Data analysis is a set of statistical and mathematical techniques that can obtain a result from information (data).

Depending on the ‘angle’ from which the question is approached, the answer may be different.

Depending on the type of data we intend to use, we can speak of qualitative analysis (the data is text) or quantitative analysis (the data is numbers).

Depending on the purpose of the analysis, we can be talking about descriptive analysis or predictive analysis. For example, in descriptive analysis, when describing a certain event, we can ask ourselves, how many are there? How do we distribute ourselves by gender, age, etc.? In predictive analytics, can we question how we will behave in the face of a certain occurrence? Or what is the sales estimate for the next month?

The data analysis process typically follows methodological principles that can be more or less complex, depending on your goals. The PSE methodology consists of three steps:

Perceive – know in detail the raison d’être of the process that is intended to be carried out; Solve: find the most suitable analytic solution to find a result;
Execute: make the result available for the decision process.

The ultimate goal of any data analysis process is always to help improve any decision process.

By managing to value the information that any organization possesses, the resulting benefits can range from a better allocation of resources to greater profitability. It all depends on the nature and objectives of each project.

The most visible impact is in the decision-making process. Any decision based on more and better information guarantees competitive advantages.

Data and information are concepts of the same nature. The important thing here is value. And the information will have more value if it is supported by results and trends that have been extracted from the data that supports it.

No. Any organization, and of any size, benefits from the insights gained through data analytics. For example, a neighborhood store, like a large supermarket, can benefit from knowing the profile, preferences and behavior of its customers because with this information it can optimize the shopping experience of each customer.

Consider acquiring knowledge as something strategic for your organization. And associated with that set of goals. This is the first step. The next thing is to design methods and projects to achieve them.

Here it is best to remember again that a good project goes through 3 stages: Carry out, Solve and Execute. Collecting, validating and verifying the quality of the data is a task that must be carried out in the early stages of the project.

Have a strategy and at least one goal. So he knows that nothing is miraculous but work, work, work…

The investment is directly related to the project. There are “ad-hoc” projects, that is, projects that aim to solve a specific problem at that time. There are others that are “continuous”, that is, they will last over time because the knowledge began to influence the development of the organization. The important thing is that associated with the investment, project profitability indicators are defined, the investment may be large but its return may be high. Just as it can be small and not generate any value.

Yes. There must be indicators and metrics to measure the profitability of the project. They must be an integral part of the entire methodology that supports their execution.