Performing Information Governance-A Step-by-step Guide to Making Information Governance Work

Make Information Governance Work : Best Practices, Step-by-Step Tasks, and Detailed Deliverables

This book introduces you to the core components of information governance and how they “thread” into the various functions of EIM. It also covers in detail how to pragmatically and practically execute information governance functions on development projects and in on-going organizational processes.

Most enterprises recognize the crucial importance of effective information governance. However, few are satisfied with the value of their efforts to date. Information governance is difficult because it is a pervasive function, touching multiple processes, systems, and stakeholders. Fortunately, there are best practices that work. Now, a leading expert in the field offers a complete, step-by-step guide to successfully governing information in your organization.

Using case studies and hands-on activities, Anthony Giordano fully illuminates the “who, what, how, and when” of information governance. He explains how core governance components link with other enterprise information management disciplines, and provides workable “job descriptions” for each project participant.

Giordano helps you successfully integrate key data stewardship processes as you develop large-scale applications and Master Data Management (MDM) environments. Then, once you’ve deployed an information asset, he shows how to consistently get reliable regulatory and financial information from it.

Performing Information Governance will be indispensable to CIOs and Chief Data Officers…data quality, metadata, and MDM specialists…anyone responsible for making information governance work.

Coverage Includes

• Recognizing the hidden development and operational implications of information governance—and why it needs to be integrated in the broader organization
• Integrating information governance activities with transactional processing, BI, MDM, and other enterprise information management functions
• Establishing the information governance organization: defining roles, launching projects, and integrating with ongoing operations
• Performing information governance in transactional projects, including those using agile methods and COTS products
• Bringing stronger information governance to MDM: strategy, architecture, development, and beyond
• Governing information throughout your BI or Big Data project lifecycle
• Effectively performing ongoing information governance and data stewardship operational processes
• Auditing and enforcing data quality management in the context of enterprise information management
• Maintaining and evolving metadata management for maximum business value

This book stresses the implementation and operational aspects of implementing information governance using the six core components of information governance and how they thread into other EIM functions such as transactional processing, business intelligence (BI), and master data management (MDM).

With that goal in mind, this book :

• Reviews the functions of EIM and the components of information governance
• Provides a step-by-step approach to performing project-level information governance activities within each of the EIM functions
• Provides a step-by-step approach to ongoing information governance activities within the organization
• Reviews case studies for each of the project-level and operational information governance activities

Analytics-Driven Development: The Business Intelligence SDLC

One of common misconceptions of designing a BI environment is to start with the sources systems. This left-to-right thought process has to do with how we consider the flow of information as we consider the challenges of moving transactional data into the data warehouse. However, one must consider the fact that these data stores are for analytic analysis and reporting. In fact, the data models are structured to facilitate ease of reporting and analytics. Because the designs of these structures are based on the analytics and not the source, the logical “start” for designing a BI environment is to answer the following questions:

• What are the key performance measures (KPMs) needed to run the core business processes?
• What types of reports and analytics are needed for the stakeholders to measure those KPMs?
• What types of data warehouse structures are needed to support those reports and analytics?
• What are the data integration processes needed from the source systems to support the data requirements to provision the reports and analytics?

This simple cadence provides an analytics-driven approach to the SDLC for a BI project and better ensures that what is needed for information needs is what is built.

Information Governance in BI Architectural Decisions

One of the major aspects of engineering a BI environment is the architectural dilemma of determining what goes where from a calculation and aggregation perspective. While this is an architectural question, it has significant information governance implications. For example, a sales-by-quarter calculation in the data integration layer is usually much better managed from a metadata management and data stewardship perspective rather than a user defined sales-by-quarter calculation in an analytic tool such as Business Objects or Cognos. At the same time, one of the actions that tends to drive down the usage of a data warehouse environment is not providing a flexible data environment, where users are asked to formally submit untimely change requests for permission to build simple reports aggregations.

These environment tend to spawn “shadow” data warehouse environments that are user owned data environments that kept out of IT’s budget and control. These environments seldom practice good data management and rarely have any formal information governance processes in place. The best approach is an architectural decision to provision enterprise data for analytics and provide a lower level of control on line-of-business (LOB) or user data.

This figure displays where transformations (e.g., conformations, calculations, aggregations) typically occur in a BI environment. Each transformation poses information governance requirements and challenges, such as the following:

• Data integration transformations
• Data warehouse transformations
• Analytic transformations

The methods, roles, and organizational structures needed are all important considerations from both an architectural as well as an information governance perspective of where and how transformations are performed in the BI environment.

Information Governance Leaders

The first set of constituents is the leaders of the various information governance functions or groups within the information governance organization. This includes those leaders who work with the CDO on the day-to-day activities such as value creation reporting activities as well as the traditional information governance auditing results of the various information governance project and operational activities. Many of the reporting activities are used to both measure the ongoing success for internal purposes as well as external purposes.

The Expanding Role of Information Governance

The expanding scope of information governance is how organizations are placing the responsibility for business-focused responsibilities such as Basel Risk Data Aggregation standard in banking within the information governance organization.

The information governance organization is assuming responsibility in new areas such as master data management, business intelligence (BI), and even overall application development functions.