Data catalogs bring together an organization’s data knowledge, business processes, goals and metrics in one place that is easy to access, search and understand. As a result, business users take on data management responsibilities and no longer rely on IT for data tasks such as analytics, data access and reporting. Users quickly discover, understand and maintain data on their own because the data is organized into defined, relevant and searchable assets. Data users can quickly pull information to analyze and convert it into meaningful insights for desired outcomes.
While this scenario sounds ideal, it’s still not a reality for many organizations. In fact, it’s quite a challenge. So how do organizations create a data catalog that’s effective and ensures success?
How would your organization answer these three basic questions?
If you’ve answered no to any of these three questions, your organization still has some work to do.
A successful data catalog contains an inventory of all data assets and knowledge about the organization’s processes to manage and consume data. The results make it easier for data users to use the information.
A data catalog inventory not only serves as an indexed “directory” of all data assets but provides access to data that was not previously accessible or information users were unaware existed in the organization.
Think of it as a centralized repository or single source of truth to ensure transparency, compliance and prevent ambiguities. If data users can discover trustworthy data easily and analyze it to gain insights and make meaningful decisions, your organization is on the right track.
Another key strategy to creating a successful data catalog is to document data lineage.
Data lineage is key to understanding data’s origins and its route across systems. As data moves through extractions and ingestions, it’s manipulated and altered. A successful data catalog provides accountability, visibility and traceability of data’s transformation along its journey.
A quality data catalog tracks multiple views of data lineage, including business and technical data lineage, and an augmented 3D view of lineage — helping users gain different perspectives.
Data catalogs give businesses an understanding of what data is available, where it came from, its business meaning and how it can be used. By tracking business lineage, business users identify critical business process relationships between data sets, data quality scores and metrics. Business lineage also reveals data’s access methods and discloses usage restrictions.
Technical data lineage tracks where sensitive data elements reside, how they change and who has access. It also helps discover data quality issues and how data sets are governed. Technical data lineage establishes regulatory compliance and audibility and improves data security.
Traditionally, a data catalog gave a singular view of data lineage by only documenting details about data on a physical level. Today, an automated data catalog provides a real-time augmented 3D view of the data landscape to help users across departments understand the context around the data and its impact across the organization, systems, policies and processes. As a result, the catalog provides complete transparency into data and business knowledge around all enterprise information.
Finally, by also integrating automated metadata, data profiling and lineage ingestion into the data catalog, organizations can automatically discover data patterns and descriptors
Without data lineage as part of a data catalog, organizations wouldn’t understand the data’s lifecycle from creation through its transformation. Users would also have a difficult time identifying and understanding available data sets.
A successful data catalog helps manage data quality and brings trust and confidence in data-driven decisions. A data catalog should automatically catalog data quality rules, apply it to the data behind the metadata and make it available for users to make better decisions. As part of the monitoring and data quality checks, a quality data catalog will identify and help eliminate data redundancies, duplicates, missing values and formatting inconsistencies, ensuing high-quality, trustworthy data.
Diverse teams can spend more time extracting value from data than trying to understand it. They can quickly organize the technical details around data assets into defined, meaningful and searchable business terms to deliver a consistent understanding of data among all consumers. In addition, data quality controls help refresh and monitor data quality over time. By including data quality scores alongside 3D lineage, users can quickly measure the impact data quality efforts have on business expectations.
Data teams and data stewards require automation to help curate, support and maintain the data catalog.
By autonomously harvesting metadata, lineage and profiling data, companies derive different views of lineage, identify relationships with other business assets, quickly understand business context around data, including data quality scores. An automated data catalog also measures business impact and, ultimately, creates a searchable, browsable, curated business intuitive data catalog.
Organizations that create a successful data catalog also ensure reliable and protected data. Organizations reduce the risk of data misunderstanding, misuse and ensure users are leveraging the correct data for the right purposes.
The organization gains a repository of data knowledge that includes data’s business meaning, its usage and how it impacts associated data. The information is easily accessible to data users across the enterprise and provides essential details around data, ultimately improving trust among business users.
Have you enabled business users to immediately derive value from data assets with absolute confidence? If not, read more about Infogix’s Data360 data catalog solution.
Can you connect your data assets to your organization’s goals and initiatives so that you can see and measure how data drives business outcomes? How 3D Data Lineage
Provides Detailed Information Around Data Catalogs