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 risk; enabling an enterprise to more readily succeed. Knowledge is power and quantitative analysis can provide that knowledge. Quantitative analysis can inform the farmer how many apples at price “A” need to be sold before he breaks even, and how many apples at price “B” need to be sold. It will even tell the farmer that if apple prices drop to a certain level, it is better to stay home and let the apples rot n the tree than pay for them to be picked. If the farmer knows that price point, he can transform his business into a ‘picking farm” that is charge people a low rate to come to the farm and pick their own apples. Knowledge may alter the way he does business.
Sources of Strength
In Thomas H. Davenport’s article, Competing on Analytics, Davenport focuses on four sources of strength that a successful analytic competitor will want to pursue. The right focus, the right culture, the right people and the right technology. “Analytics competitors are more than simple number-crunching factories…they also direct their energies towards finding the right focus, building the right culture, and hiring the right people.” (Davenport)
Right Focus
An organization is, by its nature, complex. A business organization will most likely segment various functions. Production, facilities, human resources, accounting, marketing, customer service… Any one of these departments could most likely use support in improving and being more efficient and effective. As a company looks to maximize shareholder value, it is constantly striving to stay ahead of the competition. While a decision to engage resources in becoming an analytic competitor is noteworthy, it can also spell trouble. Davenport recommends as one of the sources of strength, applying the right focus. This means that the first order of business in being an analytic competitor is to uncover the most serious obstacles to reaching company targets and goals. Choosing to focus on what might be the most obvious problems, or the problems that have simple solutions, will not produce the optimal results. Serious corporate soul searching is a necessity. It takes a certain level of vulnerability and trust for executives and employees to uncover the weaknesses in their environment. Once the ‘right focus’ is agreed to, the analyst can begin planning their work of gathering data, creating models and implementing change.
Right Culture
The second source of strength is the right culture. The company culture must support the directive to utilize quantitative methods in its decision making process. Not everyone in a company may feel comfortable with the idea of surrendering to business plans that are statistical and number based. Many people feel that it takes away from the humanness that makes their firm special. It’s possible to imagine business executives wanting their ideas implemented without having to back up their perspectives with statistical research. Transforming an environment like this into one where statistical data is encouraged and respected takes serious effort. Davenport says that the companies where top executives, such as the CEO are pushing the analytics initiative, it is more likely to be implemented. Shifting a culture takes serious effort from upper management.
Right People
Davenports states that a company must not just philosophically support becoming an analytic competitor but serious concentrated actions, such as hiring individuals who excel as quantitative analysis is imperative. Davenport says, “Analytical firms hire analytical people – and like all companies that compete on talent, they pursue the best” (Davenport, p.105)
Right Technology
Davenport explains that it’s not enough to have the right focus, the right culture and the right people, a company also needs the right technology. Investment in technology that can assist in gathering data and analyzing it to provide decision grade information is essential.
Ethics of Statistics
Pursuing knowledge and using it to further one’s efforts (for purposes of good) is supported in Scripture. “A wise man is strong and a knowledgeable man increases in his strength”. (Proverbs 24:5). While the gathering of knowledge and utilizing it is condoned, it would be misleading to present quantitative analysis as ‘fact’ and a solid predictor on future market or customer behaviors. Models are not perfect, not all contingencies can be predicted. From a normative western ethos, the future is uncertain. One must present analysis with a caveat that this is a best guess, and we most probably will come close to our goals based on our understanding of the data. But there is no guarantee. In extreme cases, data can be used in unscrupulous ways. Collecting data on wealth, race and education can lead one to say that a certain minority is unable to learn, and thus condemned to a life of poverty. This is an analysis of the data. Other analysis can be drawn from the same data, people of a certain minority have not been given access to quality education, hence they are in poverty. Drawing biased conclusion based on ideology is unscrupulous, yet it is seen often in the current political environment.
Conclusion
Analytic competitors are growing phenomena. With a narrowing gap in quality and costs between competitors, the “last frontier’ for differentiation, is coming from an in-house process called quantitative analysis. There are four centers of strengths, the right focus, the right culture, the right people and the right technology. As with statistics in general, the use of quantitative analysis should be tempered with an understanding of its uses as well as its limitations.
References
Davenport, T. (January 2006), Competing on Analytics. Harvard Business Review, Vol. 84, No.1., pp. 98-107