Accelerating Data Literacy Using Machine Learning Data Catalogs

While data catalogs have been around since the 1950s, the first ML-powered data catalog, the “Automated Data Catalog”, was not introduced until 2012 by enterprise software firm Alation. These automated catalogs enabled capabilities that seem obvious today, such as automatic metadata capture, but they paved the way for the supercharged ML data catalogs from other vendors, such as Collibra and Atlan.

1. Automated data tagging: “Home Address” is automatically tagged as “PII” and sorted into a secure access management pool and a “Customer” data domain for consumption.

2. AI-powered semantic search: By referencing search history, ML data catalog search predicts the most relevant data asset and expedites search for the user.

3. Automated data lineage mapping: Automatically captures transformations to a table from the System of Record (SOR) to the dashboard used for business consumption.

4. Data quality enhancement: The ML catalog identifies inconsistent formatting (i.e. “May 2023”’ instead of “20230501”) and provides suggestions to improve the data.

5. Automated data profiling: By analyzing the integration of liquidity data across the tech ecosystem, data teams at financial institutions are alerted to potential data quality issues that can be resolved to accurately demonstrate their risk exposure.

6. Data Discovery: When a database with consumer behavior metrics is integrated into the catalog, ML capabilities automatically classify the data and expedite future retrieval.

With these added capabilities, organizations can organize, visualize, and contextualize their data at scale, improving the quality of insights and accelerating time to delivery of analytics projects that directly support top-level decision making.

Data literacy, as previously stated, is the foundational step in becoming a data-driven organization. If data consumers (data analysts and scientists, decision makers, etc.) don’t understand the data, it’s no better than excess storage, a net negative when considering the cost of storing data.

ML-powered data catalogs support data literacy not only by removing barriers to learning about the data, but more importantly, by explaining it in the language of the business. For example, automated data tags can organize data assets into business-specific domains based on various elements, providing a common denominator that both a data engineer and an HR executive can use. Furthermore, when non-data roles are able to leverage data assets to improve their output, they’ll turn to data (and the data catalog) the next time they face a similar challenge, organically creating a data-literate and data-driven organization.

Becoming a data-driven organization is imperative given the rapidly-evolving nature of today’s business environment. In a research study conducted by Traci Gusher, a data and analytics (D&A) leader, 93% of companies indicated that they would continue to “aggressively” increase their investments into D&A capabilities. However, according to Deborah Leff, CTO of Data Science and AI at IBM, 87% of data science projects never make it past the planning phase, adversely impacting data ambitions.

With enormous investments being made by companies across all industries, the winners will be those who are able to help their stakeholders become data-literate. Succeeding in the mission to become data-driven has shown increases of EBITDA by up to 25%.

It’s important to understand that a company cannot become data-driven unless it has first taken the necessary steps to become data-literate. Empowering people with a single source of truth for their data, powered by ML capabilities that remove redundant manual tasks such as lineage mapping, assigning data tags and owners, and profiling data, boosts transparency and trust.

Machine learning has supercharged data catalogs and transformed them into an essential tool for today’s business landscape. The ability to take the guesswork out of understanding complex datasets through consistent “intelligent” actions increases transparency, which then builds confidence in data assets, resulting in greater use of data, generating greater insights, and producing an end product of data-driven decision making.