5 Considerations for Augmented Data Management

Amy King November 14th, 2019

Augmentation is a growing trend that companies should adopt to automate many of their management processes and free up valuable time from their data engineers. Gartner predicts that through augmented data management, by the end of 2022, there will be a 45 percent reduction in manual data management tasks.

Augmented data management (ADM) involves using machine learning (ML) and artificial intelligence (AI) engines to automate some of the manual tasks involved with managing data. This means making data quality checks, metadata and master data management, and data integration “self-configuring” and “self-tuning”.

Here are 5 things you need to know before you begin the process:

1. Augmented Data Management works for any use case

Since augmented data management is on the data layer, you can use it for a variety of use cases. For example, financial services companies could implement a new portfolio based on the most-often traded markets…all gleaned from the metadata the system collects. Don’t think your use is too complex or simple for this trend.

2. Have projects ready

Implementing ADM will free up a lot of time from your data engineering and data steward teams. Ensure that they’re working on higher-value projects for the company by outlining these projects to fill their newly freed-up time prior to setting up an ADM solution.

3. Governance can’t be ignored

The augmented data management system will only be as good as the rules you have in place. Your data governance needs to be strong and set in place before augmenting your data management. The system will allow users automated access based on their permissions. If those aren’t correct, you could be exposing data to unauthorized users and risking a data breach in violation of the law.

4. Consider your use case

Before you start the process, lay out the fields you’d like to be populated by your new AI & ML-enabled data management system. Want an inferred industry? How about the last viewed asset? Create those fields so they can be populated as your system grows and learns.

5. Ensure your resources can handle the compute

Wanting augmented data management and being able to “afford” it are two different things. Ensure you have scalable compute (anyone say cloud?) in place to handle the increased load requirements needed to implement both AI and ML on your data.

How to get started

Taking these considerations in mind before starting your own augmented data management will make sure you’re in a position to succeed. Then again, why start your own project when you can use a data platform that enables all this and more?

Zaloni’s platform, Arena, utilizes automation and machine learning to create active metadata and governed user access for a complete, end-to-end data management solution. With machine learning models that implement user-verified training data, you can be sure to have a single source of truth for all your projects. Interested in learning more? Contact us for your custom demo.

about the author

Amy King

Amy King was Zaloni’s Vice President of Marketing and now is CMO at Relias.