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The phrase “using data to tell stories” is so commonly used nowadays that it runs the risk of becoming a cliche, if it hasn’t become one already. This episode’s guest flips this logic around - instead of using data to tell stories, he uses stories to teach data science!
Arvind Venkatadri is a faculty member at Srishti Manipal School of Art, Design and Technology. His research/teaching interests include TRIZ, Computation in R, Design using Open Source Electronics Hardware, and Complexity Science. He is part of the School of Foundation Studies at SMI.
This is a very wide ranging conversation. We talk about, among other things, The Three Musketeers, Lawrence of Arabia and Legally Blonde. We talk about how Arvind leverages all of these to teach his students data science and logic and game theory.
At a time when the field of data science is rife with “pile stirring”, where a large section of practitioners treat it as an extension of software engineering, Arvind’s approach, centred on stories and the human experience, is really refreshing. His approach also gives a pointer on how to widen the base in terms of attracting people into data science.
I must apologise for one thing - this conversation was recorded during Deepavali in November 2021, so you can occasionally hear the sound of firecrackers in the background. I really hope you can get past that and listen to Arvind’s stories.
Show Notes
00:03:00: Arvind’s journey into teaching Data Science in an art school
00:05:45: Teaching data science to art students
00:15:45: Teaching statistics through art and stories. Wassily Kandinsky
00:23:00: Teaching coding through art
00:31:00: Shapes and colours and emotions
00:44:00: Lawrence of Arabia (can’t say more here in the description!)
00:50:00: Data science and the human experience
Links:
Arvind’s homepage
Arvind on Twitter
Arvind’s course on R for artists and designers
An intro to Wassily Kandinsky's work
Data Chatter is a podcast on all things data. It is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".
The podcast is hosted by Karthik Shashidhar. He is a blogger, newspaper columnist, book author and a former data and strategy consultant. Karthik currently heads Analytics and Business Intelligence for Delhivery, one of India’s largest logistics companies.
You can follow him on twitter at @karthiks, and read his blog at noenthuda.com
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There is a conception, or misconception, that journalists are not good at maths. It is rather common to see newspaper headlines and graphics that make basic mathematical and logical errors.
On the other hand, in the last decade or so, we have seen a massive rise in “data journalism”. With more and more data being available, journalists are able to write stories exclusively based on data.
How do these two square off?
To answer this, we have Sukumar Ranganathan, editor in chief of the Hindustan Times. He was previously editor of Mint, of which he was one of the founding editors. It was while he was at Mint that he gave a big push to the then nascent field of “data journalism”, inviting writers such as HowIndiaLives, Rukmini S and myself to write data-backed pieces for Mint. He has previously worked in editorial leadership roles at The Hindu Businessline and Business Today.
Sukumar has degrees in chemical engineering, maths, and business administration, and is interested in mathematics, science and technology, the history of business, new media, and data-based political journalism. He reads and collects comic books and is an amateur birder. He tweets under the ID @HT_ed
Show Notes:
00:03:15: Are journalists really bad at maths?
00:16:30: Impact of bad data on public policy, and information theory
00:21:00: How data in journalism has changed in the last 20-25 years
00:23:00: The data journalism story
00:31:15: Judging a data story
00:45:30: Advice to budding data journalistsData Chatter is a podcast on all things data. It is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".
The podcast is hosted by Karthik Shashidhar. He is a blogger, newspaper columnist, book author and a former data and strategy consultant. Karthik currently heads Analytics and Business Intelligence for Delhivery, one of India’s largest logistics companies.
You can follow him on twitter at @karthiks, and read his blog at noenthuda.com
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There are two dominant programming languages used for data science nowadays - R and Python, each having its own set of loyal users. Both have their own strengths and weaknesses. In this episode, we look at what each langauge is good and bad at, what kind of people are more likely to use each, and how being able to program in both and switch seamlessly can indeed be a superpower.
Today’s guest is Abdul Majed Raja RS, a Data Scientist at Atlassian. Abdul Majed likes to call himself an Analytics Consultant with over a decade of experience helping organisations solve their business problems. He's also a Content Creator trying to help newcomers navigate the Data Science space easily and learn continuously. You can find him on Twitter and on Youtube at 1littlecoder.
Show Notes:
00:03:00: How Abdul got into analytics
00:05:30: MS Excel in data science
00:07:45: When to use R and when to use Python
00:17:00: What data scientists can learn from software engineers
00:24:30: Graphics and visualisations in R and Python
00:26:45: Machine learning in R and Python
00:29:15: Why the Indian market in Data Science leans towards Python
00:34:45: Working with databases
00:37:30: Building dashboards in R and Python
00:47:00: Working with R *and* Python at the same time
00:51:30: What about Excel and Julia?
LinksI don't like Notebooks - Joel Grus -
Interface between R and Python - reticulate.
Julia Silge Youtube Channel for latest Tidymodels tutorials
Advantages of Using R Notebooks For Data Analysis Instead of Jupyter Notebooks - Max Woolf
Data Chatter is a podcast on all things data. It is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".
The podcast is hosted by Karthik Shashidhar. He is a blogger, newspaper columnist, book author and a former data and strategy consultant. Karthik currently heads Analytics and Business Intelligence for Delhivery, one of India’s largest logistics companies.
You can follow him on twitter at @karthiks, and read his blog at noenthuda.com
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Over the last decade, we have seen tremendous advances in big data, data science, artificial intelligence and machine learning. Every compnay wants to be a tech-first comapny now, and wants to “do data science". Companies can probably double their valuation by just adding a “.ai" to their names. Companies that actually use artificial intelligence and machine learning maybe have an even higher premium on their valuations.
However, is Data Science worth the hype? Is AI going to take over the world? And why is data science being eaten by computer science? What happned to classical analytics, operations resarch and statistics?
This week’s guest is someone who did data science even before the phrase had b een invented.
Amaresh Tripathy is SVP and Analytics Business Leader at Genpact. Till recently he was a Partner with PWC, leading the firm’s Data & Analytics Consulting, and helped build a $500mm business. Previously, Amaresh founded and co-led the Information and Analytics Practice for Diamond Management & Technology Consultants, and also serves as Adjunct Professor of Data Science and Business Analytics at the University of North Carolina, Charlotte.
Amaresh has helped Fortune 500 companies in multiple industries (healthcare, retail & consumer, communications) to help define and implement their analytics and AI strategies and institutionalize data enabled decision making. He has led organizations to help embed analytics in their front, middle and back office functions and manage the change process.
Show Notes:
00:03:00: Definitions - data science, artificial intelligence, machine learning, etc.
00:04:15: The rise of computer science and machine learning
00:10:15: The probelm with Kaggle, and the “race for accuracy”
00:11:30: How to scale analytics without doing bad data analysis
00:18:00: How selling data science has changed over the last decade
00:23:00: The interaction between business and Data Science
00:26:30: “Creating bilinguals at scale”
00:30:30: Machine learning trying to eat data science
00:39:00: Comparing data science practices across countriesLinks:
Thomas Davenport and DJ Patil on Data Science as the “sexiest job of the 21st century” (2012 article)
Hal Varian on statistics as a “sexy job”
Data Chatter is a podcast on all things data. It is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".
The podcast is hosted by Karthik Shashidhar. He is a blogger, newspaper columnist, book author and a former data and strategy consultant. Karthik currently heads Analytics and Business Intelligence for Delhivery, one of India’s largest logistics companies.
You can follow him on twitter at @karthiks, and read his blog at noenthuda.com/blog
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In this edition of data chatter, we will talk about maps. Maps are excellent devices for telling stories. Think of the maps you see around election times that show which parties won seats where. in fact, the first ever scatter plot - Dr. John Snow’s figure of cholera cases in London, was essentially a map. Or think of the famous map of Napoleon’s invasion of Russia.
And telling stories through maps is an exercise in data science. Data overlaid on maps can help tell really powerful stories. And as we learn in today’s conversation, the process of mapping is no diferent from the process of data science.
Our guest is Raj Bhagat Palanichamy, or as he calls himself “mapper for life”. Raj works for the World Resources Insitute India, where he leads projects on urban development, water resources and transport.
In this conversation, Raj talks about his journey into mapping, how he makes his maps, and how WWE influences the way he tells his stories.
Highlights:
00:03:00: Raj's journey into the world of maps and mapmaking
00:06:15: The process of creating maps to tell stories
00:12:30: Choosing colours
00:17:00: The importance of annotations in storytelling
00:23:15: Data, digitisation and tools
00:35:45: Taking inspiration from WWE to construct "stories" with maps
00:42:13: Mapping cities versus mapping landscapes
00:44:30: Where is mapping underrated and overrated?Raj's 30 day map challenge in 2020
Data Chatter is a podcast on all things data. It is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".
The podcast is hosted by Karthik Shashidhar. He is a blogger, newspaper columnist, book author and a former data and strategy consultant. Karthik currently heads Analytics and Business Intelligence for Delhivery, one of India’s largest logistics companies.
You can follow him on twitter at @karthiks, and read his blog at noenthuda.com/blog -
The fundamental principle underlying all analytics and data science is Probability. And probability was first invented, or should I say discovered, to assess risk. So what is risk? Can we quantify and measure it? How do we handle risk in life? Is risk always bad?
Today’s guest on Data Chatter is Bala Vamsi Tatavarthy, who is co-founder and investment advisor at Aravali Asset Management, a global arbitrage fund.
Vamsi was my classmate at IIT Madras, where he studied computer science but spent most of his time gaming. He then went to IIM Ahmedabad, where he continued to game heavily and graduated with a gold medal. He now runs a hedge fund, and spends a lot of time gaming.
Moreover, he was one of the last traders to trade on behalf of Lehman Brothers, on 15th September 2008.
Risk, as you can imagine, is a vast subject, and so this is a long podcast. We talk about measuring risk, problems with too much measurement of risk, how risk can be managed, and all that. We also talk about movies, games, the differneces between poker and bridge and physics envy.
Show Notes
00:03:45: Defining Risk, and Lehman Brothers’ collapse
00:09:00: Can risk be created or destoryed? Is it conserved?
00:15:00: Risk, probability distributions and long tails
00:20:45: Uncertainty, volatility and risk
00:28:30: Hedging
00:35:00: Utility functions
00:42:30: Games and risk
00:54:00: Bridge and poker, and finite and infinite games
01:04:15: Ergodicity
01:07:30: VaR, Risk-metrics and Goodhart’s Law
01:14:30: Correlation
Links:
Finite and Infinite Games
“Risk once created cannot be destroyed”
The Wired article about Gaussian Copula, used to estimate correlations
Too Big To Fail, by Andrew Ross Sorkin
Ergodicity Economics
Data Chatter is a podcast on all things data. It is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".
The podcast is hosted by Karthik Shashidhar. He is a blogger, newspaper columnist, book author and a former data and strategy consultant. Karthik currently heads Analytics and Business Intelligence for Delhivery, one of India’s largest logistics companies.
You can follow him on twitter at @karthiks, and read his blog at noenthuda.com/blog -
When I was graduating college in the mid 2000s, the word in job descriptions that most commonly appeared alongside “data” was “analytics”. However, around 2010, the phrase “data science” (HBR link) got coined, and took over the world in the next five years. Nowadays it seems everyone wants to be a “data scientist”
However, where is the science in data science? And why are so many people with PhDs in pure science moving to data science?
To understand this better, I bring back one of the old guests of Data Chatter. Dhanya P is an aerospace engineer turned neuroscientiest turned data scientist. She is co-founder of Messy Fractals and Kabaddi Adda, and a Senior Scientist at Sapien Labs. Dhanya talks about her journey from neuroscience to data science, why a PhD is good training for data science, and what the “science” in data science is all about.
You can follow Dhanya on Twitter at d2a2d
Show Notes:
00:02:30: Dhanya’s journey from Aerospace Engineering to Neuroscience to Data Science
00:07:00: Why data science and not academia after PhD
00:11:45: Defining data science, and how she approaches a problem
00:16:00: How a PhD prepares you for a career in data science
00:20:00: Challenges in industry due to academic background
00:23:00: Learning to code
00:26:50: The challenges of working with someone else’s data, and proxies
00:37:30: Communicating results
00:42:45: Are ex-academics better at certain kind of Data Science roles?
00:46:00: “Entropy” in the brain
00:51:30: Revisiting the biomechanics of Kabaddi players, and communicating data to sportspersonsData Chatter is a podcast on all things data. It is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".
The podcast is hosted by Karthik Shashidhar. He is a blogger, newspaper columnist, book author and a former data and strategy consultant. Karthik currently heads Analytics and Business Intelligence for Delhivery, one of India’s largest logistics companies.
You can follow him on twitter at @karthiks, and read his blog at noenthuda.com/blog -
Everyone wants to do “data science”. Companies want to introduce “machine learning” in their products. Most fund raises by startups nowadays are accompanied by a statement of intent to invest in data, and data science.
Back in 2006, mathematician Clive Humby, who was working for Tesco, made the statement that “data is the new oil” (to give context, we were in the middle of a massive bull run in oil prices then). And so companies are investing in data.
However, just investing in data capture and hiring data scientists is not enough for a company to get value. It is important to structure the relationship between data and business, and how the data team is managed, in the right way for the data team to be effective.
Today’s guest is Anuj Krishna. Over the last 14 years, Anuj has worked with multiple enterprises on both, the translation side as well as the execution side of analytics. He has helped create standard processes for analytical problem solving that are in use in multiple enterprises.
Anuj was an early employee of MuSigma, and then went on to co-found TheMathCompany. In his current role, Anuj is Head of Assets at TheMathCompany, and is also responsible for operations related to TheMathCompany.
Show Notes:
00:03:00: How business and data science currrently interact
00:06:30: Translating from analytics to business
00:13:00: Structuring a data science team
00:22:00: Data science versus business intelligence
00:29:00: How can a business person get best value out of a data team?
00:32:00: Why data science projects fail
00:38:30: Evolution of the data science industry over the last decade
Links:
Anuj Krishna
TheMathCompany (LinkedIn)
Data Chatter is a podcast on all things data. It is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".
The podcast is hosted by Karthik Shashidhar. He is a blogger, newspaper columnist, book author and a former data and strategy consultant. Karthik currently heads Analytics and Business Intelligence for Delhivery, one of India’s largest logistics companies.
You can follow him on twitter at @karthiks, and read his blog at noenthuda.com/blog -
There is an ongoing debate on when children should be taught to code. There is one group of people which insists that computer programming is a lifelong skill, and is best taught early. The opposing argument is that coding is possibly a fad, and that children will learn it when they have to.
But what about data science? The field itself is less than 15 years old. Does it make sense to introduce it to children at an early age?
According to Rahul Raghavan, the guest on this episode, the answer is an overwhelming “yes”.
Rahul is a Montessori educator and founder of pep School v2, a Montessori school in Bangalore. Before getting into education, he was in the corporate world, working in impact investing (with VentureEast) and then with Amazon.
You can follow him on Twitter at @rahulrg
Show Notes:
00:02:55 - Introduction to the Montessori method
00:11:00 - How maths is taught differently in montessori?
00:21:45 - Introducing data science to chlldren
00:29:45 - Tossing coins, and playing cards
00:37:30 - Prerequisites to learning probability and data science
00:40:00 - Discrete maths and graph theory, going into random geekery
00:44:00 - Data visualisation for children
00:51:20 - How parents can introduce “data science” to childrenLinks:
Rahul on Twitter
Commentary on the viral video on “why should I learn maths?”
Florence Nightingale’s chart on causes of death in the Crimean War
Geometric intuition on how sqrt(2) + sqrt(3) is approximately equal to pi
Edward Tufte's books
Data Chatter is a podcast on all things data. It is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".
The podcast is hosted by Karthik Shashidhar. He is a blogger, newspaper columnist, book author and a former data and strategy consultant. Karthik currently heads Analytics and Business Intelligence for Delhivery, one of India’s largest logistics companies.
You can follow him on twitter at @karthiks, and read his blog at noenthuda.com/blog -
One of the first industries to extensively use advanced maths to do better was financial services. Ever since Fischer Black and Myron Scholes published their seminal paper on option pricing in 1973, Wall Street firms hired mathematicians and scientists by the droves, getting them to model asset prices in order to get an edge in the market. Even today, top hedge funds such as Renaissance, Citadel and Two Sigma prefer to hire scientists rather than finance professionals to manage their portfolios.
However, in the last decade or so, as Data Science and Artificial Intelligence have taken over the rest of the world, Wall Street has not maintained its leadership position in the use of maths to make money. How and why did this happen?
In order to understand this, we talk to Hari Balaji, co-founder of Romulus, an award winning unstructured data automation platform for Financial Services firms.
Prior to founding Romulus, Hari spent a decade in quant & data roles at Goldman Sachs across Hong Kong and Singapore. Hari is an alumnus of IIT Madras & IIM Ahmedabad.
Show Notes
00:03:15 - What is data science and what is artificial intelligence?
00:10:40 - What Hari’s company does
00:14:00 - Toolbox versus hammer-nail approaches
00:15:00 - This history of math in the financial services industry
00:28:45 - Wall Street is never a first mover but a great follower
00:33:30 - How Wall Street uses data science nowadays
00:41:00 - Why most innovations have happened at smaller firms
00:44:00 - Why the financial industry doesn’t behave like the Tech world
Romulus on Twitter
Romulus on LinkedIn
Data Chatter is a podcast on all things data. It is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".
The podcast is hosted by Karthik Shashidhar. He is a blogger, newspaper columnist, book author and a former data and strategy consultant. Karthik currently heads Analytics and Business Intelligence for Delhivery, one of India’s largest logistics companies.
You can follow him on twitter at @karthiks, and read his blog at noenthuda.com/blog -
For a lot of people, their first introduction to data and analytics happens through sport. Fans have tracked batting and bowling averages for many decades now. In the 1990s, with the coming of satellite TV in India, cricket fans had their first brush with bar graphs and line graphs, with “manhattans” and “worms” respectively.
In the last two decades, following the publication of Michael Lewis’s Moneyball, the field of sports analytics hsa exploded. A couple of months before this podcast was released, it was revealed that footballer Kevin De Bruyne had hired a sports analytics firm in order to better negotiate his contract with Manchester City. And along the way, analytics has entered smaller sports such as kabaddi and volleyball.
Today’s conversation is a double header, featuring the husband-wife duo of Arvind Sivdas and Dhanya P, who are also founders of KabaddiAdda, a Kabaddi platform. They have worked in analytics in cricket, badminton, volleyball and kabaddi, among other sports. We talk about the evolution of sports analytics, how to quantify “continuous sports”, the role of fantasy sport and several other things.
Show Notes
00:02:50 - How they got into sports analytics
00:13:00 - The popularity of “matchups” in sports nowadays
00:20:00 - How Roger Federer used analytics to transform his game
00:25:00 - Why performance analytics has limited impact in (association) football
00:30:30 - The importance of buy-in from the management, and evaluating success
00:32:45 - How Kabaddi has evolved in the last few years
00:37:15 - The parallels between Kabaddi and Basketball
00:46:00 - Analytics in Kabaddi
00:47:00 - Data collection for sports like Kabaddi
00:49:40 - Biomechanics studies in Kabaddi
00:51:30 - How to fund analytics in smaller sports?
00:55:10 - The role of betting and fantasy in developing analytics
00:59:20 - “Moneyball” - where is it being underused, where is it being overused etc.
01:01:00 - Convincing CSK that cricketers peak in their 30s
Pradeep Narwal's 8 point raid
On how CSK won IPL 2018 with "dad's army"
Data Chatter is a podcast on all things data. It is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".
The podcast is hosted by Karthik Shashidhar. He is a blogger, newspaper columnist, book author and a former data and strategy consultant. Karthik currently heads Analytics and Business Intelligence for Delhivery, one of India’s largest logistics companies.
You can follow him on twitter at @karthiks, and read his blog at noenthuda.com/blog -
In business schools in India, there is a misconception that marketing is not quantitative, and that it is for the more “creative” people. However, if you look at its history, marketing has always been a highly quantitative subject.
To know more about data and quant in marketing, we talk to Prithwiraj Mukherjee, an assistant professor of marketing at IIM Bangalore.
Prithwiraj teaches marketing management and marketing research at the MBA and doctoral levels. His MOOC titled Quantitative Marketing Research is available on EdX and Swayam. He research interests include behavioral decision making where he models biases, and digital marketing where he investigates influencer fraud and clickbait.
Prithwiraj has a PhD in marketing from ESSEC in Paris, and degrees in chemical engineering from NITK Surathkal and IISc
He can be found on twitter at @peeleraja
Show Notes:
00:03:40 - Introduction on numbers in marketing
00:08:45 - Customised direct mail coupons
00:15:30 - Why do students in business school think marketing is a “soft subject”?
00:22:00 - Conjoint analysis
00:29:00 - Moving from sample data to population data
00:31:20 - Modelling customer loyalty
00:40:00 - Why has digital marketing evolved disjoint from marketing?
00:43:45 - What Facebook knows about you that Google doesn’t
00:45:00 - How has AI / ML / Big Data changed marketing?Data Chatter is a podcast on all things data. It is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".
The podcast is hosted by Karthik Shashidhar. He is a blogger, newspaper columnist, book author and a former data and strategy consultant. Karthik currently heads Analytics and Business Intelligence for Delhivery, one of India’s largest logistics companies.
You can follow him on twitter at @karthiks, and read his blog at noenthuda.com/blog -
Analytics and Data Science have become mainstream career choices for graduating students in India nowadays. Analytics companies are nowadays among the largest recruiters at engineering colleges.
How did we get here? How did data and analytics become so big, and so mainstream in India? In order to understand this, we need to understand the full history of analytics in India, and this is a story that goes back over a hundred years.
Today’s guest is N Dayasindhu, co-founder and CEO of itihaasa Research and Digital. For the past two decades, he has been working on R&D and innovation management especially focused on IT. He is working on the evolution of business and technology focused on IT and related domains in the Indian context. In an earlier avatar, he was a consultant advising MNCs setting up high-performance R&D and IT organizations in India.
He was also a researcher in the R&D arm at Infosys and holds a couple of US patents. His research is published in Technology Forecasting and Social Change, Technovation, ACM SIGMIS, etc. He occasionally writes in The Indian Express, The Hindu,The Economic Times, The Hindu Business Line, Founding Fuel, etc. He has guest lectured in the IIMs, the Wharton School at UPenn, NUS Singapore, etc.
He has an FPM (PhD) from IIM Bangalore, M.Sc. in Physics from IIT Madras and a B.Sc. in Physics from Loyola College, Chennai.
Show Notes:
00:03:20 - PC Mahalanobis returns to India (1910s)
00:12:30 - Using analytics for engineering problems at IISc (1950s) https://ece.iisc.ac.in/index.php/about-us/history
00:23:00 - Analytics in the industry in India (1960s)
00:33:00 - Big tech coming into India (1980s)
00:35:30 - GE sets up captive in India (1990s)
00:39:45 - Analytics services startups; IT firms get into analytics (ealrly 2000s)
00:49:30 - Analytics training institutes in India (2010s)
00:52:00 - How to characterise analytics professionals in India
Links
Dayasindhu on Twitter
Dayasindhu’s interview with L^2, the alumni magazine of IIM Bangalore
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Data Chatter is a podcast on all things data. It is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".
The podcast is hosted by Karthik Shashidhar. He is a blogger, newspaper columnist, book author and a former data and strategy consultant. Karthik currently heads Analytics and Business Intelligence for Delhivery, one of India’s largest logistics companies.
You can follow him on twitter at @karthiks, and read his blog at noenthuda.com/blog -
“Business intelligence” has become a rather unfashionable term in the world of data and analytics. From generating buisness insights from intelligent use of data, it has largely devolved to become a software engineering function - to connect databases to front end tools.
However, there is far more to business intelligence than just writing queries. In this episode, Karthik talks to another BI professional - Balaji Kuppuswamy, director of BI products at Youtube. They talk about what BI really is, the skills involved in BI, where it sits in an organisation, and how it can truly add value.
Show Notes:
00:03:30 - What is the definition of Business Intelligence?
00:07:20 - BI’s marketing and branding problem
00:12:50 - The role of science in BI
00:15:20 - Interactive dashboards
00:19:00 - What’s it like being a data scientist in BI?
00:27:30 - How Balaji got into BI
00:32:00 - Using BI tools
00:36:00 - Integrating intelligence into BI tools
00:39:00 - Building up a BI team.
00:48:00 - Agile in BI
Links:
Kaiser Fung’s article on BI and data science
Avinash Kaushik on “datapukes"
Data Chatter is a podcast on all things data. It is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".
The podcast is hosted by Karthik Shashidhar. He is a blogger, newspaper columnist, book author and a former data and strategy consultant. Karthik currently heads Analytics and Business Intelligence for Delhivery, one of India’s largest logistics companies.
You can follow him on twitter at @karthiks, and read his blog at noenthuda.com/blog -
Around a decade ago, “big data” became fashionable. There were lots of jokes and memes created around “big data”. Everyone wanted to do big data.
Now, in 2021, the hype around big data may have died down, but how to roganise and store data remains an important problem for organistions to solve.
Today’s guest is Rangarajan Vasudevan, founder and CEO of TheDataTeam, which builds AI solutions for customer intelligence. We talk about the history of big data, what companies look for when they want to organise their data, technolgies and all such.
Show Notes:
00:03:00 - What is Big Data?
00:10:10 - Why do we need to store data?
00:14:00 - Principles of data architecture
00:20:00 - How does data evolve as companies evolve?
00:24:30 - Data warehouse and data lake and data marts and other jargons
00:34:00 - How to avoid silos, and whether to centralise data engineering, analytics, etc.
00:42:40 - More on Hadoop
00:50:30 - How should a startup architect its data team (no pun intended)?Ranga on Twitter
TheDataTeam
Data Chatter is a podcast on all things data. It is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".
The podcast is hosted by Karthik Shashidhar. He is a blogger, newspaper columnist, book author and a former data and strategy consultant. Karthik currently heads Analytics and Business Intelligence for Delhivery, one of India’s largest logistics companies.
You can follow him on twitter at @karthiks, and read his blog at noenthuda.com/blog -
When we read or talk about “data science”, most of the talk is around modelling - the maths behind it, the “cool” modelling techniques, what kind of CPUs or GPUs are required, and all that. What we normally talk less about is how data science interacts with business.
In this inaugural episode of Data Chatter, I talk to S Anand, co-founder and CEO of Gramener, about this so-called “interaction layer”. Our conversation is almost completely focussed on two such interfaces - Microsoft Excel, and data visualisation. We talk about various aspects of what it takes to communicate data to business, and pros and cons of different tools.
Anand is a co-founder of Gramener, a data science company. He leads a team that automates insights from data and narrates these as visual data stories. He is recognized as one of India's top 10 data scientists, and is a regular TEDx speaker.
Show Notes:
00:03:40 - On how Anand was “always a data guy”
00:12:01 - Anand’s first tryst automated infographics
00:15:30 - What visualisations work best for whom?
00:22:00 - Visual Basic and Python
00:27:15 - “Gymnastics in Excel”
00:32:00 - Creating choropleths using Excel
00:41:20 - Google Sheets
00:45:30 - Business Intelligence Tools, such as Tableau, Power BI, etc.
00:52:09 - Pie chartsLinks:
Gramener: https://gramener.com
Anand’s website: http://www.s-anand.net
Tufte’s seminal book: https://www.edwardtufte.com/tufte/books_vdqiData Chatter is a podcast on all things data. It is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".
The podcast is hosted by Karthik Shashidhar. He is a blogger, newspaper columnist, book author and a former data and strategy consultant. Karthik currently heads Analytics and Business Intelligence for Delhivery, one of India’s largest logistics companies.
You can follow him on twitter at @karthiks, and read his blog at noenthuda.com/blog -
This is the trailer of "data chatter", a new podcast on all things data.
Data Chatter is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".
The podcast is hosted by Karthik Shashidhar. He is a blogger, newspaper columnist, book author and a former data and strategy consultant. Karthik currently heads Analytics and Business Intelligence for Delhivery, one of India’s largest logistics companies.
You can follow him on twitter at @karthiks
