A "legacy system" is an antiquated method of doing something ("Legacy System," 2010). While usually this refers to some type of archaic or stale technology application--often computer software--the term can be applied to business management too, such as a "legacy manger." In the case of the latter, the legacy manager relies on a dogmatic approach to operations, usually under the philosophy, "if it isn't broken don't fix it." While this may seem practical and safe, it inhibits potential for growth and directly challenges the entire premise of quality management.

Total Quality Management is a management strategy aimed at embedding awareness of quality in all organizational processes ("Quality Management," 2010). As part of this mindset, efficiency and improvement are a constant focus for business operations. A legacy system does not support the quality management emphasis. In particular, the latest technology should be used to collect data, enhance communication, improve decisions, streamline processes, and make other operational changes. A legacy system fails to take advantage of this. The rate of modern technology advance is so fast that an existing system can quickly become a legacy system without a quality improvement focus that allows for quick adoption of new technology. Lack of attention in this regard can result in the technology becoming obsolete and unsupported, which eventually forces an upgrade that could be even more costly. A legacy manager is likely to allow a legacy system to persist. Regardless of his or her shortcomings in general business management theory and practice, the failure to adopt new technology has potentially much greater impact on the overall business operations.

Business Intelligence (BI) is a software-based network that automatically analyzes and synthetsizes data from inter- and intra-business information in order to drive smart decision making (Starr, 2009). Adopting a modern BI model demonstrates a move forward--away from a legacy system. Computer systems allow for analysis of several variables beyond cost. Quantity, delivery time, technical specifications, and many other factors are considered by the computer system. All of these can be hidden "costs" involved in vendor and goods selection. Smart systems take into account all of the important variables, using agorythyms to arrive at the best decision. These algorithms are developed and adjusted using human intelligence and simulation models. In short, the artificial intelligence is enhanced with real intelligence to streamline a process into a super-smart system of automated decisions that are applicable to the entire organizational process, from supply chain to delivery of the product to the customer.

A good analogy for business intelligence is the Google search method. Originally started as a 1996 research project at Stanford University, Larry Page and Sergey Brin developed a formula-based Internet search system ("Google," 2010). An algorithm called PageRank was developed and patented, which relied on several inputs to determine the importance of a web pages in relation to the search string entered by the end user ("PageRank," 2010). Several trade-secret factors contribute to the algorhtym and it is likely that Google management is making ongoing adjustments to the process. Google's algorhtym is analgous with business intelligence in that humans developed algorithms and employed computers and software to automatically make decisions. The effectiveness of Google's formula was clearly evident in its lightening-quick takeover of the internet search market.

Business intelligence operates on a similar principle, the use of software-based decision making that relies on algorithms and several variable inputs. Enormous amounts of data can be analyzed very quickly, for almost instantaneous decisions that would otherwise take humans days or even years to accomplish. The benefit of an intelligent business system is efficiency and speed of decision making. Error and time constraints are cut to a minimum.

In another analogy from my field of study, kinesiology, motion analysis of sport skills was once done manually by digitizing individual frames of motion picture film. This process took several weeks just to collect the coordinate data. Then, the data had to be entered in formulas and computed by hand to quantify the movement (e.g. angular acceleration, velocity). Software systems exist now that process the data in a matter of seconds, thus allowing researchers to focus their time on analysis and interpretation of the results, building them into theoretical concepts. The use of computers to process this information provides an incomparable efficiency advantage to the antiquated "legacy" way of doing things.

In light of all of the advantages of using business intelligence, why would a legacy system persist at an organization? Here are some potential reasons:

1. Cost of the software and system
2. Cost of changeover to business intelligence
3. Risk of business intelligence failure
4. "Legacy manager" mentality
5. Lack of technology understanding or fear of technology

These are certainly valid concerns, especially those of cost. The cost of software, training, and integration for Business Intelligence is very expensive. However, in the business environment were time is money, the quality-focused business cannot afford not to adopt business intelligence.

A good plan for systematic changeover must be used to introduce a new BI system. The current system must be examined fully. Then, the software systems offered by companies like Oracle, IBM, and other should be fully investigated. Other options include hiring in-house software engineers to develop a proprietary system. Yet another option is to consider open source content management systems (e.g. Drupal, JOOMLA) as a framework for building the BI model. These will offer cost savings in the software (since they are free), but may carry extra costs with information technology staff to develop and maintain the system.

Another approach that side-steps the high cost of a full-scale changeovers is to adopt a simplistic BI method that focuses on a few key internal factor of business operations. Then, a slow transition can introduces new components to the system. Eventually the system will move backward (supply chain) and forward (to the customer) as it develops into a mature BI system. This approach needs a sound master plan from the start, in order to avoid turning into an inefficient patchwork system.

"Finding a reliable system that integrates all variables quickly and easily, and uses the enormous capabilities of the web to connect all parts of the business including the supply chain interactively on a global scale, is the ultimate goal" (Starr, 2009, spotlight 12-2). A business intelligence system is not the panacea of business operations because BI systems are at risk of falling prey to "legacy" just like any other technology practice. It is important to keep the technology system evolving and improving in order to avoid the pitfalls that lead to being a legacy system.

References

Google. (2010, April 25). In Wikipedia, The Free Encyclopedia. Retrieved 15:37, April 28, 2010, from http://en.wikipedia.org/w/index.php?title=Google&oldid=358248290.

Legacy system. (2010, February 9). In Wikipedia, The Free Encyclopedia. Retrieved 02:45, April 28, 2010, from http://en.wikipedia.org/w/index.php?title=Legacy_system&oldid=343022071

PageRank. (2010, April 28). In Wikipedia, The Free Encyclopedia. Retrieved 15:39, April 28, 2010, from http://en.wikipedia.org/w/index.php?title=PageRank&oldid=358842233

Starr, M. K. (2009). Production and Operations Management, (2nd ed.). Independence, KY: Atomic Dog.