Book description
Together, big data and analytics have tremendous potential to improve the way we use precious resources, to provide more personalized services, and to protect ourselves from unexpected and ill-intentioned activities. To fully use big data and analytics, an organization needs a system of insight. This is an ecosystem where individuals can locate and access data, and build visualizations and new analytical models that can be deployed into the IT systems to improve the operations of the organization. The data that is most valuable for analytics is also valuable in its own right and typically contains personal and private information about key people in the organization such as customers, employees, and suppliers.
Although universal access to data is desirable, safeguards are necessary to protect people's privacy, prevent data leakage, and detect suspicious activity.
The data reservoir is a reference architecture that balances the desire for easy access to data with information governance and security. The data reservoir reference architecture describes the technical capabilities necessary for a system of insight, while being independent of specific technologies. Being technology independent is important, because most organizations already have investments in data platforms that they want to incorporate in their solution. In addition, technology is continually improving, and the choice of technology is often dictated by the volume, variety, and velocity of the data being managed.
A system of insight needs more than technology to succeed. The data reservoir reference architecture includes description of governance and management processes and definitions to ensure the human and business systems around the technology support a collaborative, self-service, and safe environment for data use.
The data reservoir reference architecture was first introduced
in Governing and Managing Big Data for Analytics and Decision
Makers, REDP-5120, which is available at:
http://www.redbooks.ibm.com/redpieces/abstracts/redp5120.html.
This IBM® Redbooks publication, Designing and Operating a Data Reservoir, builds on that material to provide more detail on the capabilities and internal workings of a data reservoir.
Table of contents
- Front cover
- IBM Redbooks promotions
- Notices
- Preface
-
Chapter 1. Introduction to big data and analytics
- 1.1 Data is key to success
- 1.2 About this publication
-
1.3 Case study: Eightbar Pharmaceuticals
- 1.3.1 Introducing Erin Overview
- 1.3.2 Perspectives from the business users at EbP
- 1.3.3 Signs of deep change
- 1.3.4 Governance and compliance perspectives
- 1.3.5 Positioning the data reservoir in the enterprise architecture
- 1.3.6 The data reservoir
- 1.3.7 Inside the data reservoir
- 1.3.8 Initial mapping of the data reservoir architecture
- 1.3.9 Additional use cases enabled by a data reservoir
- 1.3.10 Security for the data reservoir
- 1.3.11 What does IBM security technology do?
- 1.4 Summary and next steps
- Chapter 2. Defining the data reservoir ecosystem
- Chapter 3. Logical Architecture
-
Chapter 4. Developing information supply chains for the data reservoir
- 4.1 The information supply chain pattern
- 4.2 Standard information supply chains in the data reservoir
-
4.3 Implementing information supply chains in the data reservoir
- 4.3.1 Erin's perspective
- 4.3.2 Deciding on the subject areas that the data reservoir needs to support
- 4.3.3 Information sources: The beginning of the information supply chain
- 4.3.4 Position of data repositories in the information supply chain
- 4.3.5 Information supply chain triggers
- 4.3.6 Creating data refineries
- 4.3.7 Information virtualization
- 4.3.8 Service interfaces
- 4.3.9 Using information zones to identify where to store data in the data reservoir repositories
- 4.4 Summary
-
Chapter 5. Operating the data reservoir
- 5.1 Reservoir operations
- 5.2 Operational components
- 5.3 Operational workflow for the reservoir
- 5.4 Workflow roles
- 5.5 Workflow lifecycle
- 5.6 Types of workflow
- 5.7 Self service through workflow
- 5.8 Information governance policies
- 5.9 Governance rules
- 5.10 Monitoring and reporting
- 5.11 Collaboration
- 5.12 Business user interfaces including mobile access
- 5.13 Reporting dashboards
- Chapter 6. Roadmaps for the data reservoir
- Chapter 7. Technology Choices
- Chapter 8. Conclusions and summary
- Related publications
- Back cover
Product information
- Title: Designing and Operating a Data Reservoir
- Author(s):
- Release date: May 2015
- Publisher(s): IBM Redbooks
- ISBN: 9780837440668
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