How to Enhance Business Intelligence with a Single Source of Knowledge

Paul RasmussenAugust 26, 2020

Video: A Chief Data Officer is often at the mercy of spreadsheets and data silos

Business users demand high-quality data to develop products and services, gather customer intelligence and generate revenue. However, there are barriers preventing business users from being data self-sufficient so they can conduct timely analysis, and act on critical business intelligence.

For example, since many business users from different departments don’t have the technical expertise to prepare data for enterprise use, they often rely on IT to provide the analytical data critical to their projects. But with IT departments usually on overloaded, the request can go unfilled for weeks.

Enabling business users to become self-sufficient with enterprise data in a timely manner requires a modernized approach—a single source that organizes and catalogs business knowledge that all data users can access.

Enhancing Business Intelligence with a Single Source of Knowledge

Today, a data catalog should serve as the single source of knowledge of an organization’s data assets, providing a full 360-degree view of both business and technical information. The catalog should supply an extensive and detailed register of all enterprise data assets. By including comprehensive details around policies, objectives, data quality scores, metrics, governance processes, standards, rules, glossaries and business knowledge, the data catalog serves as a reference for all data users—business and IT—across the enterprise.

Data catalogs also provide a comprehensive business and technical view of an organization’s “data lineage” to track data movement from the moment it is created, as it passes through different sources, through to final consumption.

By including both a comprehensive business and technical view of data lineage, companies dramatically improve efficiency and productivity.

For instance, technical data lineage traces the physical level of data including schemas, tables, columns and transformations. Tracking these details allows the IT department to quickly discover data quality issues and locate any regulated data used for compliance or audit.

Business data lineage describes how data fits the business and the impact to data if business users alter its use. These details provide business users with a clear understanding of the flow and dependencies of their data, crucial business process relationships, data quality scores, data access methods and usage restrictions. This improves organizational data literacy, fosters better collaboration between business and IT, and enables the creation of reliable business intelligence. Automated data lineage also provides additional business context around enterprise data assets.

Automating Data Lineage Ingestion

Typically, data management platforms and data catalog tools only present data lineage through data location and technical meaning. However, modern data intelligent platforms include enhanced features far beyond traditional lineage capabilities.

With additional technologies for data governance, data quality, analytics, metadata management and automated data lineage ingestion, a data intelligence platform automatically profiles and discovers data patterns and descriptors. As a result, data users quickly deduce data lineage and its relationships with business assets, easily discover business context, identify quality information and measure knowledge impact.

With all these details documented directly into the catalog, organizations deliver a browsable, well organized, business ready data catalog. With an extensive, single source of data knowledge, across all data siloes, business users can quickly connect data assets to a wide array of business outcomes and use cases to generate impactful insights that increase profits.

Are you looking for more information about breaking down data silos and establishing a single source of knowledge? Watch this video, above or below, for more detailed information.

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Video: A Chief Data Officer is often at the mercy of spreadsheets and data silos