Decision Making
Module 1 Assignment 1
Jasen M Roos
04/02/2014
BUS-660 Quantitative Methods

$228,995,945.00. People love numbers. $68,577,000. To this day they are the simplest and easiest way to tell how anything is doing. 85. Take a quick look at a company’s stock price, a persons salary or how many games a team has won and you can instantly have a fairly accurate idea if they are doing well. 96. Through all the advances in technology and science numbers still are the first things that people go to when they are trying to decide on the value of just about anything. Even more so people love numbers that are easy and straightforward like the ones I have already given. Those numbers do not seem like anything other then numbers put in a paragraph for effect. But they go straight to the heart of everything that modern analytics has tried to accomplish. What are those numbers? Well the first two are the respective team payrolls for the New York Yankees and the Oakland Athletics. The Yankees, the evil empire as they are affectionately referred to as, perennially have had the highest team payroll in all of baseball and the spend big to win big philosophy has paid huge for them as the team has won a record 27 world series titles. Oakland on the other hand has been one of the leagues cheapest teams for well over the last twenty years easily not spending in almost 4 years on their team what the Yankees do in one. How does this pertain to analytics and its business world applications? Well the second set of numbers tell that story, as those are the win totals last year for both the Yankees and the A’s. It was the Oakland A’s who back in the early 2000’s began to embrace “Sabermatrics” or the analytical approach to judging talent and acquiring players to their entire organization. As a result despite being in the situation where they are being outspent by their competition for the best talent by almost a 4/1 margin have been able to field a championship caliber team almost every year as noted by the fact that the A’s have won their division the last two straight years and the high spending Yankees spent October last year watching the games from home. This is as simple of a example as there is at how analytics can help a business achieve its goals. It doesn’t matter if you are a small market team in Oakland or the CEO of a Fortune 500 company, using analytics can help any business run more efficiently and prosper.

As noted by Davenport it takes a lot more then just a bunch of number crunchers and math wizards in order for a company to prosper as an analytics competitor. It first and foremost is a culture change that has to come from the top down. The companies that have quickly distinguished themselves in the current market as analytic competitors tend have in place as Davenport says high placed executive or CEO, “ who have a passion for the quantitative approach.” (Davenport 2006). Now this does not mean that in order for a company to become a analytic competitor they need a person at or near the top who has a background in statistics. To the contrary a lot of current companies that would be classified as analytic competitors don’t have CEO’s or top executives that are number crunchers. What they do have and what they pride themselves in finding are top people in statistics and analytics that have the ability to create, implement and interpret the models and data that they are pouring over and even further prized in these individuals are their ability to take all this data and information that they gather and be able to present it and speak to it to top executives and CEO’s in simple business terms. These people tend to be in very short supply. People who have the technical knowledge to create and implement advance statistical models that can be used to study and conceivable area of a companies business but also have the business and social skills to be able to relay this information in simple terms that will get their points across. As one company in Davenports study noted that they start recruiting and interviewing individuals 18 months prior to when they expect to have the position filled.

It starts with the right culture in a business from their in order for a company to look to become a analytic competitor they need to have the ability to ask the right questions and have the patience in order to wait to find out their answers. As many of the companies that Davenport used to research his paper stated in most cases it was five to seven years in most cases before their was enough data in order to begin to start to look at patterns and be able to make decisions based off of them. This also comes with usually a complete top to bottom overhaul in how a company does business. Most end up doing a complete change of all the technology that they use which can cost a substantial amount of a companies operating expenses. From there a company needs to look to have a data strategy in place. With the collection of millions upon millions of pieces of information from every conceivable place with in the business a company needs to figure out what they can do with this information. As most analytic competitors have hundreds if not thousands of analysts all working on experiments and models for every conceivable are of the business the company needs to have an singular easily accessible place to store all of the data that is collected so that any analyst can easily have access to it at any time.

The sources of strength for any analytic competitor is a list that can go on infinitely. The ability of these companies to use analytical data to improve just about any aspect of their business is mind blowing. For example companies like Walmart currently use analytics to measure such simple things as buying trends for individual products by region and city that allow supply chain managers within the company to use this data to set prices for their own products regionally based on demands across the county and at the same time route inventory almost immediately all across to country to reduce stores from carrying excess inventory in any particular product. Analytic competitors use the information to judge the effectiveness of advertising and break it down by region. One such example is Dell computers who had a study done and had tracked the sales numbers for Dell products before, during and directly after specific advertisements where shown. They even were able to track this all the way down to the regional level. Allowing them, after multiple years of data collection, to be able to create models that they could use to predict which types of advertisements would work the best for specific regions across the country and focus their add dollars to those types of adds. Allowing them to efficiently spend their money in a area that was long thought to be very inefficient.

As a whole analytics are a tool that can help any company be in a better position to achieve their stated goals. Though I do not believe that they are something that should be followed blindly. The real business world and in turn the real consumer is one that can change quickly and because of a multitude of factors. The one thing that analytics may never be able to quantify is human emotion. As Tony La Russa the hall of fame and world series champion coach of the Oakland A’s and St. Louis Cardinals stated when asked about the advancement of analytics in the game of baseball by Buzz Bissinger in his book Three Nights in August, “He appreciated the information generated by computers. He studied the rows and the columns. But he also know they could take you only so far in baseball, maybe even confuse you with a fog of over analysis. As far as he knew, there was no way to quantify desire. And those numbers told him exactly what he needed to know when added to twenty-four years of managing experience.” That in the end perfectly sums up my view of analytics. They are an amazing tool at every leaders disposal and give you the ability to look over and efficiently make decisions that will help you run your business. But in the end it is still the individual that has to make the decisions based off of this information and it should never be looked at as a blue print that has to be followed but more of a compass that can be used to help you read the map more clearly.

Citations

Competing on Analytics; Davenport, Thomas; Harvard Business Review; 12 pages. Publication Date: Jan 01, 2006. Prod. #: R0601H-PDF-ENG