Why Context is Important in Data Visualisation

 Most organisations make decisions based on the data, whether these are decisions related to expansion, mergers, operations, or hiring.

To make sure that the data gives us the right insights, the data should be clean, reliable, complete, and consistent, among others. But even if there are all the desirable traits, does the data give us the right insights?

But by working for different organisations, what I have realised is that even if the data is complete, reliable, complete, or consistent if the context is missing, it can mislead, which can be quite dangerous.

For example, the graph given below shows the number of days taken by different executives (A, B, C, D, and E) to resolve queries. Just by looking at the graph, it seems that A is not a good performer, while E is the best performer.

Here, the data has all the qualities (such as completeness, integrity, etc.), and yet it misleads.

One of the CXOs decides to fire A, but before that, he consults with the department head. In that meeting, the CXO gets to know the reason for the high-resolution time for A:

1. A was handling most of the queries.

2. A was handling queries that were very critical.

3. A was handling those queries that were dependent on the CXOs’ decisions, such as taking a decision to send a hard copy of a card, etc., and the decision was not being taken by the CXOs themselves, and hence the queries were open.

The reason for E’s good performance was that E had handled only two special queries so far, and she is not even a part of the team.

In this case, despite the data being good, it was misled through the dashboard. Only context could save it.

What did you think just after looking at this dashboard?

Promento Solutions LLP