[UMN MSBA] Business Analytics 101

In the first week of the school, MSBA program at UMN welcomed me with oceans of assignments, quizzes and cases. I could imagine the loads of these stuffs before the orientation. But it was a little bit off that I could barely handle. I still survive, thanks to God.

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Summer session includes only ten weeks. Instructors stormed in the teaching right away after the intro of course structure. And in the course of “Analytics for Competitive Advantage,” I kicked off the door of business analytics.

Let me do my best to put down some ideas shared in the class.

First of all, what is the importance of business analytics? This question is suitable for the first class. “How can I make informed decision?” It is the mission statement of analytics. Companies strive to survive in the competitive market to earn profit and maximize shareholder value. By doing analytics, talented analyst provides way and method to make informed decision.

Analytics is growing more and more important. Different from the past, when businessman proposed strategies with intuition, companies are able to plan next strategic move with the help of data. However, not every data analytics project succeeds, the instructor pointed out only one third of projects really bring the positive impact on the company operation.

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2 Bases and 4 Pillars

Two bases for business analytics is CAUSATION and PREDICTION.

Causation analytics focuses more on the attribution of diverse factors, while prediction targets at expectation, trying to find the pattern for the future business process based on reasoning.

Four pillars just more meticulously break down the problems which are usually asked when running a business.

1. Exploratory/Descriptive : It tackles the problem of “what happened?” It is the description of status quo. It seems to be the most familiar parts for me. Since I acted as the web analyst in my previous company, most of jobs was to find insight with descriptive data. I tried my best to depict the status quo for the management level, hoping we could find the best way out for the products.
2. Predictive: It answers the question of “what will happen?” We will have the course of predictive analytics in our fall semester. I wish I could know more about this domain and share more with the readers.
3. Causal: “Do inputs affect outputs?” Every strategy is basically in this logic flow. We all want to know if I raise the price, what will change in the sales. This is causal analytics. The useful tools at hand are A/B testing and experiments. For me, it is the must learned lesson. I have to be very keen on this topic when dealing the product in the near future.
4. Prescription: It mainly focuses on the problem of “how should we likely respond?” It is the method to better the circumscribed condition. And the most famous term for this analytics field is optimization. Within the limited resource and effort,we learn how we can turn the table and make the most benefits.

Stages of Solving Analytics Projects

There are four stages for achieving a successful analytics projects. Carlson school tried to plant the seed of problem solving in our mind. Although it is still very vague for me, I hope with the process of live cases, I can learn the skill within this intense year.

And I also believe that by getting familiar with the process, we can really distinguish ourselves from students of statistics, MIS or computer science. Maybe technically, we are not that skillful. But in the aspects of communication, problem solving and value creation, we may find our own way out in this competitive job market.

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Here are the four stages of analytics projects:

1. Framing: It is the process of defining and asking right questions. To see the iceberg beneath the ocean surface. Sometimes bosses or clients may ask the questions which are not so right. Defining question well can guarantee higher rate of success.
2. Planning: It is the step we plan to do “data” staff. From getting messy data, cleaning them, to analyzing them, we plan the way where we can answer our framed question and hypothesis.
3. Interpreting: We used expected value here. We manage to give some meanings about the result we get. And moreover, make recommendation.
4. Convincing: Last step is to convey our recommendation to the management level and really make changes within the organization. We usually bring our problem at first stage and map the solution to the problems.

Instead of the linear way, the stages of business analytics go circular. We need to keep defining problems. And if we fail to plan and interpret data, we step back and try again. It is always a dynamic process to go through. And I think it is the main reason why being a business analyst is a fascinating job!

I think I will never be ready for the coming MSBA week, which is so unpredictable and so intense. But I will keep checking my progress in a descriptive way, try my best to predict right, do some good causal experiments to lead better results, and finally optimize my life here with fair prescriptive analyses.