Today's competitive global business environment, understanding and managing enterprise wide information is crucial for making timely decisions and responding to changing business conditions.

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  1. Introduction

Today’s competitive global business environment, understanding and managing enterprise wide information is crucial for making timely decisions and responding to changing business conditions. Many companies are realizing a business advantage by leveraging one of their key assets - business data. There is a tremendous amount of data generated by day-to-day business operational applications. In addition there is valuable data available from external sources such as market research organizations, independent surveys and quality testing labs. Studies indicate that the amount of data in a given organization doubles every five years. Data Warehousing has emerged as an increasingly popular and powerful concept of applying information technology to turn this huge islands of data into meaningful information for better business decisions.

1.1.        Data Warehousing

A data warehouse is a subject-oriented, integrated, time-variant, non-volatile collection of data in support of management decisions.

  • Subject-oriented means that all relevant data about a subject is gathered and stored as a single set in a useful format;
  • Integrated refers to data being stored in a globally accepted fashion with consistent naming conventions, measurements, encoding structures, and physical attributes, even when the underlying operational systems store the data differently;
  • Non-volatile means the data warehouse is read-only: data is loaded into the data warehouse and accessed there;
  • Time-variant data represents long-term data--from five to ten years as opposed to the 30 to 60 days time periods of operational data.

Data warehousing is a concept. It is a set of hardware and software components that can be used to better analyze the massive amounts of data that companies are accumulating to make better business decisions. Data Warehousing is not just data in the data warehouse, but also the architecture and tools to collect, query, analyze and present information.

1.2.        Concepts

1.2.1.        Operational / informational data:

Operational data is the data you use to run your business. This data is what is typically stored, retrieved, and updated by your Online Transactional Processing (OLTP) system. An OLTP system may be, for example, a reservations system, an accounting application, or an order entry application.

Informational data is created from the wealth of operational data that exists in your business and some external data useful to analyze your business. Informational data is what makes up a data warehouse. Informational data is typically:

  • Summarized operational data
  • De-normalized and replicated data
  • Infrequently updated from the operational systems
  • Optimized for decision support applications
  • Possibly "read only" (no updates allowed)
  • Stored on separate systems to lessen impact on operational systems

1.2.2.        OLAP / Multi-dimensional analysis:

Relational databases store data in a two dimensional format: tables of data represented by rows and columns. Multi-dimensional analysis solutions, commonly referred to as On-Line Analytical Processing (OLAP) solutions, offer an extension to the relational model to provide a multi-dimensional view of the data. For example, in multi-dimensional analysis, data entities such as products, geographies, time periods, store locations, promotions and sales channels may all represent different dimensions. Multi-dimensional solutions provide the ability to:

  • analyze potentially large amounts of data with very fast response times
  • "slice and dice" through the data, and drill down or roll up through various dimensions as defined by the data structure
  • quickly identify trends or problem areas that would otherwise be missed

Multi-dimensional data structures can be implemented with multidimensional databases or extended RDBMSs. Relational databases can support this structure through specific database designs (schema), such as "star-schema", intended for multi-dimensional analysis and highly indexed or summarized designs. These structures are sometimes referred to as relational OLAP (ROLAP)-based structures.

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1.2.3.        Data Marts:

Data marts are workgroup or departmental warehouses, which are small in size, typically 10-50GB. The data mart contains informational data that is departmentalized, tailored to the needs of the specific departmental work group. Data marts are less expensive and take less time for implementation with quick ROI. They are scaleable to full data warehouses and at times are summarized subsets of more detailed, pre-existing data warehouses.

1.2.4.        Metadata/Information Catalogue:

Metadata describes the data that is contained in the data warehouse (e.g. Data elements and business-oriented description) as well as the source of that data and the transformations or derivations ...

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