Decoding the What and Why of Top Data Catalog Capabilities

In today’s digital world where data is everywhere, enterprise data catalogs have become a “must-have” asset within information architectures. While some years back, it was considered more of a “nice-to-have,” over time things have changed significantly. Why is that so? Nowadays, most businesses gather enormous amounts of data, sometimes in various data formats, making it increasingly difficult to manage valuable information at scale.

What is a Data Catalog?

It is a reference application available to any data user (developers, administrators, users, and analysts) to find, learn about, and tag data for future reference. IT professionals swear by data catalogs to engage in organizing, integrating, and curating their data. Since big data is becoming more robust, new approaches to finding, understanding, and establishing trust in data are more and more relevant. Best suited to perform self-service analytics, data catalogs comprise multiple features that make it easier for the data users to engage in data collaboration through the ability to provide pre-decided data definitions useful in design analytics models and organizing related data.

Data catalogs aggregate metadata on datasets meant for analysis. According to the metadata, the standard database objects are tables, queries, and schema that are stored either in a data lake or data warehouse. Annotations, sample projects built-in business intelligence (BI), even analytic applications can be used to augment a data catalog.

In the physical form, a data catalog is either an on-premises or cloud-based server that indexes data systems on an automatic basis and even provides a data inventory of those assets that allows single-source access. Also, a data catalog crawls the databases and BI systems and offers a single point of reference for the enterprise data.

A number of data catalogs are capable of using machine learning to offer further behavioral context on how the data is being leveraged. By carrying out the log analysis, a data catalog becomes capable of making certain assumptions about the worthiness or even quality of the data being accessed by the data catalog. Apart from being an enormous boon for technical data users, data catalogs offer relief for the non-technical users to consume data productively. As compared to the extract, transform, load (ETL) tool, the information stored in a data catalog resides in its native format, so going back to the original application is much simpler.

All in all, data cataloging allows an organization to become data-driven rather than being just data-rich.

Why Organizations Should Care about a Data Catalog

While many businesses have started to realize the relevance of centralizing their enterprise data, they are not aware of how tough it is to securely access that data. The difficulty occurs because the data is ingested from various places with different amount of structures.

For all data lake deployments, data catalogs are an essential element because they are capable of ensuring that data sets are properly tracked, identifiable by the business terms, and governed as well as managed. The ability to scale is necessary when handling large amounts of data. Data catalogs are well suited to ensure that corporate data governance policies are put into practice for driving enforcement and carrying out audits for compliance.

The inclusive nature of a data catalog allows it to be used for collaboration and centralized data sharing in any recognized location accessible across the organization. For the data scientists, data engineers, and other analytical users, it has become the entry point in developing data sets for analytical use. Data catalogs ensure different teams can readily collaborate on data set usage, quality, and business descriptions.

A data catalog can be employed in a minimal and nearly standalone manner as it enacts as a technology tool. Even a business considered data-light can benefit from a data catalog through policy enforcement, strengthened collaboration, and data-backed decision-making enablement and due to a data catalog’s straight forward integration.

Top Data Catalog Capabilities that are Worth Appreciating

Considering the global data catalog market size is predicted by Report Consultant to grow from $200 million this year to $600 million by 2026, data catalogs will likely fast become an integral part of the data landscape. An organization planning to go for a new data catalog offering has to ask the right questions for their business and emphasize those features that will make it worthy of their investment.

Here’s a list of the top features a top-class data catalog should support:

• To effectively scan and load the metadata collected from varied prospective data sources, automation is mandatory. Efficient data catalogs leverage artificial intelligence and machine learning for intelligent profiling, tagging, and also populating the objective metadata about the data quality.
• Enablement of human review and stewardship of automatically populated tags should be among the data catalog features. AI backed catalogs should use machine learning to automatically learn from the human content additions to bring improvement in the accuracy of automated tagging.
• A good quality data catalog should allow adding of comments, flags, support rating data sources, and offer information about data sets for context.
• To maintain the relevancy of data catalog, maintaining new and incremental scans is essential to update the current metadata. This is vital concerning identifying sensitive data or data that is subject to regulatory compliance policies.
• Data catalogs that have been natively developed on the big data technology like Spark, Solr, or cloud infrastructure are the best option because they will support the scaling of services across the organization. An effective data catalog is expected to manage a wide variety of data source types (semi-structured, structured, unstructured, or even relational) irrespective of its location (hybrid, cloud, or on-premises) and should be able to scale further with the ever-strengthening data landscape.
• Data catalogs should be proficient enough to easily integrate with a wide range of business tools like data prep, data discovery BI tools, business glossaries, and BIs with open APIs.
• A data catalog that is capable of using a search engine like Solr, one that comes with the ability to scale so that the users are able to search the data catalog without any problem, allows for making better data-driven decisions.
• New initiatives on data security, governance, data rationalization, and consent management applications will compel data catalogs to adapt themselves to the ever-changing data legislation. It is best to initiate a solution that is capable of supporting this vision.
• Data catalogs should be able to inform where the data originated and where it was sent. To resolve the data lineage concerns, the data catalog should proficiently be able to identify and propose missing lineage between the data sets to make it easier for the missing data lineage chains to make a manual entry.
• An efficient data catalog should support data masking and offer role-focused, granular data security.

Conclusion

Since data integration challenges are escalating and becoming more complex as the data types and volume keeps on growing, data cataloging becomes a necessity. One good example is regulatory compliance. With the much talked about European GDPR already in place and the yet-to-be-implemented CCPA becoming effective next year, businesses are reeling under the pressure of becoming aware of exactly where the all the data of employees, customers or prospects as stakeholders reside to avoid incurring a penalty.

It is vital that organizations realize data catalogs are multi-faceted in boosting data rationalization, improving overall data accuracy, and standing at the heart of any data management strategy. So, if you haven’t yet explored data cataloging as an option, the time is now, because, in the coming days, the world of data will be continuing its rapid growth.

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