The Importance of Meta Data


Meta data describes an organization in terms of its business activities and the business objects on which the business activities are performed. Consider, for example, a sale of a product to a customer by an employee. The sale is a business activity and the product, customer, and employee are the business objects on which the sale activity is performed. Business activities and business objects, whether manual or automated, behave according to a set of relationships and rules, which are defined by the business. These activities and objects, and the relationships and rules that govern them, provide the context in which the business people use the business data every day.

Meta data is so important for the BI decision-support environment because it helps metamorphose business data into information. The difference between data and information is that information is raw data within a business context. Meta data provides that business context; that is, meta data ensures the correct interpretation (based on activities, objects, relationships, and rules) of what the business data actually means.

For example, what is profit ? Is it the amount of money remaining after a product has been sold and everybody who was involved in that product has been paid? Or is it a more complicated calculation, such as "total annual revenue minus sum of average base cost per product minus actual staff overhead minus accumulated annual production bonuses minus wholesale discounts minus coupons divided by twelve?" Does every business person have the same understanding of profit? Is there one and only one calculation for profit? If there are different interpretations of profit, are all interpretations legitimate? If there are multiple legitimate versions for profit calculations, then multiple data elements must be created, each with its own unique name , definition, content, rules, relationships, and so on. All of this contextual information about profit is meta data.

Since meta data provides the business context in which business data is used, meta data can be viewed as a semantic (interpretive) layer of the BI decision-support environment. This semantic layer helps the business people navigate through the BI target databases, where the business data resides. It also helps the technicians manage the BI target databases as well as the BI applications.

Some important characteristics of meta data and meta data repositories are listed below.

  • 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.

Meta Data Categories

There are two categories of meta data: business meta data and technical meta data.

  1. Business meta data provides business people with a roadmap for accessing the business data in the BI decision-support environment. Since many business people are relatively nontechnical, they should have access to meta data, which defines the BI decision-support environment in business terms they understand.

  2. 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.

Table 7.1 highlights some differences between business meta data and technical meta data.

Table 7.1. Business Meta Data versus Technical Meta Data

Business Meta Data

Technical Meta Data

  • Provided by business people

  • Provided by technicians or tools

  • Documented in business terms on data models and in data dictionaries

  • Documented in technical terms in databases, files, programs, and tools

  • Used by business people

  • Used by technicians, "power users," data-bases, programs, and tools (e.g., ETL, OLAP)

  • Names fully spelled out in business language

  • Abbreviated names with special characters , such as "_" ( underscore ) or " “" (dash), used in databases, files, and programs



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

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