When it comes to MBA degree programs, the emphasis is typically on analysis of business cases; strategic thinking; best practices; and standard techniques of management, marketing, finance, manufacturing, and other functional areas. But one area of study often underrepresented is the use of data.
Unfortunately, the result is glossing over one of the most important aspects of running a business. The smart interpretation of data is critical to achieving goals. The more intelligently you can use the data resources at hand in a modern corporation, the more easily you can steer toward the outcome you want.
The extensive use of data analysis might have developed the reputation as an occupation for specialized nerds. And yet, that flies in the face of the common experience of most people. If you were going to drive across country to a new city, you might look at maps, traffic reports, and even information coming from a GPS unit to direct your travels. When you arrived, chances are that you’d ask for recommendations from friends on social networks or look at guide books.
On the most elementary levels, people know they must collect information to solve problems and undertake tasks. The quality of the information and the way you make use of it has a profound influence on the outcome of your endeavor.
The same is true of business. “World class” best practices, case studies, and other variations of what people have done in what seemed to be similar circumstances certainly has its place in teaching people the process of making decisions. And yet, no two businesses are exactly the same, no matter how similar. Internal processes, customer demographics, various strengths and weaknesses of managers, differences in financial tactics and circumstances — all these factors and others suggest that the specifics of strategic plans held by one won’t necessarily fit the needs of another.
The quality of data becomes an important issue. If the information that is the basis for a decision is unreliable, can the results be anything else? A simple example would be that a prospect database is in need of a process called de-duping, in which the company scours the database for duplicate records as well as for entries that have already become customers that should require different treatment. Absent this step, a company could both waste money and annoy prospects with duplicate emails, calls, or mailings.
The choice of what data to consider or exclude, or the techniques of interpreting the data, will affect decisions and courses of action. Sometimes a given type of data like sales history might have little bearing in the context of new markets or product introductions. Data is necessary, but its use can be tricky. That is why American Sentinel University’s MBA program has a significant focus on how to properly and effectively use data in decision making and business operations.