Commentary: Many vendors offer business intelligence solutions. Project managers need to understand not only the vendor application but more importantly the business strategic objectives.
The article “Best Practices for Great BI Performance with IBM DB2 for I” discusses another vendor solution to the general information science problem of data storage. Practices such as Business Intelligence, BI, relate more to contextual presentation of data patterns. How data is stored, retrieved, then processed on demand is essential to good BI. BI is a subset of the broader discipline of Decision Support Systems, DSS, which are at the top of the information food chain. Information sub-systems collect, sort, validate, and store data before passing the information to the next level. As the data moves up through the levels the quality of the data improves.
The article “Best Practices for Great BI Performance with IBM DB2 for I” discusses another vendor solution to the general information science problem of data storage. Practices such as Business Intelligence, BI, relate more to contextual presentation of data patterns. How data is stored, retrieved, then processed on demand is essential to good BI. BI is a subset of the broader discipline of Decision Support Systems, DSS, which are at the top of the information food chain. Information sub-systems collect, sort, validate, and store data before passing the information to the next level. As the data moves up through the levels the quality of the data improves.
Acquiring quality data for DSS systems and applications begins at a much lower level. Operational sub-systems collect data and business rules validate then store data usually in several different relational databases. Typical operational sub-systems data involves data entry of customer data, employee time clock data, repair data, bookkeeping, and logistical information collected by scanners. Information across these databases are then gathered or organized in support of operational level processes which add value to the data through operational processes. Typical operational processes involve activities such as purchase orders, payroll, travel, customer service, financial statements etc… Operational level data across numerous systems and databases is then rolled up into a DSS system. DSS level processes are dramatically different than operational processes. DSS processes look at the character of the data sets, for example trends, patterns, and behaviors, in order to form strategic decisions. Because of the large data sets involved in DSS; storage, processing, and reporting of the data is critical in order to meet on demand for review requirements in a timely manner.
The common approach currently in use is the data mart that are working subsets of larger primary databases and present a unique view. These data marts when organized in ways to permit multi-dimensional modelling of the operations are called data cubes. The use of methodologies such as online transaction processing (OLTP) and online analytic processing (OLAP) can continuously usher data into the data cubes in support of on demand reviews. Numerous vendors are beginning to offer services in this arena although the methodologies and markets are still being shaped. “BI will surge in the mid market” (Cuzillo, 2008) “In the last two years or so we have seen some important new technologies emerge and begin to influence BI, and I believe they’ll have an even more significant effect in the coming year. Some examples include SOA/Web services (and overall componentized design), in-memory analytics, integrated search, and the use of rich media services to provide more compelling (Web-based) user experiences.” (Briggs, 2008)
Commentary: Decision support systems are a growing business interest as markets become increasingly volatile. Possessing a fundamental understanding of these systems and their value to business is pinnacle to architecting effective systems. Many businesses continue to struggle with the best way to employ information technologies and serve business intelligence needs. Project manager's implementing these kinds of projects need to be involved right from the inception in order to maintain a focus on the strategic objectives. It is the project manager who corals and focuses these projects for the senior leaders and achieving their visions.
Commentary: Decision support systems are a growing business interest as markets become increasingly volatile. Possessing a fundamental understanding of these systems and their value to business is pinnacle to architecting effective systems. Many businesses continue to struggle with the best way to employ information technologies and serve business intelligence needs. Project manager's implementing these kinds of projects need to be involved right from the inception in order to maintain a focus on the strategic objectives. It is the project manager who corals and focuses these projects for the senior leaders and achieving their visions.
Reference:
Englander, I. (2003). The Architecture of Computer Hardware and Systems Software: An information Technology Approach. (3rd ed.). New York: John Wiley & Sons Inc.
Cain, M. (2008). Best Practices for Great BI Performance with IBM DB2 for i. Penton Media, Inc. Retrieved from http://www-03.ibm.com/systems/resources/Great_BIPerformance.pdf
Cuzzillo, Ted, Dec 2008. Analysis: BI Transformation in 2009, TDWI, Retrieved from http://www.tdwi.org/News/display.aspx?ID=9262
Briggs, Linda, Dec 2008. Q&A: Market Forces That will mold BI in 2009, TDWI Retrieved from http://www.tdwi.org/News/display.aspx?ID=9263
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