Brief note on data maps and big data

As with all statistical results (inductive conclusions), network “maps” are commonly interpreted as facts (absolutely correct statements, true propositions).

Thus, conclusions drawn from “mapped” data are usually mistaken for scientific facts.

This tendency is probably based on the common presumption that quantitative (numerical) statements are more reliable.

In fact, quantification is no more than replacing impresion and narrative by measurement, and measurement per se is neither precise nor reliable by definition.
An important mediator and a potential source of bias: Relevance to practice, i.e. decision-making—with varying degrees of social economic cultural impact. Possible alternatives for the purpose of data collection—and mapping big data:   

-          Applicable to policy-making, i.e. “translational”—the procedure of “translation” presents additional logical/mathematical challenge, since translation, by definition, involves induction, generalization.
-          Remotely, if at all, associated with policy-making
-          Built specifically to inform policy-making

1 yorum:

  1. I agree,
    Data mapping is a comprehensive tool that enables us to analyse i.e. the Market.(determining the optimal price for a commodity in big data of goods and so forth) A tool also does not need to be precisely true in order to be useful. I think that the reliability of data mapping is supposed to be in a place that we consider as satisfaying for a certain purpose in a scale of wrong-...-true, with a room for disagreement.
    On the web, where the supply of information is huge, extremely diverse in origin, and ever-changing, the ways without mapped data is not very efficient.
    Thank you