Businesses are continually grappling with how to organize and use the massive amounts of incoming big data. In fact, a recent survey reveals that 62% of organizations feel they need to improve data management efficiency. But many organizations with legacy systems and overloaded data silos, lakes, and warehouses are unable to efficiently analyze and apply all the stored data to critical decision-making situations. This issue results in limited growth, profitability, and disconnects with clients and partners. But what is the solution to the costly dilemma of holding repositories that sit idle with stored, unused data? The answer lies in data cataloging.
What Is A Data Catalog?
Similar to the concept of product catalogs that are easily accessible pages of inventoried items, data catalogs help locate needed metadata and data sets on topics more efficiently.
The data catalog is stored on a server located either in the cloud or on-premise. The catalog offers the following benefits and features.
- Provides a single reference point for business intelligence systems and databases.
- Enables users such as data analysts and data scientists to effortlessly find, curate, and act on data from a single source within data lakes and warehouses.
- Facilitates easier collaboration from a single data source environment.
- Automates the management of metadata.
- Can be used for extended purposes, which can assist stakeholders in understanding and acting upon the data.
How Data Catalogs Work
According to Gartner, data catalogs assist with four main metadata management tasks to increase user productivity. These primary activities are as follows:
- Discovery of metadata
Modern data catalogs with native connectors and REST-based APIs scan and extract metadata from widely uses sources and types. These sources include data warehouses, operational databases, cloud data stores, and others. Machine language (ML) algorithms perform semantic searches and can browse and filter data as needed. - Understand, enrich, and trust data
Data catalog users can perform a series of additional tasks upon discovery of the data. Users can view profile statistics to determine data quality. Today’s modern ML-augmented data catalogs can tag and annotate metadata. More advanced catalogs allow the creation and maintenance of business glossaries with the ability to import glossary assets. - Contribute and govern
Using a crowdsourced model, users can rate datasets so that data stewards can tag attributes that are too risky for use. Users can also rate the quality and usefulness of data to create records over time. These records help business users identify and track datasets that have not been certified by data stewards.
The lineage of the data assets are also documented for privacy and compliance regulations. This information shows the origin of the data as well as transformation, access, and query histories. - Consume
In this last task, business users can query across the datasets. Many data catalogs are used with popular third-party analytic software packages to connect, scan, and collaborate.
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Why Your Business Needs Data Cataloging
In addition to the typical customer and product data businesses normally generate as part of their day-to-day operations, third-party data from other records and data brokers are also acquired. This imported data may not match the format the business uses. Managing this data without an automated process is a difficult feat that wastes time and resources. Gartner reports that data management’s top two biggest challenges were finding and identifying data that delivers value while supporting both data governance and security. Both factors are centered around the importance of data catalogs.
It is important to note that older, more traditional metadata catalogs cannot effectively address the need to work with more cumbersome and repetitive data tasks. However, today’s machine learning data catalogs can tag and label this data to ensure it is easily accessible to those that need it.
In summary, businesses need machine-learning data catalogs for the following reasons:
- A fast and inexpensive way to inventory and classify disorganized data. Machine-learning catalogs categorize, access, interpret data across multiple sources.
- Data sets are consolidated in a single location for efficient search and discovery by all users. This configuration enables them to flag, comment, share, and recommend useful datasets.
- Compliance with stricter regulatory and compliance rules.
Conclusion
Implementing data cataloging is a vital step for businesses to facilitate data-driven decision-making. The data catalog’s five activities demonstrate how users can harness its power for collaboration, governance, and increased data quality. First Mile is a leading data consulting company that helps empower businesses through proven solutions and processes. Contact us for more information on how our innovative data-driven solutions can take your business to the next level.
As a Data Analyst, Taj helped The Harvard-Smithsonian Center for Astrophysics gather, cleanse, and analyze data via observations made with the Chandra X-Ray Space Telescope on the Orion Nebula star region.
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