Avoid Data Silos and Promote a Single Source of Truth With Data Management, Integration, and a Cloud Data Warehouse

It’s the bane of any data analyst, business user, or executive decision-maker who needs clean and unified data: The data silo.

Data silos, also known as information silos, are information repositories controlled by one group or department and cut off from the rest of an organization (a similar concept to silos on a farm, which contain and keep separate various materials).

While data silos are relatively common at many large enterprises with legacy information systems, their inherent isolation inevitably causes a lot of problems, including:

• Incomplete and inaccessible data: Because each group’s data is separate and inaccessible to other groups, no one in the organization has the full picture from all available data — which can lead to mistaken assumptions and uninformed business decisions.
• Inconsistent data: Siloed data is almost always inconsistently formatted and of poor quality, because each dataset is tended separately by different groups with different formatting standards and rules.
• Inefficient use of data resources: Data silos inevitably lead to added IT costs because each group or department has its own unique datasets and processes, and typically requires its own on-premises or cloud storage and other investments (often managed separately, adding even more redundant costs).
• Compliance and security issues: Inconsistent, incomplete, and inaccessible data is a data security and compliance nightmare. When you can’t find the right data, can’t access it, or don’t understand what it means, it’s basically impossible to meet compliance and data privacy regulations.
• A lack of collaboration and unity: Siloed organizations risk becoming fragmented and dysfunctional as departments jealously guard “their” data, as opposed to embracing a collaborative effort across the entire enterprise.

Despite their ongoing negative impact, data silos are relatively common at large enterprises with several business units, departments, or regional offices. Organizations with decentralized technology procurement or management structures, companies that have gone through acquisitions or fast business growth, and organizations with a corporate culture that discourages openness and common goals regarding data are also prone to developing data silos.

Indeed, most organizations will eventually become siloed to some extent unless there’s a concerted effort in the opposite direction. This is especially true as data volumes and velocities climb ever-higher and legacy systems are confronted with new data types they weren’t designed to handle.

And that’s why a strategy including a cloud data warehouse paired with data integration and data governance tools is so important.

Breaking down data silos: The three most effective ways

Dismantling data silos helps organizations use their data more efficiently and for less cost. But how do you do it?

The most common and effective approaches typically include some form of data management and governance, data integration, and a cloud data warehouse. When implemented correctly, these tools can help clean, de-duplicate, and integrate all your data into one highly available master dataset that’s easily queried for insights.

• Data management and data governance policies: Like anything else, data needs good governance to function most efficiently. That means organizations need a well-defined framework of data governance rules, preferably organized by a centralized data management group composed of employees across the organization. This ensures the organization’s rules for data are known and followed by all departments and groups across the enterprise. Part of your data governance initiative should also be educating staff on the importance of data integration and SSOT. Becoming a truly non-siloed, data-driven organization takes a culture change involving everyone. Otherwise, you’ll just end up with more data silos down the road.
• Data integration tools: Tools such as Google Data Fusion or AWS Glue help integrate an organization’s data from disparate sources — including new data types, such as sensor data from IoT devices — through the creation of extract, transform, and load (ETL) and extract, load, transform (ELT) pipelines. ETL and ELT do just what they sound like: They take data from sources, normalize it with other datasets, and make the data instantly available for analysis within the data lake or data warehouse. Enterprises can also write SQL or Python scripts to migrate data into your data warehouse, but they’re far less efficient when dealing with multiple active big data sources.
• Data lakes and data warehouses: The final piece of the SSOT puzzle is the implementation of data lakes and data warehouses. “A data warehouse is a database where the data is accurate and is used by everyone in a company when querying data,” explains The Data School.

A data warehouse accompanied by a data lake enables organizations to more easily consolidate data sources (typically through joins and unions on data from disparate sources, creating one coherent and centralized dataset while ensuring past data isn’t lost or destroyed). It allows the simplification of database schema. It also allows simplification of tables, columns, and the unification of naming conventions as engineers clean up legacy table and column names.

Indeed, these target systems act as centralized data repositories with the ability to hold and normalize all of an organization’s data in one place, ensuring all your data is highly available and already integrated. Data lakes often hold most of the unstructured or semi-structured data, while data warehouses mostly handle structured data. These systems help keep all your organization’s data in one place.

Breaking down data silos with a cloud-based data warehouse

CapeStart’s big data, data engineering, and data warehousing teams help organizations make better business decisions by harnessing and integrating the power of all their data, from all data sources. We’ll work with you to create custom-made, reliable, and scalable ETL or ELT pipelines; migrate data from your legacy systems to a cloud data warehouse; and deploy DataOps and data management best practices and methodologies.

Contact us to set up a brief discovery call with one of our technical experts and see how CapeStart can help you achieve a single source of truth at your organization.