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Introduction

More business users are demanding access to explore data today than ever before, to glean their own insights and drive localized initiatives. New-world explorers care little for established data governance practices, which adds risk to the business.

Highlights

  • Modern data governance requires a true balancing act between enabling exploratory analysis and protecting sensitive business information.
  • We believe that enterprises today need to tackle governance in a much more modular fashion than ever before. From a strategic perspective, this will require organizations to adopt a "divide and conquer" strategy with data to achieve some degree of control.

Features and Benefits

  • Analyzes emerging approaches for governing exploratory analytics.
  • Identifies a working approach to governance for external data.

Key questions answered

  • What are the key challenges to applying a traditional governance model to this area?
  • How can organizations work with exploratory analytics while ensuring that it is reasonably governed?

Table of contents

Summary

  • Catalyst
  • Ovum view
  • Key messages

Recommendations

  • Recommendations for enterprises
  • Recommendations for vendors

Getting the basics right: What is traditional data governance?

  • Overview
  • Traditional data governance treats data as a known physical asset
  • Maturity models are useful for getting started on the data governance journey

Traditional models of data governance are too academic for today's hands-on analytics

  • Data governance needs to adapt

How data governance will evolve

  • Evolution, not a rip-and-replace

Appendix

  • Methodology
  • Further reading
  • Author

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