With 2024 coming to a close, it’s a fitting time to reflect on one of the most significant and persistent data challenges in government and business operations: the data silo. Over the years, companies like Savan have made strides in supporting data integration and data-driven decision-making. Yet significant work remains to identify and dismantle the silos that continue to hinder the effective use of information.
Data silos occur when data are stored and managed in isolated systems, often within specific departments or teams, making it difficult for others in the organization to access or use it. This fragmentation creates roadblocks to data sharing, integration, and collaboration, leading to inefficiencies and missed opportunities to unlock the full value of business data and make more informed data-driven decisions. In its 2024 report, the Defense Innovation Board highlighted that deeply entrenched data silos are a critical barrier to building a modern data economy within the Department of Defense (DoD), underscoring the importance of breaking down these silos to improve operational effectiveness and innovation.
Data silos often emerge due to the following:
- Organizational Structure: Many organizations, especially large government departments, are structured with components that each manage their own data. These components often have myriad programs with mission imperatives and funding that further distribute data stores across the organization. This results in isolated data that is only accessible to the program team or agency that generates it.
- Technology and Infrastructure Choices: Different teams may use varying tools, databases, and software solutions, making it challenging to connect disparate systems.
- Legacy Systems: Older systems not designed for modern data sharing often lack interoperability with newer technologies, making extracting or integrating data difficult. In our experience, older legacy systems may not have been designed with data sharing in mind and lack strong standards around naming conventions and metadata, further hampering interoperability.
- Cultural Barriers: Data silos are sometimes perpetuated by organizational culture, where teams are reluctant to share data for reasons such as maintaining control or avoiding additional workloads. See
Stephen Holden
's piece titled Bridging the Divide: Traditional Information Management and the Data Revolution for more on this topic.
The consequences of data silos can be far-reaching:
- Limited Decision-Making: in July 2023, the Government Accountability Office (GAO) stated, “Federal decision makers need evidence—such as data and the results of studies—to determine if programs are working as intended and to identify potential improvements.” When data is locked away in silos, decision-makers are left with an incomplete view of the organization’s performance or the larger context in which they operate.
- Inefficiencies: Teams may unknowingly duplicate efforts because they cannot access or are unaware of similar data elsewhere in the organization. How many times over are agencies buying and storing the same asset due to an incomplete inventory of all data?
- Inconsistent Data: Data managed in isolation often leads to inconsistencies, where different departments maintain conflicting records or definitions of the same entity (e.g., customer or service data). The data inconsistencies undermine data trust. Many agencies spend an inordinate amount of time answering a question from leadership when different offices arrive at different answers because there is no single point of truth.
- Difficulty in Innovation: With data dispersed across silos, it becomes harder to harness advanced technologies such as AI, machine learning, or even big data analytics, all of which thrive on large, diverse datasets.
- Reduced Data Quality. Data silos degrade data quality by creating inconsistencies, duplicates, and fragmented information across departments, leading to incomplete and unreliable data. They further limit visibility, hinder data governance, and allow outdated or incorrect information to persist, reducing the overall integrity and usefulness of the data. Poor data quality literally prevents the government from making accurate, evidence-based decisions.?
- Heightened Risk: As an IT decision maker, consider being alerted to a privacy breach in a data repository you were unaware of. Imagine your legal department presenting a discovery request for data that should have been legally disposed of years ago but wasn't due to the governance team's lack of awareness about the repository's existence. Silos of data created silos of risk.
To unlock the full potential of data and break down silos, organizations are turning to approaches such as Data Warehouses, Data Lakes, Data Cloud platforms, Data Mesh, Data Fabric, and Data Virtualization. These frameworks and technologies promote collaboration, data sharing, and real-time access across departments, enabling government agencies and enterprises to integrate their data in ways that were previously impossible.
Here’s how each approach plays a role in overcoming data silos:
- Data Warehouse: At its core, a data warehouse is a centralized system for storing structured data, making it perfect for tasks like business intelligence (BI) and operational reporting. Its standardized structure ensures that data are consistent and easy to analyze across the organization, making generating reports and getting insights simple. However, the structured nature of data warehouses can make them less flexible, especially when dealing with unstructured data (e.g., images or sensor data), and their high setup costs can be a hurdle, particularly in environments that need to adapt quickly to new data. Data Warehouse technology has existed since the late 1980s, first introduced by Bill Inmon and later expanded upon by Ralph Kimball. Newer solutions, like Data Mesh and Data Fabric, have emerged to address some of these limitations, offering more flexibility for modern, fast-changing data environments. Despite these newer approaches, data warehouses are still widely used, especially in federal agencies, where many are considered legacy systems. For instance, the IRS has relied on data warehouses for years to manage taxpayer data, helping them streamline reporting and ensure compliance for tax enforcement purposes.
- Data Lake: A Data Lake is a centralized storage solution that can handle raw, unstructured, and structured data, giving organizations the flexibility they need for advanced analytics, machine learning, and big data projects. It’s especially effective in cloud environments, where it can scale easily to accommodate growing data needs at a relatively low cost. However, without proper governance, data lakes can turn into "data swamps," where the lack of organization makes it hard to find and use valuable information. While data lakes excel in handling large, complex datasets for analytics, they aren’t as well-suited for traditional BI and reporting, which require structured, well-organized data. For example, the U.S. Census Bureau has invested in an enterprise data lake to modernize how it stores and processes economic and demographic data. This allows the Bureau to handle increasing data volumes more efficiently and generate insights for public policy and planning. The flexibility of a data lake makes it easier for the Census Bureau to keep up with growing data demands while improving its ability to analyze and extract meaningful information.
- Data Cloud Platforms: Data Cloud Platforms offer a scalable and cost-efficient way to handle large datasets, thanks to their cloud-based infrastructure and flexible pay-as-you-go models. They allow organizations to store, analyze, and manage data seamlessly while also offering advanced tools for AI, machine learning, and analytics, making them ideal for a wide range of use cases. One of their biggest advantages is elasticity, meaning you can scale up or down based on your needs without upfront investment in physical infrastructure. However, there are some challenges to keep in mind. Ensuring data security and staying compliant with regulations like HIPAA or FISMA can be tricky in the cloud, and there’s always the risk of latency when transferring large amounts of data. There’s also the concern of becoming too dependent on one specific cloud provider (vendor lock-in), which can make it costly to switch down the road. Despite these challenges, Data Cloud platforms are becoming more common across the federal government. Agencies such as DoD, the National Aeronautics and Space Administration (NASA), and the Department of Health and Human Services (HHS) have already shifted a significant amount of their data to the cloud to take advantage of its flexibility and advanced capabilities, particularly for managing the complex and distributed data environments typical of large government operations.
- Data Mesh. Coined by Zhamak Dehghani, Data Mesh offers a decentralized approach for organizations that need more flexibility and scalability in managing their data. Instead of a central team handling everything, Data Mesh allows individual teams or departments to manage their own data while still ensuring everything works together through federated governance. This setup reduces bottlenecks and allows teams to work more independently and quickly. However, decentralizing data management can create some challenges. Maintaining consistency and governance across different domains isn't easy, and without proper oversight, teams may end up duplicating data or creating redundant systems. Additionally, moving to a Data Mesh model requires a cultural shift, where data are treated as a product with ownership and accountability. While the concept is gaining traction, Data Mesh is still relatively new in the federal government compared to traditional setups like data warehouses or data lakes. Adoption is growing, but it’s not as widespread yet. The need for strong federated governance may limit its adoption in the federal environment, but Data Mesh could work well in decentralized organizations. ?
- Data Fabric: Unlike Data Mesh, Data Fabric delivers a unified, metadata-driven architecture that integrates disparate data sources without the need for physical data movement. Introduced in the 1990s, it enables real-time access to data across on-premises and cloud environments, making it ideal for organizations managing hybrid or legacy systems. While highly flexible, Data Fabric's reliance on accurate metadata and complex implementation processes can pose challenges, and it's not optimized for storing large volumes of raw data. Furthermore, the requirement for strong governance and organizational alignment can hinder a successful Data Fabric implementation. Federal agencies, such as the DoD, are particularly invested in Data Fabric solutions, which are used to enhance data accessibility, integration, and security across hybrid environments, including cloud, on-premises, and edge systems. The DoD's use of Data Fabric is critical in supporting initiatives like Joint All-Domain Command and Control (JADC2). It is predicted that Data Fabric will become the go-to data architecture for distributed environments that need seamless access to data across multiple sources; however, its high complexity will likely limit its use to those organizations that can truly take advantage of the flexibility it offers.
- Data Virtualization. Data Virtualization is a flexible approach to breaking down data silos by providing real-time access to information from various systems without needing to physically move or duplicate data. Acting like a bridge, it connects different data sources—whether they’re in databases, the cloud, or older legacy systems—so users can see and analyze everything in one place, as though it were stored together. This approach simplifies how data are integrated and reduces costs, although it might run into some performance issues when dealing with complex queries or systems that require heavy writing operations. Despite these potential hurdles, data virtualization remains a highly effective way to access and use distributed data quickly, making it a valuable tool for improving decision-making without the headache of data duplication. It’s already gaining traction in federal agencies, like the U.S. Department of Housing and Urban Development (HUD) and its Office of Inspector General (OIG), where it’s helping to unify scattered data sources, making it easier to generate reports and perform predictive analysis.
Below is a summary of each approach.
Looking out on the horizon of data architecture, Data Fabric 2.0 and Composable Data and Analytics represent two transformative trends set to reshape how organizations handle their data. Data Fabric 2.0 builds on its predecessor by using AI and automation to make data integration, governance, and access even more intelligent and seamless, especially across hybrid cloud environments. This evolution allows organizations to handle real-time data needs with more precision and efficiency, paving the way for data to move and adapt almost autonomously. Meanwhile, Composable Data and Analytics promises to revolutionize how businesses interact with their data by offering the flexibility to assemble and reconfigure modular data solutions on the fly. This approach incorporates microservices and APIs, giving users the freedom to adjust to changing business needs without the limitations of traditional infrastructures. As demand for real-time insights and agility continues to grow, both approaches are poised to gain substantial traction.
Choosing the Right Approach
Breaking down data silos is essential for improving decision-making, operational efficiency, and innovation in modern organizations. The right approach—whether it’s a Data Warehouse for structured data and reporting, a Data Mesh for decentralized management, a Data Fabric for seamless integration, Data Lakes and Data Cloud platforms for advanced analytics and scalability, or Data Virtualization for real-time access to diverse data sources without replication—depends on an organization’s specific needs and infrastructure.
At Savan, we understand that overcoming the challenges of siloed data requires more than just implementing cutting-edge architectures. It demands a tailored, strategic approach that aligns with each organization’s unique objectives, operations, and culture. Our team is prepared to work closely with our clients to design and deliver solutions that not only overcome the barriers of siloed data but unlock its full potential to drive value and insights.
Authored by Dan Albarran, Savan’s Chief Strategy Officer.
Savan is a premier data and information management-focused firm that is a trusted partner to public sector clients, helping them solve their most critical data challenges with sustainable success that is uniquely tailored to their environment. Savan Group is headquartered in Vienna, Virginia.
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