We are in conversation with master data story teller: Anand,

who is the co-founder of Gramener, a data science company. He leads a team that tells visual stories from data. He is recognized as one of India’s top 10 scientists, and is a regular TEDx speaker.

Anand is a gold medallist from IIM Bangalore and an alumnus of IIT Madras, London Business School, IBM, Infosys Consulting, Lehman Brothers, and BCG.

More importantly, he has hand-transcribed every Calvin & Hobbes strip ever, is addicted to Minecraft (thanks to his daughter), and dreams of watching every film on the IMDb Top 250 (except The Shining).

He blogs at s-anand.net. His talks are at bit.ly/anandtalks.

Highlights:

• [00:00:36] When moving from London to Bangalore, my co-founders were nudging me to move to hyderabad. So I did what I ought to do, which is pull all the data from GitHub, scraped the developer dataset and looked at the number of developers in Bangalore.
• [00:02:00] Every year there's 60% more data that's generated than the previous year.But if you look at the growth of the analytics industry, that's only about 30%, which means roughly half the new data generated every year not analyzed.
• [00:04:08] but what are stories? Simple- Insights connected together in a sequence.
• [00:13:16] The best dashboard that I've seen is the non-existent one. For one of our clients, each of their sales team gets an email. They don't have to see the dashboard. They get an email that says, here are the three contracts that are going to expire the coming month.
• [00:19:47] You can construct charts out of smaller elements, just like you can construct a dashboard out of charts.
• [00:27:23] I can always define a standard of data that cannot possibly be met. Because the real world is messy.
• [00:27:40] Question is, does that number even define what I want it to mean? Let's continue the example of delivery time. What constitutes delivery time? The time at which it was left at the doorstep, or the time at which the customer picked it up?
• [00:32:13] Now if we can't stop lying to ourselves, lying to others is just so much easier.
• [00:35:00] One of the things that we find fascinating is comic based data storytelling.

Resources:

• Team of Rivals
• The Grammar of Graphics
• Comicgen
• SlideSense
• Explorable Narratives
• Urban Heat Islands of Calgary
• The Data Detective

They call themselves a process safety company but there is more to Empirisys than just safety. Listen to industry veteran Pete Sueref talk about how data analytics and behavioral science can combine to help prevent the greatest man-made disasters of modern times. Wouldn't you like to know what really caused the challenger disaster?

Highlights

• [00:03:30] human factors or organizational factors ... are the root cause of pretty much every single huge catastrophe.
• [00:05:21] there's quite a harmful, safety artifact that you see in lots of buildings... we haven't had an accident here for a hundred days, and you update her every day and 101 days...
• [00:06:34] there's a whole movement in the industry around safety one versus safety two, where safety one is looking at fault and blame and safety two ... improving and taking ownership for safety in the organization.
• [00:13:00] as well as being a data science consultancy, we also have leadership and behaviors and culture consultants.
• [00:15:25] it's probably a dirty secret, the analytics world, generally the most, dashboards don't really get used that much.
• [00:20:00] There's this virtuous triangle... You can't have good reliability and good productivity without good safety.
• [00:28:40] advice I give to everyone that wants to be a data scientist is learn SQL before you do anything else, learn SQL.
• [00:33:22] This idea of the citizen data scientists become much more prevalent.