Organizational silos and the tendency to hoard work products cause people who should be on the same team to work against each other. Silos can result from rivalry or turf struggles and can be a particularly frustrating part of the culture in any organization—especially one that is large and has subsidiaries or has recently merged.
At the granular level, data silos grow when individual departments or business units maintain their data separately from the rest of the organization. Typically, the data is stored in a standalone system, which is either incomplete or incompatible with other data sets maintained by the organization.
Breaking down organizational silos requires high-level leadership and remedial team-building efforts. However, even the most cohesive organization can experience the adverse effects of data silos. This post is for an organization that requires a well-defined and implemented data strategy to overcome the problems of data silos.
About This Post
Within the context of data management, this post will address the problem of eliminating data silos and address the following key points:
1. Why data silos are problematic 2. How to deal with and eliminate data silos 3. The basic building blocks of a project to eliminate data silos within a business
How Data Silos Cause Problems
Data silos can be a symptom, cause, and contributor to the aforementioned organizational silos.
Organizational access to the data can be limited and isolating.
With data silos, business leaders don’t get a full 360-view of the enterprise information. This approach can lead to multiple technical implementations that become burdensome, redundant, and repetitive over time.
For example, each silo could have its own tools, processes, etc., that need to be supported by IT or the business itself. Data silos are not optimized, consistent, or cost-effective. Instead, data silos can be costly.
Data silos require additional technical infrastructure support and maintenance. Those silos are generally resistant to employing technical specialists as they are owned by the business. Whoever loaded the data silo must provide support and data maintenance.
Likewise, the lack of integration with other data can lead to more narrow decisions impacting revenue and customer service at public contact points. The most severe effect is the potential lack of organizational trust in the data, compounded by less cooperation between end users across the organization.
Data silos have inherent compliance, security, and regulatory issues.
Whether the data is stored in private Excel spreadsheets or standalone business tools or platforms, those silos come with data privacy and security risks. With the proliferation of mobile technology, a compromised or lost device containing personal data could result in catastrophic consequences for the organization.
How to Eliminate Data Silos
The first step toward eliminating data silos is to define the problems or impact that data silos have on the organization and the effect on the business. A Data Assessment can help a company document the current processes and tools, as well as the key business drivers and expected success metrics.
The assessment will provide an audit of the different data management systems relative to how data is collected and used and then apply current and future requirements and priorities to help establish a roadmap for future data integration initiatives.
The result will help define the tools and resources for the use cases to be presented to senior management for their support. The roadmap is the output of the assessment.
The Building Blocks of a Project to Eliminate Data Silos
The basic building blocks of a project to eliminate data silos for the organization are as follows:
1. Be on the lookout for so-called “shadow IT.” Shadow IT occurs when people in an organization use non-standardized systems and software not supported by IT. Aside from the adverse security and data governance ramifications, shadow IT is a sure sign of user dissatisfaction with existing IT resources—so they take data management upon themselves.
2. Establish data governance and security requirements as the foundation for the project. This typically involves data owners and stewards that will establish the rules by which data will be managed.
3. Upgrade the company’s technology infrastructure to support the new data-driven requirements, per the roadmap. This includes both tools (i.e., ETL and real-time data integration, and possibly database management platforms) and supporting resources.
4. Integrate the siloed data management systems per the roadmap. This will result in new data management systems being implemented, which may be physical or logical, such as a data warehouse, data lake, data marts, or various combinations.
5. Cultivate a data-driven culture that promotes transparency, sharing and collaboration of data across the enterprise.
Data silos typically evolve organically over time, often without intention, sometimes as a deeper symptom of organizational silos. Without enterprise-wide oversight, technical decisions may be made at a lower level and may not be the best solution for the overall organization.
Therefore it is critical to develop a unified enterprise-wide data management strategy to ensure that data isclean, accessible, consistent, and trustworthy throughout the organization.