world queries across common retail tasks, such as product pricing, availability, ratings, shipping and specifications, reveals systemic inefficiency at the heart of web-based AI agents. On average, 97.9% of the data retrieved by agents from live web pages is irrelevant to the query being answered. Specifically, the average page ingested ran nearly 9,000 characters, while the average answer was just 32 characters, resulting in a noise-to-signal ratio of 278:1. Price queries were the most extreme outlier, with noise rates approaching 99.5%. That's not a rounding error. That's a structural problem ...

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...