Dismantling the Data Silo: How Data Silos Are Costing Your Company, and How to Fix Them
In today’s data-driven world, companies rely heavily on external data sources to gain insights, improve decision-making, and drive competitive advantages. Yet, despite the growing use of external datasets, many companies are struggling to realize the full potential of these resources. One of the biggest barriers is the rise of data silos, where critical datasets—and the knowledge of how to use them—are tightly controlled by specific departments or teams.
At Nomad Data, we’ve spoken with over a thousand companies about their data practices. One of the most common challenges we hear about is the formation of data fiefdoms, where teams not only control access to external datasets but also limit knowledge sharing about how to use those datasets effectively. These fiefdoms often arise because the individuals or teams responsible for acquiring and managing the data believe that only they have the expertise to handle it correctly. Unfortunately, this behavior stifles collaboration and innovation, and it prevents organizations from unlocking the full value of their data investments.
To break free from the limitations of data silos, companies need a cultural and operational shift, supported by tools like Nomad Data’s Data Relationship Manager (DRM). In this article, we’ll explore the hidden costs of data silos, why they persist when external datasets are involved, and what companies can do to foster a more open and collaborative approach to data usage.
What Are Data Silos?
A data silo is a repository of data that is isolated from the rest of the organization, often controlled by a single department or team. When external datasets are involved, these silos can become even more pronounced. Teams that procure external data often feel they are the only ones equipped to use it correctly, leading to restricted access and a lack of knowledge sharing, which further exacerbates the problem.
Consider this scenario: A retail company procures a valuable external dataset containing customer demographic and purchasing trends. This dataset is managed by the marketing team, which uses it to design targeted campaigns. However, the team believes that only they understand how to interpret the nuances of the dataset and, as a result, limits access to it. Even though the product development and sales teams could greatly benefit from this data, they are kept out of the loop because the marketing team insists that they alone know how to correctly leverage the insights. Other teams are unlikely to know much about the value of the dataset because there’s no place to see the learnings about and uses of that data.
This type of scenario plays out frequently across industries, where external data is siloed and only one team controls both access and the expertise on how to use it.
Why Do Data Silos Exist?
Data silos form for a variety of reasons, but when external datasets are involved, certain dynamics often come into play that reinforce their existence. Here are some common reasons why silos form around external data:
Expertise Gatekeeping
When an external dataset is procured, the team responsible for acquiring and managing the data typically spends time learning how to use it effectively. This learning process is often informal and undocumented, leading to a situation where only a small group truly understands the intricacies of the data. These individuals then become gatekeepers, believing that only they can interpret the data correctly and fearing that if others get involved, it could lead to misinterpretation or misuse. Without a system for tracking all these learnings over time it becomes increasingly challenging for other groups to use the data without climbing up the same learning curve.
Example: A hedge fund purchases a complex alternative dataset that tracks satellite imagery of store parking lots to estimate retail foot traffic. The quantitative research team tasked with managing this dataset spends months developing models to extract insights. They believe that only they understand how to properly interpret the data and fear that if other teams, like operations or risk management, gain access, they might misinterpret the data and make faulty decisions. As a result, the dataset—and the insights it could provide—remain siloed within the quant team.
Fear of Misuse or Misinterpretation
Teams that have invested time and effort in learning how to use a particular dataset often worry that if others gain access, they might misuse the data or interpret it incorrectly. This fear leads to restrictive practices that prevent other departments from accessing valuable insights.
Example: In a pharmaceutical company, the data science team procures an external dataset containing clinical trial data from a third-party provider. After months of deep analysis and interpretation, they feel that other teams, such as marketing or business development, might not fully understand the limitations of the data, leading to incorrect conclusions. To avoid this, they limit access, believing they are protecting the company from making poor decisions based on misinterpretation. In doing so, they inadvertently create a bottleneck where only one team can leverage the data.
Incentives for Control
In some organizations, teams are incentivized to maintain control over specific resources, including external data. These teams may feel that by hoarding access to valuable datasets, they increase their importance within the company. This behavior reinforces siloed thinking and prevents cross-departmental collaboration.
Example: A consumer goods company acquires a market intelligence dataset from an external vendor. The insights team, which manages the dataset, feels that their value to the organization is tied to their exclusive access to this data. By restricting access to the dataset, they believe they can maintain their role as a key decision-maker in the company. However, this prevents other teams, such as product development and sales, from using the data to inform their strategies, limiting the overall impact of the dataset.
The Negative Impacts of Data Silos
Data silos, especially those involving external datasets, can create significant challenges for organizations. Here are the major consequences:
Operational Inefficiencies
When only one team controls access to an external dataset, other departments may either be forced to make decisions without it or waste time trying to acquire similar data on their own. This not only leads to inefficiencies but also delays decision-making and reduces the organization’s agility. At Nomad Data we commonly see this result in companies buying a nearly identical dataset from other vendors and going through the entire compliance, onboarding and learning process, all of which could have been avoided.
Example: A large e-commerce company purchases an external dataset on consumer sentiment from social media. The data is managed by the customer insights team, which uses it to inform marketing strategies. However, the product development team, which is working on a new feature to address customer feedback, is unaware of the rich insights available in the dataset. The may not even be aware their company has purchased the data at all. They end up conducting their own, redundant research, wasting time and resources that could have been saved if the dataset had been shared.
Missed Opportunities for Collaboration and Innovation
External datasets often have value across multiple departments, but when they are siloed, other teams miss out on opportunities to leverage the data for their own purposes. This stifles collaboration and innovation.
Example: A logistics company purchases an external dataset that provides real-time traffic and weather data to optimize delivery routes. The operations team, which manages the dataset, uses it to plan delivery schedules. However, the business development team, which is exploring partnerships with regional carriers, could use the same dataset to identify bottlenecks in delivery zones and negotiate better terms. Because the dataset is siloed within operations, the opportunity for strategic collaboration is missed.
Limited Return on Data Investments
Companies invest significant resources in acquiring external datasets, but when access and knowledge about how to use the data are restricted to a single team, the return on that investment is limited. The dataset’s full value isn’t realized if it only benefits one part of the organization.
Example: A financial institution spends a considerable budget on an external dataset that provides credit risk scores for potential borrowers. The risk management team controls the dataset and uses it to enhance their models. However, the marketing team, which could use this data to better target campaigns for loan products, is unaware of the dataset’s existence. As a result, the company fails to maximize the value of its investment, as the dataset isn’t being used to its full potential across the organization.
Changing the Measurement of Data Professional Value
One of the root causes of data silos around external datasets is the way companies measure the value of their data professionals. In many cases, data professionals are rewarded for their control over specific datasets and for their expertise in using them, rather than for their ability to facilitate collaboration and share knowledge across the organization. Some of this results from poor management and the lack of hard KPI goals assigned to the team, making their value more esoteric and challenging to measure.
To dismantle these silos, companies must shift their focus.
Data professionals should be valued not just for their technical expertise but for their ability to share knowledge and enable other teams to use the data effectively. This means recognizing and rewarding collaboration, open data practices, and cross-departmental support. It’s also imperative that concrete goals that are aligned to company goals are set and a framework put in place to effectively measure them.
How Nomad Data’s Data Relationship Manager (DRM) Can Help
Breaking down data silos requires not only cultural change but also the right tools. Nomad Data’s Data Relationship Manager (DRM) is specifically designed to address the challenges of managing external datasets across multiple teams.
The DRM provides a centralized platform where all stakeholders can see interactions with data vendors, track data usage, and, most importantly, record and share learnings about how to effectively use external datasets. This helps eliminate the knowledge bottlenecks that form around data fiefdoms, ensuring that everyone in the organization has access to both the data and the knowledge needed to leverage it. The DRM is also updated to always be aware of new use cases for the datasets being purchased, allowing for increase ROI on data spend over time.
Transparency Across Teams
With the DRM, all departments can see what data is being used, how it’s being used, and by whom. This transparency ensures that datasets aren’t hoarded by one team and that others can benefit from the insights. It also allows new teams to shortcut the learning curve to using the data.
Centralized Knowledge Sharing
The DRM allows teams to document best practices and insights related to using external datasets. This ensures that the learning process is recorded and accessible, so no single team has exclusive expertise on how to use the data.
Improved Collaboration
By providing a clear view of external data interactions, the DRM fosters collaboration across teams, allowing everyone to contribute to data-driven decision-making.
Building a Data-Driven Culture Without Silos
To dismantle data silos, especially those involving external datasets, companies need to take proactive steps:
Promote Knowledge Sharing
Encourage teams to document and share their learnings about how to use external datasets, ensuring that knowledge doesn’t stay confined to one group.
Value Collaboration Over Control
Change the way data professionals are incentivized, placing more emphasis on their ability to facilitate collaboration and share insights across departments.
Invest in Technology
Use tools like Nomad Data’s DRM to centralize external data management and knowledge sharing, ensuring that no team becomes a gatekeeper of valuable information.
Conclusion: The Future of Data Management is Silo-Free
Data silos, especially those involving external datasets, are holding companies back. They create inefficiencies, lead to missed opportunities, and limit the return on data investments. To truly unlock the potential of external data, companies must break down these silos by promoting knowledge sharing, rewarding collaboration, and investing in the right tools.
Nomad Data’s Data Relationship Manager (DRM) can help companies dismantle these data fiefdoms by centralizing data interactions and ensuring that everyone has access to both the data and the knowledge needed to use it effectively. By embracing a silo-free approach to data management, companies can drive innovation, improve decision-making, and fully realize the value of their external datasets.
