If you aspire to run a data-driven organization, it’s time to get interested in data silos. It’s a more common issue than you’d expect.
Data silos impact company operations and the data analytics projects that underpin them. Silos prevent executives from using data to manage processes and make sound business decisions.
Imagine what happens to a business where sales or customer service employees can’t access critical information about customers, products, and supply chains. This is another negative impact of data silos.
Data silos are a challenge many organizations face today. None of them can afford to keep data disconnected and scattered.
How can data version control help address and prevent data silos? Keep reading to find out.
What is a data silo?
A data silo is a storehouse of data maintained by a single department or business unit and isolated from the rest of the organization, similar to how grass and grain in a field silo are kept apart from outside factors.
Siloed data is often stored in a separate system and is incompatible with other data sets. This makes it difficult for users from different parts of the organization to access and use this data.
Issues caused by data silos
Data silos can affect a company in the following ways:
Incomplete datasets
Data silos keep data separated from those who cannot access it. As a result, strategies and decisions can’t be based on all available data, potentially leading to poor decision-making. Silos can also prevent the creation of data warehouses or data lakes that connect various data sources for business intelligence (BI) and analytics purposes.
Inconsistent data
Many data silos don’t align with other data sources. Such anomalies cause data quality, accuracy, and integrity challenges for end users in operational and analytical applications. They’re especially problematic when external users, clients, and partner organizations use an application programming interface or online app to access one walled data source while data from other internal sources differs.
Create duplicate data platforms and procedures
Data silos drive up IT costs by increasing the number of servers and data storage devices that a business must purchase. In many circumstances, departments deploy and operate these systems separately from an organization’s data management team. This results in increased spending and wasteful usage of IT resources.
Less collaboration among end users
Isolated data sources in silos limit the chances for data collaboration and sharing among users from other departments. When various teams lack visibility into compartmentalized data, it becomes more difficult to collaborate effectively.
Departments are organized in silos
Data silos contribute to organizational silos: departments and business units that keep their data private and are hesitant to share it with others. They may also oppose data governance programs that seek to break down data silos and ensure that firm data is consistent and correct across all of an organization’s platforms.
Data security and regulatory compliance concerns
Individual users maintain certain data silos in Excel spreadsheets or online business tools such as Google Drive, which are typically accessed via mobile devices. If firms do not have appropriate controls, the dangers to data security and privacy escalate. Silos also impede efforts to comply with data privacy and protection requirements.
Why are data silos created?
Data silos may have technical, organizational, or cultural origins. They tend to arise naturally in large organizations since various business divisions frequently function autonomously, with their own goals, priorities, and IT budgets. However, any company that lacks a well-planned data management strategy may develop data silos.
Technological aspects of data silos
Some companies decentralize IT purchasing choices, allowing departments and business units to purchase technologies independently. This frequently results in deploying databases and business applications incompatible with or tied to other systems.
The same might happen when corporate IT teams are involved in purchase choices when a department needs a specific technology. The multiplicity of data platforms presently available also contributes to data silos.
In addition to traditional relational databases, organizations can use big data platforms, NoSQL databases, cloud object storage services, and special-purpose databases to satisfy various business requirements.
People and culture aspects of data silos
Even when IT and business activities are more closely integrated, company culture can encourage the formation of data silos. There are fewer incentives to avoid them if data sharing is not a cultural norm and a business lacks uniform data management goals and principles. Departments may also regard their data as an asset that they own and control, which promotes data silo growth.
Growing companies are prone to data silos. As a firm grows, new business requirements must be met rapidly, and new business units can be established. Both of these scenarios are natural data silo incubators. Mergers and acquisitions also introduce silos inside an organization, both known and hidden.
Data lake as a concept to reduce silos
To eliminate silos in data management systems, teams can pool all business data into a cloud-based data warehouse or data lake.
This central repository is geared for efficient analysis. Data from many sources will be homogenized and integrated, allowing individuals or groups to reconcile business needs with privacy and security effortlessly.
When data is consolidated and integrated, you can centralize data access and control through a data governance framework. Robust data access policies enable self-service analysis, allowing business users with authority to readily access the required data without the frustrations or delays when IT workers serve as gatekeepers.
When it comes to centralizing data, you can choose from on-premises or cloud services. On-premises ETL and ELT solutions streamline transporting data from several sources to the data warehouse. These technologies extract data from sources, turn it into a common format for analysis, and load it into a data warehouse within the organization’s data center.
Cloud providers are simplifying and speeding up the ETL process, as data and the cloud are inextricably linked. Cloud-based ETL leverages the cloud provider’s infrastructure, including a data warehouse and ETL tools optimized for their specific environment. ETL technology unifies data from multiple sources into a single location for analysis, breaking down silos – ultimately improving data integrity and ensuring fresh data for all users.
How lakeFS helps on top of data lakes to solve data silos
Data version control solutions like lakeFS help to address the problem of data silos in multiple ways:
RBAC for fine-grained access control
In lakeFS, users can set up resource access to be managed similarly to AWS IAM – through Role-Based Access Control (RBAC).
The system consists of five essential components:
| Component | Description |
|---|---|
| Users | The entities that access and use the system. A user is assigned one or more Access Credentials for authentication |
| Actions | A logical action within the system, such as reading a file or establishing a repository |
| Resources | Unique identifiers representing a specific resource in the system, such as a repository, item, or person |
| Policies | A collection of actions, a resource, and an effect: whether these activities are permitted or prohibited for the specified resource(s) |
| Groups | A named collection of users. Users can belong to several groups |
Access is controlled by adding Policies to either Users or Groups. Every system activity, whether an API request, UI interaction, S3 Gateway call, or CLI command, requires a set of activities to be permitted for one or more resources.
Commits and Pull Requests for change management
Data is quickly becoming one of an organization’s most valuable assets, and like code, it’s always evolving. However, data change management has historically lacked the same level of governance, transparency, and control as code.
Data updates can be dangerous because minor flaws in a dataset can spread to models, dashboards, and decision-making processes, resulting in costly mistakes.
This is where pull requests for data in lakeFS come in.
Pull Requests allow data practitioners to propose changes to datasets in a controlled environment. Changes, such as updating a dataset, adding new records, or amending metadata, can be reviewed before merging into the main data branch. Teams can leave comments, do validations, run data quality tests, and confirm that the data is correct before it’s included in the production pipeline.
This building component also allows us to implement the Write, Audit, and Publish pattern in a very straightforward way.
Branch protection for enforcing organization-wide best practices
lakeFS, like other version control systems, lets you set up Actions to run when certain events occur – for instance, to keep data quality high.
Branch Protection rules, on the other hand, restrict direct changes and commits to particular branches. Only merges are permitted on protected branches. With pre-merge hooks, you can validate your data before it reaches your critical branches and is exposed to customers.
You can use glob syntax to construct rules for a specific branch or branch that fits a specified name pattern.
Wrap Up
Integrating data with different systems is the most straightforward way to break down silos. The most common type of data integration is extract, transform, and load (ETL), which involves extracting data from source systems, consolidating it, and loading it into a target system or application.
A solution like lakeFS sitting on top of your data lakes helps to reduce data silos from both the technological and people/culture perspective.
