Data Correlation, Data Causality, or Data Explanation? Part 3

If you read part 1 and part 2 already than you know we are still in this helicopter. If we take a this view above data management then we see all kinds of different methods to read data and determine the worth of data. We ask the question about the trustworthiness of data often. And there are many different ways to answer this question. How about data correlation, data causality or data explanation. When does which methods fits best?

The method that we try to clarify in this article is the method of Data Explanation.

Definition of Data Explanation

An explanation is a set of statements constructed to describe a set of facts which clarifies the causes, context, and consequences of those facts. This description may establish rules or laws, and may clarify the existing ones in relation to any objects, or phenomena examined.

In scientific research, explanation is one of several purposes for empirical research. Explanation is a way to uncover new knowledge, and to report relationships among different aspects of studied phenomena. Explanation attempts to answer the “why” and “how” questions. Explanations have varied explanatory power. The formal hypothesis is the theoretical tool used to verify explanation in empirical research. by Wikipedia

In practice

Explanation is the ability to answer the question why, how and even the question what. That means that you have all the factors in place. If we look at data explanation with the basics in data causality and data correlation we can say that we reached the absolute ultimo.

If you are able to research N equals all. This means the whole population and create the causal structures you are need for the research all the data will have a meaning. If we take the Influenza example from part 1 it would be like having the whole United States population being capable of searching for flu related items (N equals all) and let them do the search with pre-defined terms to gain causal connections. At this moment this would be too complex to do but how about the future? I am sure it will be able to combine all these sources into one and let data explanation rule.