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Business Intelligence Techniques: A Perspective from by Murugan Anandarajan, Asokan Anandarajan, Cadambi A.

By Murugan Anandarajan, Asokan Anandarajan, Cadambi A. Srinivasan

Modern companies generate large volumes of accounting facts every day. the new developments in details expertise have given agencies the power to seize and shop those info in a good and powerful demeanour. in spite of the fact that, there's a widening hole among this knowledge garage and utilization of the information. enterprise intelligence suggestions may also help a company receive and strategy appropriate accounting facts fast and value successfully. Such thoughts contain, question and reporting instruments, on-line analytical processing (OLAP), statistical research, textual content mining, facts mining, and visualization. Business Intelligence thoughts is a compilation of chapters written through specialists within the a variety of components. whereas those chapters stand in their personal, taken jointly they supply a entire assessment of ways to use accounting info within the enterprise environment.

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Extra resources for Business Intelligence Techniques: A Perspective from Accounting and Finance

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Why do I need to understand this stuJf? Isn't this ETL stuft just for the IT guys? " Although this scenario has been crafted to illustrate the disconnect that can sometimes occur between business and technology people in an organization, we believe this type of situation occurs frequently in the contemporary business environment. This situation occurs primarily because most people associated with the project believe that ETL is a technology challenge. This belief is incorrect. As we will see, this is a key misconception about ETL, a misconception that will manifest itself as a set of significant issues when business users begin to look at the information coming out of the Accounting Data Warehouse (ADW).

1. 3 Taxonomy of Data Before data can be transformed into meaningful information, it must first be collected and classified. These two steps are inexorably linked. Where the data come from plays a predominate role in determining the classification given to the data. For example, company generated data, such as a list of check disbursements, may be classified as intemally produced, text 30 Patrick W. Devine. A. Srinivasan. Maliha S. Zaman based, structured data, while an analyst's advice to the CEO may be classified as externally produced, audio unstructured data.

Such systems are called journaling systems. Ledger systems are an example of a journaling system. A non-journaling system does not effective date every transaction. Non-journaling systems are much more common than journaling systems in the uni verse of all potential source systems. The effect of journaling versus non-journaling on the complexity of the extract process is significant. One of the fundamental tasks of the ETL process is to determine what has changed in the source system since the last time the source system was processed.

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