“No data exists on its own; there has to be a story, or a theory, or a hypothesis which connects that and everything else that we know about the world or the subject matter of your story. When you say that these two states in northern India have developed, is it reflected in the GDP per capita numbers? Is it reflected in the unemployment numbers, that government data itself is collecting? Is it reflected in the other development indicators?”That is Pramit Bhattacharya - the ex-Data Editor at Mint and currently a freelance data columnist based in Chennai. He writes the 'Truth, Lies, and Statistics' column for Mint, and 'Simply Economics' column for the Hindustan Times.Data is the core raw material with which we build a story. If the quality of your sand or clay is rubbish, then the bricks, and the house that you build with it, will also be rubbish.Pramit has spent several years tracking macro data in India - from various government and institutional sources. He deeply understands the storied history of India’s statistical infrastructure as well as some of the recent troubling developments in that space. Over the years, he has written several detailed pieces arguing for what needs to be done to improve our data foundation.He has also written data-driven investigative pieces which have shed more light on key sectors of the economy.In this conversation, Pramit shares some of the techniques that we can all use when working with data:1. The importance of validating your data. - How do you know what data sources (especially from the government) to trust? - How the use of transparency - especially concerning the raw-data and collection methodology - can engender trust in data? - And how you can check the credibility of one metric by triangulating data from other related sources?2. Hypotheses vs bias: Pramit also shares the technique he uses to avoid getting swayed by his own hypotheses and biases when he's investigating a data story.3. Counter-factuals: Finally, he talks about the importance of the counterfactual - a key technique to ensure that we don’t get too influenced by alarmist headlines. By asking ourselves - 'Ok, X looks bad, but what is the counterfactual? What is the norm for a similar context?' - we can be better placed to come to an informed judgement about X.It’s a conversation filled with practical nuggets of wisdom that you can use to improve your own data stories.Unfortunately, we had to cut this conversation a bit short because of an unforeseen commitment that he had. I definitely hope to continue my conversation with Pramit sometime in the future!With that, let’s dive in.***Show notes:Pramit's columns in MintPramit's columns for Hindustan TimesPramit on Twitter and LinkedInPramit's article on reading budget dataPramit's article on the pitfalls of night lights data

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