3 of 3: Data Storytelling:Obvious to None Analysis

If you have not read the introduction to this blog series, we recommend you read it  here .

This blog is a part of 3 part blog series where we have learnt a sequence of analysis method used by radiologists and using that knowledge created a sequence of analysis method for data analysts.

In this blog post we cover how best to perform data storytelling and present your analysis when it is obvious to none.

Obvious to None Analysis

radiologist radiologist-method

Multiple abnormalities need to be assessed and brought together but answer is not clear. A radiologist would describe the abnormalities seen and the relevant things that are not clear, and conclude by giving a short list of possibilities, with one or two considered more likely, and what else is needed to get to a diagnosis.The radiologist’s goal here is to1) Analyse the findings and come up with some possible causes

2) Come up with a shorter list of probable causesHere are some ways to reduce possibilities and have a short list of probable causes

  • Reduce the number of possibilities with more information from the referrer about the patient
  • Reduce the number of possibilities by looking at previous scans if available
  • Ask for another test to get more information that will add to the information we already have then put everything together for a diagnosis.

3) Collaborate with clinicians to get to actual diagnosis.
data-analyst2 data-analyst-method

Multiple reasons to be assessed and brought together on increase of sale but the answer is not clear. A data analyst would describe the reasons seen and what areas are not clear.End up giving a short list of possibilities with one or two probabilitiesData analysts goal here is to1) Analyse the findings and come up with some possible causes.

2) Come up with a shorter list of probable causes

Here are some ways to reduce possibilities and have a short list of probable causes

  • More information from other business units. For example, if you are look at the data for the number of tasks done by the factory workers and you notice the tasks done are way below the average, you can ask for more information about resignations, systems failure, holidays etc.
  • Historical data. For example, if the tasks done fall below average every year around this period? Is it seasonal?
  • Ask for more data points required to find out what is happening. For example, you may ask data on background information on where do these workers come from, to further investigate.

3) Collaborate with business unit to get to actual finding from the probabilities.

It is important for the data analyst to keep the following points when presenting data. 

  • You are not the keeper of all the information and you may need to get more information from the business before you can come to a conclusion. And that is more than fine. Many a times you need to trigger a conversation, not provide a solution.
  • You need to be comfortable with sharing a point of view based on the best information available to you and giving the data a direction.
  • Be descriptive, share what you see and what you don’t.
  • Share possibilities and probabilities.

Today our roles are super specialised and many experts come together to get to the same goal.

In the case of a radiologist the goal is diagnosis for patient care but it is not the radiologist alone who has all the information to get to that goal. But she is responsible for helping everyone move towards that goal. For a data analyst, the goal is find the insight for better business outcomes and the data analyst alone can’t achieve that goal.

In absence of being able to explain a cause, describe what you find, narrow it to a few possible causes, ask for more information that might assist …all of which make get you towards being a trusted analyst and not just a slide presenter.

You can read part 1 of 3 of this blog series here  : Obvious to all 

You can read part 2 of 3 this blog series here  : Obvious to some 

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