Data Catalogs: Accelerating Analytics and Data Quality Operationalization

Enterprises have been under a lot of pressure to stay competitive amid the business decline caused by COVID-19 lockdowns and closures. One trend is clear, though: despite trying to cut costs, companies increasingly rely on data to innovate and come up with new revenue streams. 49% of enterprises have either launched new analytics projects or are proceeding with ones already planned.

What Slows Down Analysts

As the bulk of operations in many industries has gone online, new data sources have emerged and the influx of data has increased. This presents both an opportunity and a challenge for data analysts and scientists. On one hand, they have more data readily available—possibly very granular data on prospects and customers. On the other, they have to understand this new, raw data and get access to it, fast.

At the same time, getting access to data has actually become a little harder. One reason for this is that IT teams are overloaded with business continuity tasks. Another is that the appetite to invest in complicated and costly data integration projects is low, while the benefits are hard to justify.

Lastly, enterprises have become more aware of data governance and personal data protection, and therefore need to strike a balance between restricting data access to sensitive data elements and allowing access to data and metadata for analytics.

How a Data Catalog Speeds Up Prep Work

Now more than ever, data analysts and data scientists can benefit from a data catalog with the following critical capabilities:

  • A self-service way to search for and request access to data
  • A sandbox environment for safe and iterative data preparation
  • Support for data governance policies
  • Data quality information for each data set and the ability to evaluate data with additional checks

How Ataccama Helps

Ataccama's data catalog is part of a modular, self-driving data management and governance platform. What makes the Ataccama data catalog stand out is its built-in business glossary, data preparation capabilities, and quick data quality evaluation and monitoring features. It is a fully audited solution that supports roles and permissions for comprehensive data governance compliance.

From Discovery to Operationalization

Finding and preparing data faster is important for analytics, but what really matters is solution operationalization. Below are the three steps for getting data ready to be used for analytics or operational use cases:

  1. Sandbox: Give business users an easy way to work with data.
    • Provide easy, self-service access to data, subject to roles and permissions
    • Create data preparation recipes and instantly preview results in a safe sandbox environment while source data remains untouched
  2. Enhance: Pass the initial configuration to ETL developers or data engineers to prepare the advanced configuration.
    • Connect it to data sources and specify outputs
    • Add additional transformations and enrich data
    • Fine-tune data processing performance
    • Prepare the solution for integrating with the organization's ETL pipelines
  3. Operationalize: Pass the advanced configuration to IT admins to integrate the solution with the organization's ETL pipelines.
    • Schedule the solution and set up launch triggers
    • Make sure the solution works with the rest of the systems in the organization

Watch how this would work within the Ataccama platform in practice in this webinar recording:

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