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Introduction

This report explores the trends that are helping the enterprise not only achieve multi-regulatory compliance, but leverage its core principles to accelerate and operationalize data-driven initiatives.

Highlights

  • GDPR compliance programs are maturing into global privacy management programs, which aim to strategically manage multiple evolving regulatory requirements.
  • As algorithms become opaque, the governance of models and process (not just data) becomes critical for both compliance and operationalization of data-driven initiatives.
  • Guided “smart” functionality for end users, driven by machine learning, is increasingly becoming a differentiator between information management products and tools.

Features and Benefits

  • Identifies three key trends in data governance and the regulatory landscape that are shaping the market for products and services.
  • Identifies key suggestions for the enterprise to consider when developing business strategy amid changes in the data governance market.
  • Identifies key suggestions for vendors to consider when developing business and product development strategy in order to meet the current needs of enterprise organizations.
  • Assesses the current state of the global regulatory landscape for data protection and data privacy, providing suggestions for ways that the enterprise can modernize its compliance efforts.
  • Assesses the status of machine learning and AI initiatives in the enterprise, and analyzes the role of machine learning-driven functionality in modern information management products.

Key questions answered

  • What are the key trends shaping the current market for data governance functionality and products?
  • How is the modern enterprise adapting its compliance strategy amid proliferating global regulations for data privacy and data protection?
  • What is the role of data governance in scaling and operationalizing data-driven initiatives, such as data science, in the enterprise?
  • How are the definition and practices of data governance evolving in response to current business needs, such as the need to balance regulatory compliance with enterprise-wide leverage of data?
  • What is the role of AI and machine learning-driven functionality in current information management products, and how can the enterprise most effectively leverage these features?

Table of contents

Summary

  • Catalyst
  • Ovum view
  • Key messages

Recommendations

  • Recommendations for enterprises
  • Recommendations for vendors

Global privacy management programs emerge

  • GDPR compliance is not enough in the data protection era
  • Global privacy management is adaptable and strategic
  • New purchase patterns come with global privacy management

Governance of models and process, not just data

  • Algorithms are becoming more opaque, creating challenges
  • If you cannot govern the black box, govern everything else
  • How to achieve operationalization … and compliance

Machine learning-guided functionality differentiates

  • Self-service is steadily expanding upstream beyond analytics
  • Machine learning-guided functionality is the new battleground
  • Guided functionality expands the pool of enabled workers

Appendix

  • Methodology
  • Further reading
  • Author