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Introduction

While automated database functionality currently targets lower-level technical tasks, lifting the burden of daily drudgery from administrator roles, the technology has promise for augmenting higher-level tasks related to data governance.

Highlights

  • To date, most automated database functionality is focused on basic data security rather than high-level governance. However, this helps meet regulatory requirements for data protection “by design and by default.”
  • Data privacy will be a main use case for automated database functionality. Machine learning algorithms to detect and protect sensitive data are rapidly improving.
  • The cultural shift associated with automated database functionality will fuel data governance efforts. DBAs, and related roles, will have more time available to contribute to strategic, collaborative efforts.

Features and Benefits

  • Identifies current capabilities associated with automated database functionality, and examines their historical evolution.
  • Analyzes the role of current automated database capabilities in the holistic enterprise data governance effort.
  • Identifies key strategic recommendations for both vendors and enterprises, given the current capabilities and evolution of automated database functionality.
  • Identifies potential data governance use cases for automated database functionality, considering current and roadmap capabilities offered by database vendors.
  • Evaluates the cultural changes that automated database functionality will have on administrator roles, and identifies ways the enterprise can best leverage existing talent.

Key questions answered

  • What are the current capabilities of automated databases, and how do these capabilities affect or augment enterprise data governance strategy?
  • How do the automated security features of automated databases help achieve governance requirements for data protection, as required by regulations such as GDPR?
  • Will automated database capabilities, to any extent, replace the capabilities of dedicated data governance tools?
  • What are the potential data governance pitfalls of automated database functionality, and how can the enterprise avoid them?
  • What are the cultural and process-oriented changes required by the enterprise to effectively leverage and retrain database administrator talent, given the era of database automation?

Table of contents

Summary

  • Catalyst
  • Ovum view
  • Key messages

Recommendations

  • Recommendations for enterprises
  • Recommendations for vendors

Self-running database functionality: an evolution

  • Automated and AI-driven functionality did not appear overnight
  • Governance is the final frontier for database automation

Autonomous capabilities to date focus on security

  • Automated security is a necessity given modern threats
  • Ensuring data protection "by design and by default"

Privacy will be a primary use case for functionality

  • Data warehouse and transaction processing are primary uses
  • Helping detect and protect sensitive data assets

The shift to "autonomous" frees up human capital

  • Refocusing valuable human skill sets on governance
  • A people and process challenge, not just a technology one

Appendix

  • Methodology
  • Further reading
  • Author

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