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Usage monitoring added-value features

Most usage monitoring products collect the core usage monitoring information such as SQL command, its start and end time, CPU time, program and client that initiated the request. Data usage monitoring products distinguish themselves from each other in what features they provide with their products. These additional features can be in the form of standard reports, explains on each SQL statement, collection technique, granularity of data, policy/governance rule engines management, or type of data store. From my experience using data usage monitoring products for a number of years now the two features that provide the greatest value add are data granularity and the rules engine.

Data granularity

Parsing the SQL statements into their lowest form provides a wealth of additional information that can be used to enhance the value of the data collected. The granularity of data increases the depth and breadth of information collected that can be used by a number of users to answer usage type questions. In the past, when IT management, business clients or sponsors would ask how data was being used in a database the answers were filtered through the DBA group. A simple question such as what tables and columns are being used and which clients were using them would be difficult to answer. The DBA group would have to write a specialized script to parse the captured SQL and produce a formatted report or include all the captured SQL statements in a document for the client’s reference. This was not the most efficient and effective way to acquire or deliver the results. Furthermore, there were numerous other question that could not be answered due to resource or time constraints and the unstructured form of the data. Parsing the SQL statement into this lower form and storing it in a relational data store facilitates easier access to the information and is benefits multiple groups in an organization not just the DBA group.

Parsing an SQL statement into its lower level components and storing the results in a relational data store provides an easy and flexible platform to access the data. Firms can leverage their significant investment in Business Intelligence tools such as Cognos, Hummingbird, Business Objects, Actuate and Microsoft Excel and Access rather than relying solely on the reports provided by the vendor. Providing a flexible and extensible reporting layer will give all employees in the firm the ability to harness the raw usage i>nformation and transform it into strategic value. This untethered access to the data will enhance and expand its use throughout the firm.


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