what we use the data for?
Have you ever had to decide which ice cream to order in your kilo?
Or which toy to buy with the money your parents gave you?
Or maybe you had to choose between taking the subway or the bus to get to your destination.
In all these cases, you will have reached a decision after at least thinking for a milisecond.
And at that moment you were performing what we call data analysis.
Whether it was thinking about who likes what flavor best based on what they ordered previously or by an explicit request, or weighing which type of toy would last longer or which was more eye-catching based on your own metrics, or comparing the times you took on each mode of transportation beforehand.
All of these actions are part of life and are the way all humans do data analysis. This entails capturing all the information presented to us, choosing which is relevant to the problem at hand, cleaning that information according to current parameters or circumstances, analyzing this information, and reaching a conclusion that provides us with as much satisfaction or benefit as possible.
This is all well and good, and we all do it on a day-to-day basis, but how do we translate it to our business in a way that brings us benefits? And where do we start?
First, it is important to define what is the problem to be solved or what we want to improve. A good delimitation of the parameters is key to avoid superfluous, irrelevant or even erroneous data. It is not the same to say “I want to know why my business is doing badly” as to say “I want to know in which areas sales are lower than expected and why”. The former implies that the causes can range from the cost of cleaning the premises to the owner’s profit margin, while the latter indicates that the solution to the problem is sought within the sales of the business.
Second, it is important to ask the right questions to get the maximum benefits. For example, if you have an ice cream shop and you want to know which tastes sell the most annually, it is a very simple question that could possibly bring benefits when analyzed, but it would be much better if the inquiry were more specific. For example, which tastes have a very seasonally variable demand during the year. This question would make it possible to stop ordering tastes that are not in demand in winter, and to have a more stable stock of tastes whose demand is rather constant.
And finally, it should never be forgotten that the data we have are almost always incomplete or does not help us to solve the problem to be solved. Therefore, a good job of data cleaning and sorting is vital in order to obtain conclusions that will entail benefits.
All this process seems tedious and long, but once a continuous improvement of the business is implemented using data analysis, the results will soon be reflected in its metrics.
At Aulasneo, we use our own data analysis framework, which allows us to extract, clean, sort and analyze all the information that your course can give, using it to continuously improve the way you teach and to keep track of all the parameters you want, presenting them in a didactic and simple to understand way, so that anyone can draw conclusions from said data.
If all this made you think that your business may need this service, do not hesitate to contact us.
