Meta Data Repository Analysis Business Intelligence Road Map By: Meisam Nazariani Professor: A. Abdollahzadeh Amir Kabir University Of Technology Computer Engineering and Information Technology Faculty AUT - Business Intelligence - Meisam Nazariani 11 December 2009

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 The four groupings of meta data components: ownership, descriptive characteristics, rules and policies, and physical characteristics AUT - Business Intelligence - Meisam Nazariani 11 December 2009

This Chapter Covers The Following Topics: 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 AUT - Business Intelligence - Meisam Nazariani 11 December 2009

Things to Consider: Meta Data Repository Usage Meta Model Requirements Meta Data Repository Security Meta Data Capture Meta Data Delivery Staffing AUT - Business Intelligence - Meisam Nazariani 11 December 2009

Meta Data Repository Definition: 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. AUT - Business Intelligence - Meisam Nazariani 11 December 2009

Contextual Information About 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 AUT - Business Intelligence - Meisam Nazariani 11 December 2009

Some Important Characteristics Of Meta Data: A meta data repository is populated with meta data from many different tools, such as CASE tools, ETL tools, OLAP tools, and data mining tools. Meta data documents the transformation and cleansing of source data and provides an audit trail of the periodic data loads. Meta data helps track BI security requirements, data quality measures, and growth metrics (for data volume, hardware, and so on). Meta data provides an inventory of all the source data that populates the BI applications. Meta data can be centrally managed, or it can be distributed. Either way, each instance of a meta data component should be unique, regardless of its physical location. AUT - Business Intelligence - Meisam Nazariani 11 December 2009

Meta Data Categories: Business meta data provides business people with a roadmap for accessing the business data in the BI decision-support environment. Technical meta data supports the technicians and "power users" by providing them with information about their applications and databases, which they need in order to maintain the BI applications. AUT - Business Intelligence - Meisam Nazariani 11 December 2009

Meta Data Repository as a Navigation Tool: AUT - Business Intelligence - Meisam Nazariani 11 December 2009

Groupings of Meta Data Components: AUT - Business Intelligence - Meisam Nazariani 11 December 2009

Meta Data Usage By Business People: AUT - Business Intelligence - Meisam Nazariani 11 December 2009

Meta Data Usage By Business People: AUT - Business Intelligence - Meisam Nazariani 11 December 2009

Prioritization Of Meta Data Components: AUT - Business Intelligence - Meisam Nazariani 11 December 2009

Meta Data Repository Challenges: AUT - Business Intelligence - Meisam Nazariani 11 December 2009

Example of Meta Data in a BI Query: AUT - Business Intelligence - Meisam Nazariani 11 December 2009

The Logical Meta Model: AUT - Business Intelligence - Meisam Nazariani 11 December 2009

Meta-Meta Data: Since meta data is the contextual information about business data, meta-meta data is the contextual information about meta data. Many components of meta-meta data are similar to those of meta data. For example, every meta data object should have components that cover name, definition, size and length, content, ownership, relationship, business rules, security, cleanliness, physical location, applicability, timeliness, volume, and notes. AUT - Business Intelligence - Meisam Nazariani 11 December 2009

Meta Data Repository Analysis Activities: 1. Analyze the meta data repository requirements. 2. Analyze the interface requirements for the meta data repository. 3. Analyze the meta data repository access and reporting requirements. 4. Create the logical meta model. 5. Create the meta-meta data. AUT - Business Intelligence - Meisam Nazariani 11 December 2009

Meta Data Repository Analysis Activities: AUT - Business Intelligence - Meisam Nazariani 11 December 2009

Deliverables Resulting from These Activities: 1. Logical meta model This data model is a fully normalized E-R diagram showing kernel entities, associative entities, characteristic entities, relationships, cardinality, optionality, unique identifiers, and all attributes for meta data repository objects. 2. Meta-meta data The meta data entities and attributes from the logical meta model must be described with meta data. Meta data–specific meta data components (meta-meta data) are meta data names, meta data definitions, meta data relationships, unique identifiers, types, lengths, domains, business rules, policies, and meta data ownership. AUT - Business Intelligence - Meisam Nazariani 11 December 2009

Roles Involved in These Activities: Data administrator Meta data administrator Subject matter expert AUT - Business Intelligence - Meisam Nazariani 11 December 2009

Risks of Not Performing Step 7: Without meta data, the business people would have a difficult time understanding and using the transformed data in the BI target databases. It would be as frustrating as aimlessly driving a car for weeks or months without a map, guessing your way to your destination. Once the business people perceive the BI application as difficult to use or they think the BI data is unreliable because it no longer matches the source data in the operational systems, they could label the BI decision- support initiative a failure. AUT - Business Intelligence - Meisam Nazariani 11 December 2009