The Da Vinci of data

Generative AI has eclipsed technologies that make life easier through computers, such as services and processes. Third millennium solutions that serve users almost infallibly.   For those who use Windows, you know that by pressing CTRL+ALT+DEL you can see processes and services. Usually you manually close processes when the overlying program no longer responds to …

Angels and demons of the third millennium: processes, services, AI Read More »

In this blog we have been talking about the facets of artificial intelligence for years now. But the most requested use, after years of data collection, has absolutely nothing to do with the main activity of Enterprise Statistics.   If you probably follow me on instagram, and you pay attention, you will have noticed it, …

Intellectual honesty about the most requested use of artificial intelligence Read More »

There are more and more events with the theme of artificial intelligence for entrepreneurs. I’ve commented on one in the past. In this article I want to give a general overview of the use of that broad tool, which is absolutely not exhaustive. Be careful, however, as I use the term artificial intelligence mainly for …

Artificial intelligence for entrepreneurs in 2025 Read More »

Some entrepreneurs and managers think of using AI for data analysis to reduce the cost of employees. But this practice has hidden costs that arise from an exquisitely psychological phenomenon, which can go hand in hand with others.   There are various cognitive distortions or prejudices but in my opinion there is one that makes …

The hidden costs of using AI for data analysis Read More »

On LinkedIn you may notice ads for sales analysts and business analysts. Often these are non data-based companies.     Here is an example ad:   We are selecting a sales analyst figure to be included in support of Commercial Management. Duties: Carry out analysis of sales data to support the commercial management and the sales …

Does your company need a sales analyst? Read More »

Before the popularization of statistics, which dates back to the 1980s (later for data science), companies rarely had computers. As a result, they navigated by sight and made decisions mostly based on intuition and experience. Simplifying, companies were based on people, calculators and printers, not on the data.   I’ll immediately make a clarification: obviously …

How did companies make decisions before data science? Read More »

Those who are managers know that there are various ways to get an idea of the timing and cost of a project. Those who manage IT projects will be more familiar with agile methodology. I became familiar with agile methodology in a previous experience. In calls you would create a story, e.g., “the user wants …

Project management with data science: why yes and why not Read More »

There are various ways to represent a data stack, anatomically speaking. Texturally, I like docker compose, a feature of docker that allows you to attach various services together like LEGOs. Clearly it is not always convenient to have all the components of the enterprise data stack via docker. You will see the example of n8n, …

Data stack anatomy Read More »

When you have hardware limitations on the enterprise server, that is, of number of CPUs, or computational units, RAM and hard disk, you are left with only software or/and organizational choices . Kind of like when you want to cover as many stops as possible depending on the bus fleet and a reasonable waiting time. …

How to get more out of your business server Read More »

Statistical models, according to someone part of artificial intelligence, explain a phenomenon of interest using other phenomena. They try to go from the particular (row of data, observation or sample) to the general. We NEVER have all the data, so as even the inductive turkey who thought he was getting food every day, except becoming …

Artificial intelligence, artificial induction or artificial deduction? Read More »