Matt Cooke Womens Jersey

[xiaoming]

The Illusion of Business Intelligence Author: Max J. Pucher Oam's Razor: 'Of all possible answers to a question the most simple one is usually the right one.' The proponents of BI however hope to fulfill the vision of Laple; who suggested: '... with an intelligence sufficiently vast to submit all prehensable data to analysis ... nothing would be uncertain and the future and past present to his eyes.' Business Intelligence is not a productive system Ryan Suter Womens Jersey , such as aounting, stockkeeping, or supply chain managent. Custor care or relationip managent ould first be consodered as a tool to manage and trk your custor service Nino Niederreiter Womens Jersey , but is often seen as an analysis tool for custor behavior to improve for example cross-selling rather than service quality. Business Intelligence is plex and expensive sofare for gathering and analyzing masses of data that supposedly will be of help to make better business decisions. It relies on a faith in mathematics that were obtained by Bernoulli, Coombs, Edwards Niklas Backstrom Womens Jersey , Neumann and Morgenstern. They saw heuristic approhes to decision making as defective because it takes resource saving ort-cuts. The proponents of BI now claim that it provides the puting power to optimize decision making by calculating probability for maximum utitlity, as described by Simon (1955). Optimisation however, relies on a number of restrictive assumptions Mikael Granlund Womens Jersey , such as that the process of decision analysis has to be followed precisely and that the data available are correct and relevant. Let's for a mont assu that the garbage-in problem has been solved and the data given to the business executives are good. Klein (1999) provides a list of these restrictions and requirents that were identified in many studies : 1. The goals must be well defined in quantitative terms. 2. The decision makers values must be stable. 3. The situation must be stable. 4. The task is restricted to the selection of options. 5. Exhaustive generation of alternatives. 6. Optimal choice must be possible in reasonable ti. 7. Thorough parison of options. 8. Use pensatory strategy 9. Probaility estimates must be coherent and aurate. 10. Failure prediction must be exhaustive. 11. Evaluation must be exhaustive There are many studies about human decision making and most of them e to the conclusion that 'less is more'. Less rmation about a subject makes for better decisions. Bi can be used to gather and consolidate rmation that then seems to be simpler and easier to use for decisions. The problem is one of prehension and trust. Can the user prehend what the data values truly an? Do the tadata make sense to the deciding person? Can past averages, ans, standard deviations and periodic data be used to predict the future? I propose that just a few people within any organization might even sensibly prehend what those data could an. The old adage of garbage-in-garbage-out still holds. Who knows if the mathematical thods used to process the data are well chosen. Klein (1999) proposes that forcing people to give up their heuristic approh to decision making puts them into 'rmation overload' and questions optimization as the gold standard for decision making. Now that users of Business Intelligence data do not find them too helpful and seem overwheld Matt Cooke Womens Jersey , the new idea is to aid or reple human decision making with predictive analytics, using probability calculated from past data. Probability puting about future events is the next illusion that BI proponents sell. Here is a list of what the executives and managers really need for decision making and don't get from Business Inteligence: - what custors want. - what to do to be petitive. - where business has to innovate. - how the market will ret to current and uping changes. - what petitors are currently doing. - how employees really see the pany. - how to impove the profitability of the business. - how to improve internal munications. - the quality of business processes (not the quality of execution). You might recognize a mon elent in the above list: Knowledge is not about knowing a lot of data, or taking decisions based on data. One has to e up with an ACTION or a list of alternative tions and then take a decision which one to perform. Finding out from BI that revenue is dropping only says that the managent is out of touch with the business. Simon (1972) was concerned that optimization was not prtical in a field setting because of its restrictions. I propose that there is no proof available that BI solves the problem of the restrictions listed above. Business Intelligence can enforce the optimization process for the decision maker and seemingly create the conditions necessary. It can however only propose options based on the data available to the system. It can not propose to the user to go outside the system and analyze other rmation as it brakes the optimization process. As a consequence it BLINDS the decision maker to the real world. Klein (1999) proposes that it the enforced optimization process stops the decision maker from gaining experience for future benefit. Outside opportunities and constraints would be totally ignored. Business Intellilgence enforces a better decision making process but not a better quality of decision making. Tily and good quality rmation is not like having a crystal ball. It is abstrt rmation and will tell the user nothing that he doesn't know. We can only make decisions based on analogies to previously perceived patterns that have to be fairly simple. Statistical sofare does not take better decisions just because it can process more data. Any data given to the user that is not truly relevant turns into noise that reduces the quality of the munication channel and obfuscates the important rmation. Less data ans less noise. Wha . Wholesale NFL Jerseys Wholesale Jerseys Cheap Jerseys From China Cheap NFL Jerseys Cheap Jerseys Cheap NFL Jerseys Cheap Jerseys