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Storytelling is very much a learnable skill – and by thoughtful application of its tools and techniques, you can derive unending benefits at work.
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E20: Rukmini S – Understanding India through data
I’m pleased to present a podcast episode with Rukmini S. Rukmini is an eminent data journalist and author of ‘Whole Numbers and Half Truths’ a seminal book that looks at …
ChatGPT is a photocopier
🐦 3 Tweets of the week Questioning someone’s sense of certainty can be tough for their self-esteem! I’m not a pets person, but uff, this ad will bring a lump …
Of Skeumorphism and bullshit in science
🐦 3 Tweets of the week If there’s one tweet thread you read this week, make it this one about skeumorphism – a concept that we dont talk about enough, …

E20: Rukmini S – Understanding India through data
I’m pleased to present a podcast episode with Rukmini S. Rukmini is an eminent data journalist and author of ‘Whole Numbers and Half Truths’ a seminal book that looks at India through the lens of data. Quick – answer whether these questions are True or False:1. Delhi has the highest rate of crimes against women in India2. Most of India’s migration is rural to urban3. UP is safer for women than many big states as per an NCRB report4. India has a large middle class5. You are a part of that middle classIf you answered ‘True’ for any of the questions above, you need to read Rukmini’s book.I teach how to craft narratives with data and one of the things I used to take for granted was the ‘data’ part. Earlier on the podcast, when I’d interviewed Brent Dykes (author of ‘Effective Data Storytelling’), we discussed the importance of ensuring that the data part of the equation is thoroughly vetted and not taken for granted.And if you aren’t rigorous about getting the right data, you end up with narratives that may be divorced from the truth. Just like those True/False statements above.But if those statements are not true, then what is the truth?As per Rukmini’s book, the answer is, um, complicated. In a series of ten illuminating chapters, she covers a wide range of topics about India – from crime to education to income, to eating habits to how we vote, how we fall ill and how do we fall in love and get married – and deftly unveils a truer picture of our country.In this conversation, Rukmini shares her approach to researching and writing the chapters. She offers a nuanced take on India’s unique data architecture and why it’s in all our interests to safeguard and nurture it. She also elaborates on the need for better communication and storytelling of data findings, especially if they seem to contradict the audience’s perception based on lived reality. Finally, she shares her productivity approach of getting writing done during the pandemic despite being a parent to two young kids!Let’s dive in. As always, I’m sharing some lightly-edited extracts from the conversation – tagged under ‘the 3Ps’ – The Philosophical, the Practical, and the Personal: 1. The Philosophical: a. It’s not enough to just publish the data; someone also has to build the ‘story bridge’ connecting the data to the general public Rukmini and I discussed the importance of storytelling once data is published. Ravi: …in some of these other situations where the data that’s coming out (showing) the fact that real incomes are coming down, or wages have come down – what happens when, for not just the government but the public at large – the data that comes out contradicts what they perceive as their lived experience… (and they react) that, “How can you say wages are coming down? I have been paying more salary to XYZ.” So when that happens I think there is a gap that opens between lived experience and data, and someone has to fill that gap. Ideally, people with lived experience should grapple with the data and figure it out (themselves). But given the polarized times we live in, is that sometimes a role for the data provider to become a bit of a storyteller and try and bridge that gap? To say that, “I know this what you feel; I know you feel that Uber and Ola drivers are coming and there’s a gig economy, but here is how this survey actually includes that also.” So to reconcile with some of these possible arguments, do you think that the data providers themselves should take on the responsibility of bridging that gap? Rukmini: Absolutely. I think if there is this response, and if there is outrage to data by people who feel that it doesn’t accurately reflect reality then instead of reflexively blaming those people for being oblivious or choosing to not accept what reality is, I do think it reflects on a failure of those communicating the data. If the process and the methodology for collecting the data isn’t clearly explained, it becomes that much easier to bat that data away. I find that the best estimates we have on consumption expenditure which show that in 2017-18, if you spent more than 8,500 Rupees a month as an individual it puts you in the top 5% of urban India. That’s something that people often push back against, find unbelievable, or want to say that “There are so many people I know who are spending this, that; you can’t tell me I’m in the top 1% of the country”, (I think that it) comes from us not being able to properly explain how these numbers were calculated and what goes behind it. Explaining to people “Is your objection that this is being missed? Let me show you how it is actually captured in the data,” “There’s all this black money – let me tell you how consumption doesn’t mean that black money isn’t captured.” I completely believe that there’s a failure of communication rather than a refusal to accept reality on people’s parts. b. Go into the research phase with questions, not hypotheses Rukmini is careful about not having pre-conceived notions about the data when she begins her research: Ravi: Let’s start with the research process which precedes the writing one. When you are going out to try and answer a few questions in your mind like how much crime is really there? Or how much do Indians really earn? What do they spend? etc., there is this dichotomy or this tussle that I’ve always faced between having a hypothesis about the data that you want to research – which is useful, (because) if you don’t have any hypothesis then it’s just overwhelming (to figure out) what questions you want to answer and even if you get some insight, is it surprising or not? If you have a hypothesis, you can compare against it. That’s one; the
ChatGPT is a photocopier
🐦 3 Tweets of the week Questioning someone’s sense of certainty can be tough for their self-esteem! I’m not a pets person, but uff, this ad will bring a lump to your throat… especially with that lovely twist in the tail. Meetings ^ ♾️. 📄 2 Articles of the week a. R Ashwin on Pujara (Cricinfo, as told to Sidharth Monga) In his lovely tribute to Cheteshwar Pujara, the articulate Ashwin entertains with his narration of specific incidents and the distilling of Pujara’s safety-first cricketing approach into these funny stand-up-comic-like takes: We joke that Puji’s dad, Arvind, didn’t teach him the whole sport of cricket. He has taught him this: there is a round object, it is red in colour, people will hurl it at you, and you have to hit it. Hit it in a way that the ball doesn’t fall far from your feet. The other aspects of the sport he doesn’t even see as cricket…In fact, we joke that his house doesn’t have a lock combination. His dad and his wife throw a few balls at him, and he has to knock them back along the ground. Only then is he allowed in. b. Disinflation by Prof. Scott Galloway A rapid economic history of the past 3 years in Prof G’s inimitable style. Among others, this chart was eye-opening: 📖 1 long-form read of the week a. ChatGPT Is a Blurry JPEG of the Web by Ted Chiang A fascinating point of view on ChatGPT – about how it is not meant to be do original thinking, but just provide a grammatically-accurate and superficially clever regurgitation of material it picks from the web. In response to the thought that authors, writers, creators etc can use ChatGPT to build a first draft and then work on it, I loved this point of view of the writer: Your first draft isn’t an unoriginal idea expressed clearly; it’s an original idea expressed poorly, and it is accompanied by your amorphous dissatisfaction, your awareness of the distance between what it says and what you want it to say. That’s what directs you during rewriting, and that’s one of the things lacking when you start with text generated by an A.I. That’s all from this week’s edition.
Of Skeumorphism and bullshit in science
🐦 3 Tweets of the week If there’s one tweet thread you read this week, make it this one about skeumorphism – a concept that we dont talk about enough, but one that has significant impact on life. (PS: Follow the Cultural Tutor on Twitter – you cannot go wrong). A nuanced take on the Adani saga. A corollary: The purpose of life is to avoid experiencing things for which you will later experience regret. 📄 2 Articles of the week a. The spectacle of Shubman Gill by Sidharth Monga Just like Shubman Gill’s quick reflexes slow down the game for him, Sid Monga’s keen story sense slows it down for the reader b. ‘The Fleishman Effect: In a city of Rachels and Libbys, the FX show has some New York moms worried they’re the ones in trouble’ by Caitlin Moscatello At one level most of us are far removed from the world of the New York elite – but read through this evocative piece and you will find some commonalities. Extract: “I get up at 6 a.m., and I work until she wakes up, then I do breakfast and get her ready, then the nanny comes, I work all day, I relieve the nanny, and then get back on my computer and work until midnight after my daughter goes to sleep. I do that every day,” she says. “And it’s still not enough” 🎤 1 podcast episode of the week a. ‘Why There Is So Much Bullshit in Science’ on Plain English by Derek Thompson In the episode Derek makes the startling assertion that despite rising spends and publications, the quality of scientific progress has fallen. He attributes it to several factors: primarily a messed up incentive structure that prioritises paper publishing over genuine breakthroughs, high existing burden of knowledge making general research difficult, a paradox of choice in reading existing research sources and bigger team sizes leading to dis-economies of scale. That’s all from this week’s edition. Please let me know what you think of the new format.
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