Chapter Seven. Step 7: Meta Data Repository Analysis


graphics/ch07.gif

Chapter Overview

This chapter covers the following topics:

  • Things to consider when analyzing whether to license (buy) or build a meta data repository

  • Why it is important to deliver meta data with every BI project

  • The differences between the two categories of meta data: business meta data and technical meta data

  • How a meta data repository can help business people find and use their business data after the data has been standardized for the BI decision-support environment

  • The four groupings of meta data components : ownership, descriptive characteristics, rules and policies, and physical characteristics

  • How to prioritize meta data for implementation purposes

  • Five common difficulties encountered with meta data repository initiatives: technical, staffing, budget, usability, and political challenges

  • The entity-relationship (E-R) meta model used to document the meta data requirements

  • A definition and examples of meta-meta data

  • Brief descriptions of the activities involved in meta data repository analysis, the deliverables resulting from those activities, and the roles involved

  • The risks of not performing Step 7

Things to Consider

Meta Data Repository Usage

Who will be using the meta data in the meta data repository?

What standards do we have in place for its use? What standards do we need to develop?

Do we already have a meta data repository? If not, will we license (buy) one or build one?

Will this meta data repository support only the BI decision-support environment, or will it be used for all systems throughout the organization?

How will we know if we are using the meta data repository effectively?

Meta Model Requirements

Do we need to create a meta model for the meta data repository, or do we already have one?

If we have one, what meta data objects do we need to add to it?

Which meta data repository products support our meta model?

Are these meta data repository products extendable?

Meta Data Repository Security

What type of security will we need for the meta data repository?

Who will be authorized to enter and maintain the meta data?

Will everyone be authorized to access any meta data at any time?

Meta Data Capture

What types of business meta data do we need to capture?

Will we be using a computer-aided software engineering (CASE) tool to capture the business meta data?

What type of technical meta data do we need to capture?

Will we be capturing technical meta data in the extract/transform/load (ETL), online analytical processing (OLAP), and other access and analysis tools?

How will we extract the meta data from these tools and migrate it to the meta data repository? Who is responsible for migrating it?

Who will connect the business meta data from the CASE tool to the technical meta data from the ETL, OLAP, and other tools?

Meta Data Delivery

How will meta data be displayed? How will it be accessed? Will we have a Web interface to the meta data repository?

Will we need to produce meta data reports? What types of reports ?

How will these reports be distributed?

Will there be a help function (online tutorial)? Will the help function be context sensitive?

Staffing

Do we already have a meta data administrator? If not, do we have a data administrator with technical skills who can perform the functions of a meta data administrator?

Will we have to hire more meta data administrators?

How will meta data responsibilities be divided among data administrators and meta data administrators?

A meta data repository is a database. But unlike ordinary databases, a meta data repository is not designed to store business data for a business application. Instead, it is designed to store contextual information about the business data. Examples of contextual information about business data include the following:

  • Meaning and content of the business data

  • Policies that govern the business data

  • Technical attributes of the business data

  • Specifications that transform the business data

  • Programs that manipulate the business data

Contextual information about business data inherently exists in every organization, whether it is documented or not. When contextual information is documented, it is known as meta data. When the information is not documented, it is usually not known to everyone in the organization. As a result, business people often invent their own business rules and create their own redundant data along with redundant processes, not realizing (or sometimes not caring) that what they need may already exist. Forty years of such practices have now brought most organizations to the brink of drowning in data and dehydrating from lack of information.



Business Intelligence Roadmap
Business Intelligence Roadmap: The Complete Project Lifecycle for Decision-Support Applications
ISBN: 0201784203
EAN: 2147483647
Year: 2003
Pages: 202

flylib.com © 2008-2017.
If you may any questions please contact us: flylib@qtcs.net