Vendors are adding ML capabilities and AI engines to make self-configuring and self-tuning processes pervasive. These processes are automating many of the manual tasks and allowing users with less technical skills to be more autonomous when using data. By doing so, highly skilled technical resources can focus on higher-value tasks. This trend is impacting all enterprise data management categories including data quality, metadata management, master data management, data integration and databases.

Through 2022, data management manual tasks will be reduced by 45% through the addition of machine learning and automated service-level management.

Gartner Predicts

What does Augmented Data Management Enable?

  • Emergent metadata can be inferred from data utilization, users and use cases rather than descriptive metadata that is often no longer synchronized with actual data capture/write and subsequent usage.

  • Organizations need to easily know what data they have, what it means, how it delivers value, and whether it can be trusted. Utilizing the statistics of existing systems and their available capacity and known resources policy-level instructions will determine where data operations will take place. They can even manage their production deployment to the point of reconfiguring that deployment when necessary.

  • Data fusion can track which assets are used by use case, and form a knowledge and utilization graph. When new data assets are encountered, fusion engines will analyze the similarity to other well-known data assets. They will determine their affinity to existing data/use cases, then alert other automated systems that new data is available, and is a valid candidate for inclusion.

  • Dynamic data identification allows new and existing data assets to be evaluated “in stream,” and cumulative information about them to be used to develop related event models. Over time, the use cases will form processing requirements that indicate which data needs to be provided for operational and/or analytics use cases — and the rate or frequency of providing it.

How Does This Impact Your Organization and Skills?

  • Augmenting the data engineer or automating some data engineering tasks.

  • Alerting data engineers to potential errors, issues, and alternative interpretations of the data.

  • Creating automated system responses to errors, issues and alternative interpretation of data.

  • Increasing the capability to use publicly available data, partner data, open data and other assets that are currently difficult to determine as appropriate for utilization.

  • Automating data interrogation that mimics data discovery and even evaluates the “confidence” that new assets conform to known or existing models.

We've got you covered!

Relevant Sessions

  • Augmented Data Management Forges a New Alliance Between Human and Artificial Intelligence
  • Upgrade to a Data Fabric Design to Transform Your Data Management and Integration Architecture
  • Augmented Data Catalogs: Now an Enterprise Must-Have for Data and Analytics Leaders
  • From Self-Service to Enterprise to Augmented Data Preparation — Understanding the Data Preparation Tools Market
  • Ask the Expert: What Is the Typical Roadmap for AI Adoption?
  • Ask the Expert: Getting More from the Data Lake

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