skip to main content
Close Icon We use cookies to improve your website experience.  To learn about our use of cookies and how you can manage your cookie settings, please see our Cookie Policy.  By continuing to use the website, you consent to our use of cookies.
Global Search Configuration

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

Recommended Articles

;

Have any questions? Speak to a Specialist

Europe, Middle East & Africa team: +44 7771 980316


Asia-Pacific team: +61 (0)3 960 16700

US team: +1 212-652-5335

Email us at ClientServices@ovum.com

You can also contact your named/allocated Client Services Executive using their direct dial.
PR enquiries - Email us at pr@ovum.com

Contact marketing - 
marketingdepartment@ovum.com

Already an Ovum client? Login to the Knowledge Center now