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Introduction

Data privacy is an increasingly key driver of business decisions and IT practices. If done correctly, data management architecture can drive privacy and compliance as well as facilitate more proactive functions such as the use of data in analytics.

Highlights

  • Data privacy depends on consistent control of data, and consistent control of data is a driver of business value.
  • "Siloed" data architecture is not a feasible approach to the management of data privacy, because it fragments control and creates disparate policies for data.
  • The managed data lake, with a shared metadata framework, is the ideal environment for ensuring that privacy controls are consistently applied.

Features and Benefits

  • Identifies the long-term benefits of a robust data privacy program within the enterprise.
  • Assesses the drawbacks of a "siloed" data architecture with regard to data privacy.
  • Evaluates the key benefits of the managed data lake approach for gaining control of data.
  • Identifies strategies for ensuring that a data privacy program is an ongoing process rather than a single project.

Key questions answered

  • How can data privacy support analytics initiatives and be a driver of business value?
  • Why are data silos detrimental to the implementation of a data privacy program?
  • How does the managed data lake approach help gain control of data for data privacy implementation?
  • Why does data privacy need to be an iterative process rather than a one-time compliance project?
  • What role will AI and machine learning play in the role of information governance of big data?

Table of contents

Summary

  • Catalyst
  • Ovum view
  • Key messages

Recommendations

  • Recommendations for enterprises
  • Recommendations for vendors

Data privacy can be a key driver of business value

  • Privacy and protection is about much more than compliance
  • Consistent control is necessary for all use of data

Data silos are unsustainable and therefore unsuitable

  • Segregating sensitive data in silos is an outmoded technique
  • Compatibility between systems is increasingly important

The managed data lake provides a route to privacy

  • A "single source of truth" for data, regardless of where the data resides, is the goal
  • A shared metadata framework is critical to governance
  • Inheritance of governance settings maintains control of data

Privacy and data protection is an iterative process

  • May 2018 is not going to be the finish line for data privacy
  • The entire business must be involved in governance and privacy
  • Automation and machine learning need to play a bigger role

Appendix

  • Methodology
  • Further reading
  • Author

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