Governing and Managing Big Data for Analytics and Decision Makers

A draft IBM Redguide publication


Having the right data is key to success with analytics. A data lake is a collection of data repositories that has been created to provide a rich set of data for data discovery, analytics, ad hoc investigations and reporting. Governance and self-service capabilities are key to enabling an organization to use the data from the data lake with confidence. A data lake that is built with management, affordability and governance at its core, is called a data reservoir.

This IBM Redguide publication describes the benefits of a data reservoir and how to design and operate it for your organization. The guide discusses how it fits into the existing business IT environment, and identifies sources of data for the data reservoir. It also provides a high level architecture of a data reservoir and discusses key components of that architecture. It identifies key roles essential to creating, supporting, and maintaining the data reservoir and how information integration and governance play a pivotal role in supporting the data reservoir.

Table of contents

Executive overview
Why build a data reservoir
Analytics in the business world
Working with a data reservoir
Data reservoir in the business environment
Architecture of the data reservoir
Organizational change and impact
Rolling out a data reservoir
How IBM helps make it happen


These pages are Web versions of IBM Redbooks- and Redpapers-in-progress. They are published here for those who need the information now and may contain spelling, layout and grammatical errors.

This material has not been submitted to any formal IBM test and is published AS IS. It has not been the subject of rigorous review. Your feedback is welcomed to improve the usefulness of the material to others.

IBM assumes no responsibility for its accuracy or completeness. The use of this information or the implementation of any of these techniques is a customer responsibility and depends upon the customer's ability to evaluate and integrate them into the customer's operational environment.


Last Update
08 July 2014

Planned Publish Date
29 August 2014

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