Analytic Competitors

Topics: Scientific method, Knowledge, Competition Pages: 5 (1588 words) Published: May 29, 2013
Running head: Analytic Competitors

Analytic Competitors
Lev Mallinger
Grand Canyon University
BUS 606: Quantitative Methods
August 24, 2012

“A wise man is strong and a knowledgeable man increases in his strength” (Proverbs 24:5) Introduction
An analytic competitor may be described as an organization engaged in the committed activities of accumulating data, organizing and analyzing it into meaningful information, and following through with business decisions and activities informed by that information. An uninformed layperson may assume most businesses operate as an analytic competitor. This, however, may not be so. Many time business decisions are made through anecdotal or subjective non-quantitative means. An example of this might be a person wanting to open up a restaurant in town. He sees there are no 24 hour diners in the town, so he figures that since he likes diner food, other people must also and without competition he will have a good chance to succeed. It is very possible that he will succeed, but his chances of success might improve if he were to do some research beforehand. Perhaps he could gather data that informed him more about his potential customer base. How interested are they in diner food? Do the town’s people engage in night time activity that would have them out of the house during the night and early morning hours? Buy collecting data and analyzing it the restaurateur can better plan his offerings and hours of operations. While being an analytic competitor does not guarantee success, it will raise his chances of success. We have been taught in American society that if one builds a better mouse trap, the world will beat a path to their door. Perhaps once it was this simple; today not so. While there is value in having a better product to sell than the competitors, it is not a guaranteed path to success. We think a better mouse trap is the answer, but perhaps a less expensive mouse trap will earn more revenue. Or one that is marketed to key demographics will produce a better Return on investment. While having a product or service that truly offers value is still important, knowing who your market is and how to reach them is vital as well. Additionally one must have an estimate of the cost of investment and the potential payback that a venture offers. Success through developing models

Two weeks ago NASA landed the new Mars rover, Curiosity, on the red planet; an amazing feat that included turning the space vehicle into a hovercraft that lowered the rover to the surface of the planet. The complexity of this project involved hundreds of scientist and technicians, coordinating efforts in a variety of ‘sub-projects’ that include do building the space vehicle and rover, propelling it out of earths orbit, landing on a designated spot on Mars, then operating on mars by remote control, driving taking pictures, gathering soil samples and analyzing. Anything one of a thousand details could go wrong, yet computerized simulations of all this were performed in advance. Experimenting with a model allows one to see potential results before risking the actual activity. Mathematical models are virtual scenarios of ‘what if’s”. Understanding the processes, the variables and constants, one can calculate the potential outcome of future actions. In the past a person would consider a piece of farmland, examine the soil via his senses and determine what could be grown on it. Then he would farm the land and take the produce to market. He’s investigative work was gathering data from his personal experience and anecdotal offerings from friends and others. He ate apples, he knew other people ate apples and baked apple pies, so he grew apples. He did not know in any quantitative way what the supply or demand for apples were. He did not know what his expenses were and how many at what price would he need to recoup to break even. Without this information, he operated at more risk. Quantitative analysis can lower the...

References: Davenport, T. (January 2006), Competing on Analytics. Harvard Business Review, Vol. 84, No.1., pp. 98-107
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