Description

Meta data is one of the hottest arenas in information technology today. Corporations have realized the value of meta data and the absolute need for it for their businesses to thrive in increasingly competitive markets.

Building a managed meta data environment (MME) is no longer an option, but an absolute requirement for corporations. The MME represents the architectural components, people and processes that are required to properly and systematically gather, retain and disseminate meta data throughout the enterprise. Companies have realized that without an MME their IT departments cannot properly manage their systems and their systems cannot provide true value to the business end user. An MME is the system that manages your company’s information systems.

This presentation acquaints the audience with the basic concepts of meta data and enterprise data management, focused on the managed meta data environment. It will illustrate how meta data management is a critical part of any enterprise data management program. Robust meta data management is much more than simply implementing a meta data repository; so the architecture of the Managed Meta Data Environment (MME) will be reviewed. Finally, the question of “buy versus build” for the meta data repository will be addressed.

Audience

• Data Architects
• Enterprise Architects
• Data Administrators
• Database Administrators
• Database Designers
• Systems Analysts
• Business Analysts
• Project Managers
• IT Professionals

Seminar Outline:

• Meta Data Overview
• Meta data defined
• Meta data example
• Current and preferred state of data management
• Types of meta data
• Meta data value
• Meta Data in Enterprise Data Management
• Enterprise data managment (EDM) defined
• EDM goals and objectives
• EDM components and technologies
• Managed Meta Data Environment (MME)
• MME defined
• Six layers of the MME
• MME development challenges
• Meta data sources
• Buy versus Build
• Current meta data tools
• Product evaluation
• Making the decision

Have you wondered why some organizations succeed in implementing Data Governance and Management initiatives towards cost and revenue optimization, risk management or compliance adherence, while others fail? Are you in the process of planning and designing a single view of customers, suppliers, products or partners to yield higher returns from your data assets?

Traditionally, IT and data governance were centered around standards and processes with emphasis on applications and systems. However, given the challenges of semantic reconciliation and quality management, coupled with the lack of alignment between IT and business, many governance programs have not yielded the results that were originally intended. There is a heightened awareness of a need for a more focused and disciplined approach to manage data with increased automation.

Join Informatica – an industry authority on Data Governance and Management – presents regarding:

• the guiding principles for successful Data Governance and Management initiatives
• phased methodology for the program design and execution,
• assessment on Data Governance attributes - Quality, Consistency, Availability, Accessibility, Auditability and Security,
• customer success stories on data governance and management transformation
• linking IT investments to business goals and plan phased delivery
• balance of strategies and tactics to take an initiative engagement to completion and expand scope

Through conferences, sales calls and seminars, more and more companies share pain points with their data warehouse deployments. Most of these companies are struggling with the same issues:

• "I can't meet the demand of increasing new data warehouse project requests"
• "The numbers that are derived from the warehouse do not match what the business has"
• "My processing windows have grown to the point that I can not add any more data"
• "The information in my warehouse is stove-piped so we are having trouble integrating new subject areas"

The presentation followed by the more in-depth two-hour workshop address these issues and others that are common in data warehouse deployments.