When we look at data retention and storage – possibly short-term copies for backup purposes, but generally long-term storage for business use or archiving – inevitably you end up having to look at how the storage is structured: Siloing data into separate storage spaces – or unifying your storage structures down into one cohesive whole.
What’s a data silo?
A data silo is a repository of data that’s controlled by one department or business unit and isolated from the rest of an organization – similar to a farm grain silo.
In other words, it’s a description of what happens when your data structure is disunified and begins to create parallel systems.
Why are data silos a problem?
- Incomplete data sets. Data silos lock data away in separate data sources from users who can’t access it.
- Inconsistent data. Many data silos aren’t consistent with other data sources. For example, a marketing team might format customer data differently than other departments.
- Duplicate data platforms and processes. Data silos add to IT costs by increasing the number of servers and data storage devices an organization must buy.
- Less collaboration between end users. Isolated data sources in silos reduce the opportunities for data sharing and collaboration between users in different departments.
- Data security and regulatory compliance issues. Some data silos are stored by individual users in Excel spreadsheets or online business tools like Google Drive, often on mobile devices. That increases data security and privacy risks for organizations if they don’t have suitable controls. Silos also complicate efforts to comply with data privacy and protection laws.
Are there any benefits?
- Enhanced Security and Compliance.Despite the last point, assuming serious issues are avoided isolating sensitive datasets can reduce exposure risk and simplify compliance with laws such as GDPR or HIPAA. By keeping data segmented, organizations can enforce stricter access controls and limit the blast radius of potential breaches.
- Departmental Autonomy and Specialization.Different business units often have unique data needs and workflows. A siloed structure can allow teams to optimize tools, schemas, and processes for their specific functions without being constrained by enterprise-wide standards. For example, marketing may prioritize campaign analytics while
- Reduced Complexity in Early Stages.For rapidly growing teams, maintaining separate datasets can speed up deployment of specialized systems without waiting for enterprise integration.
- Controlled Data Governance.In some cases, silos can act as governance boundaries, ensuring that only vetted, relevant data is shared externally or across departments. This can help prevent misuse or misinterpretation of raw, uncontextualized data.
- Performance Optimization.By keeping high-volume or high-frequency data localized to the systems that use it most, organizations can reduce network load and improve query performance for mission-critical applications.
How can we work around this?
One approach is to unify data structures down as much as possible. This can be a large infrastructure-level undertaking – Romania’s government, for example, is creating massive a private Cloud specifically to allow a unification of all government data.
There are less extreme steps that can be taken, however:
- Establish a common data pipeline. Ensure that your data is ending up captured by common backups and archives – this helps with compliance as well as allowing merging and deduplication.
- Unify data under a single data platform.
- Where possible, standardize.
Your Data In Your Hands – With TECH-ARROW
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