Source: devopsschool.com
The good of Data Science simply consists in making rational decisions because they are supported by incontrovertible objective information.
Therefore, before giving answers, the intelligence that dominates data science is evident in the right questions, in the ability to create real hypotheses by exploring them individually with attention and objectivity.
Only after all this, we can go into verifying the answers that the data is providing us. And if they don’t convince, we have to find out why they don’t convince.
Is it only due to counterintuitive logic or some incorrect or simply not rigorous assumptions?
But then how can I demonstrate and convince that the logic used is correct, that the hypotheses are unassailable, that the validity of the data is beyond any reasonable doubt and therefore the results are reliable and usable for making important decisions?
All this background is behind our vision on data science to bridge the real and consistent gap between highly skilled technicians and business team directly risking wrong decisions causing serious consequences to the organization.
Always too often the managers who control the business do not have adequate personal expertise to create presentable and immediately understandable statistical reports. Managers therefore need to get help from technical specialists and this would be a good thing if you could get them to collaborate! But this collaboration can develop if the two teams want to learn from each other what seems not to be part of their cultural growth.
So, we have reached the core of the problem: the culture of data must be horizontally pervasive, expanding into all categories and towards all roles within an organization regardless of humanistic or legal or marketing or administrative extraction, or human resource management.
Everyone must know data representation just as everyone must know arithmetic and mathematics.
Last Updated on June 5, 2024
