Augmented Data Catalog to Improve Analytics

Avatar photo Matthew Monahan February 10th, 2020

Accelerate analytics value data augmentation

Time to deployment. Time to value. Time to insights. These are all top of mind for anyone working with data. When data takes too long to reach your business intelligence and analytics teams, it becomes stale, unreliable, and essentially useless. How can you provide easy access to data augmentation while also maintaining security? Many organizations are adopting augmented data catalogs to help alleviate the time it takes for value to be realized from their data.

In fact, according to Gartner, “By 2022, over 60% of traditional IT-led data catalog projects that do not use machine learning to assist in finding and inventorying data distributed across a hybrid/multi-cloud ecosystem will fail to be delivered on time, leading to derailed data management, analytics and data science projects.”

We’ve found there are five items needed for an augmented data catalog.

1. Catalog everything

Whether your data resides on RDBMS, file systems, external catalogs, or applications, it needs to be indexed in a single data catalog for fast searching and easy provisioning for analytics.

2. Harness the power of machine learning and AI for data augmentation

By applying machine learning (ML) and artificial intelligence (AI) enhancements to your catalog, you can automate some of the more monotonous tasks that take up most of your time. Using a machine-learning led data discovery and classification engine will ensure entities are added and tagged appropriately as soon as the data is ingested. You’ll also be able to perform duplicate data forensics to make sure your data is a single source of truth.

3. Leverage autonomous data management

Workflows and data quality rules that are created with AI and ML in mind can be repeatable and autonomous. This provides another avenue to save time and get to insights faster. Instead of waiting on manual workflows to be created and run, data that matches criteria previously defined (or newly learned) will automatically be entered into a workflow step.

4. Apply right-sized data governance

Having policies created based on user roles and departmental access can not only automate the data access approval process but also allow for scalability as the team grows. No longer will you need to manually add individual users to your permission groups.

5. Provide self-service provisioning

Users that can “check out” the data they need, when they need it are likely to provide faster insights especially when that data can be integrated directly into their favorite data science notebook, like Jupyter. These same users are also able to ingest the data they have without creating a mess down the line (thanks to the automated rules and ML/AI processes put in place).

By implementing these five steps, you can be well on your way to enabling data augmentation that leverages artificial intelligence and machine learning capabilities to save you time and create insights from your data faster than ever before.

Interested in learning more? In a recent webinar, I discussed exactly this topic and dove into the details on each of the five steps listed above.

Want to start leveraging an augmented data catalog for your projects? Schedule your custom demo today!

data augmentation

about the author

Matthew Monahan was Zaloni’s Director of Product Management, with years of experience building every aspect of software applications.