What Are Data Silos?
Data is one of a company’s most valuable assets, but its value is tied to how well a company can leverage that data to make business decisions that drive impact and revenue. Data silos prevent companies from getting a full view of their data and those gaps can impact a leader’s ability to make data-driven decisions.
The name “silo” may conjure up images of silos on a farm, where different grains are stored in separate containers. Data silos refer to this same separation of data within companies. Different teams often collect, manage and store their data separately from one another, with access limited to those within certain groups. Sometimes the separation is designed around product units or job functions, but sometimes data silos are created through acquisitions.
In many organizations, data is siloed by type. In this case, structured data is stored in multiple data warehouses, both on-premises and in the cloud. Meanwhile, unstructured and streaming data is stored separately in a data lake. This separation complicates data management and limits the value that organizations can mine from their data.
Why Are Data Silos a Problem?
Data silos are a problem because they impede visibility and access to data, increase inefficiency and costs, hinder effective governance and lead to organizations leaving important insights on the table.
When a company’s data is spread across multiple, independent systems controlled by separate groups, accessing all the data is challenging, if not impossible. Analyzing aggregated data is also difficult. Teams often struggle with duplicate data that is hard to reconcile or missing data that leaves gaps. And having structured and unstructured data stored in different places makes it hard to implement advanced machine learning capabilities and GenAI initiatives. Organizations also need multiple governance models to deal with siloed data, increasing security and compliance risks.
Removing data silos helps leaders get a complete view across their company, reduce duplication and inefficiency, streamline governance and take full advantage of AI capabilities to make data-driven decisions.
Business Costs of Data Silos
Moving from a traditional architecture with data silos to a modern data lakehouse gives companies visibility across their data and can also reduce costs. When data is siloed across multiple teams, each team must take on the cost of infrastructure and IT staff to support their unique data needs. Centralizing data allows companies to consolidate their tech stack and reduce the cost of administration and maintenance.
Modern data architectures, such as the data lakehouse, offer enhanced flexibility and scaling to help companies control compute costs. Performing data preparation and ad-hoc analysis in a cloud solution like Databricks, that scales up and down dynamically based on the compute clusters needed, ensures companies aren’t paying for unused compute resources. For example, moving to Delta Lake and Databricks helped Relogix reduce their infrastructure costs by 80%.
How Do Data Silos Occur?
Data silos often resemble company org charts, with data silos often created when data is separated by different business units or product groups. This separation can be strengthened by conflicting approaches to data management or a desire to keep certain data confidential. However, data confidentiality is better achieved through proper access controls. Data might also be siloed based on job type because disciplines such as engineering, marketing and finance have different data needs and priorities.
Some teams simply don’t talk to each other enough to realize they are duplicating efforts. A lack of communication can also result in teams being unaware of the needs of other teams and not realizing they have data another team might benefit from seeing. As teams independently develop their own approaches to managing and collecting data, siloes only grow, and storing data independently, they inadvertently develop systems that aren’t compatible and make it hard to share data.
Not only do data silos echo organizational silos, but all that data is often stored in a siloed way by type. While structured data is stored in multiple on-prem and cloud data warehouses, unstructured data used for AI is stored in data lakes. Each of these architectural patterns requires its own governance model, limiting the ability of an organization to safely access its data and use it for AI insights that drive competitive advantage.
How to Identify Data Silos
Data silos are often identified organically through use cases that pop up in day-to-day business operations. Teams realize that they lack access to or can’t find certain data. Employees may complain about how much time and manual effort it takes to compile reports. Leaders may receive similar reports from different teams that have discrepancies, duplications or gaps. Teams might start storing and tracking data outside of the typical data tools in order to have more control or faster access to their data, leading to duplicate and offline copies of data.
Companies can proactively identify data silos by performing data audits. Carefully tracking down and documenting various data sources across the company gives leaders a clear understanding of their data management and storage situation. They can use this as a starting point to plan for a transition to a centralized data model. Once silos are removed and a centralized architecture is in place, smaller data audits can be done on a regular basis to detect any new silos and quickly bring them back into the central data repository.
How to Break Down Data Silos
Once data silos are identified a company can start taking steps to break them down and move toward a centralized shared storage solution.
Cloud storage solutions provide a scalable way to store centralized data in a single location, but traditional cloud solutions such as Amazon and Azure often become a storage dump – a shared location to put data without an organizing structure or shared understanding of how the shared storage should be used.
Cloud Data Warehouses bring an added layer of order and understanding through schema definitions. With defined schemas, data can be classified and organized to enable greater analytic insights. However, defining and maintaining these schemas can be time consuming and it can be challenging to support all the data types your business requires in a single schema.
Data lakes are more flexible than data warehouses because they don’t require a data schema and can support all data types, including unstructured and semi-structured data like images, video, audio and documents. This flexibility makes it easy for teams to move to a single, central storage location without needing to significantly alter their data management practices. Data lakes also make analytics on various formats possible and enable users to address concerns about the cost and vendor lock-in of data warehouses.
Data lakes meant that some companies could shift from expensive, proprietary data warehouse software to data lakes. Data lakes also allowed companies to analyze massive amounts of unstructured data in a way that wasn’t possible with data warehouses and also allowed for machine learning.
However, data lakes don’t support transactions and lack security features required by many companies. They can also experience performance issues as data grows. While data warehouses are more reliable in these feature areas, they only support structured data and are not available in open formats like data lakes and data lakehouses.
A data lakehouse combines the scale and flexibility of data lakes with the transaction support and governance of data warehouses, enabling advanced AI and analytics scenarios that truly break down data silos. A data lakehouse enables users to do everything from BI, SQL analytics, data science and AI on a single platform. The lakehouse takes an opinionated approach to building data lakes by adding data warehousing attributes — reliability, performance and quality, while retaining the openness and scale of data lakes.
Lakehouses are built on open source table formats, like Delta Lake or Apache Iceberg. This allows teams to store structured, semi-structured and unstructured data in a data lake, using a portable format that prevents vendor lock-in. These formats offer ACID-compliant transactions, schema enforcement and data validation.
One of the key challenges that organizations face when adopting the open data lakehouse is selecting the optimal format for their data. Any open format is better than putting your data into a proprietary format. However, choosing a single storage format to standardize on can be a daunting task, which can result in decision fatigue and fear of irreversible consequences.
Delta UniForm (short for Delta Lake Universal Format) offers a simple, easy-to-implement, seamless unification of table formats without creating additional data copies or silos. With UniForm, Delta Lake tables can be read as Iceberg tables, so you can use any compute engine that works with the Delta Lake or Iceberg ecosystems.
Another challenge data silos create is limited collaboration, both internally and externally, which restricts the flow of information and innovation. By breaking down these silos and establishing a unified source of truth across data lakes, databases, warehouses, and catalogs, organizations can facilitate seamless access to data and AI assets from any compute engine or tool using Open APIs. This is where Databricks Unity Catalog comes in as the industry’s only unified and open governance solution for data and AI.
With Unity Catalog, organizations can seamlessly govern data and AI assets, including structured and unstructured data, AI models, and files, across any cloud or platform. It enables secure discovery, access, and collaboration for data scientists, analysts, and engineers, boosting productivity through AI. By promoting interoperability and accelerating data initiatives, Unity Catalog simplifies compliance and drives collaboration at scale, all while avoiding vendor lock-in.
Extract, Transform, Load Tools
Extract, Transform and Load (ETL) processes help teams standardize and share data. ETL tools can be leveraged to move data from existing silos into a centralized location like a data lakehouse. Engineers can build ETL pipelines to manage ongoing real-time ingestion and maintain quality control over data coming into the shared central storage.
Culture Shift
Breaking down data silos and preventing them from recurring also requires a culture shift and thoughtful planning about how to migrate systems and processes to use centralized data storage. Understanding what gaps or technical challenges are blocking teams from adopting a new data storage solution is key to getting everyone on board and will inform change management decisions. Ideally, new processes will also be scalable and flexible and able to adapt as company requirements and data needs evolve.
Putting additional governance and data management policies in place will help prevent new data silos from popping up in the future. Clear documentation on policies, standards and procedures is essential for teams to adopt and continue managing their data within a shared central storage. Conducting regular data audits can quickly identify gaps in processes or areas of the company that have not made the cultural shift.
Having executive support and getting management buy-in are key to achieving a culture change. Articulating clear benefits – both short and long term – will help garner support for broader change. Map out current data tasks that will get easier or less expensive and highlight what new capabilities modern architectures enable.
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