The Art of Analytics: Recent Episodes
Nirmal, Ashish
Analytics is too important to be left to data scientists. Whether one is a CEO or a bus driver, shopkeeper or a nurse, one needs to know about the Art of Analytics. We spoke to a diverse bunch of people who are neck-deep in analytics to uncover lessons on the art, science, and engineering of analytics. Every one of them had a take on what was the art they saw, what was important, what wasn’t.
Follow the podcast on our website: artofanalytics.org
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
Calvin Chu Yee Ming is Managing Partner at Eden Strategy Institute, LLP. He has advised senior executives in over 20 countries, including organizations such as 3M; Bell Labs; Canon; Coca-Cola; Cummins; DBS; DHL; Disney; Fujitsu; HP; Intel; General Electric; M1; MasterCard; Medtronic; Nikkei; Nokia; Reed Elsevier; Roche; Samsung; SKF; StarHub; Standard Chartered Bank; Sumitomo; TNT; UNDP; UNESCO; UNICEF; and VISA; as well as the governments of Australia; Malaysia; New Zealand; Indonesia; Philippines; Singapore; Thailand; and the Kingdom of Saudi Arabia.
Calvin was recognized as a NetImpact Change-maker in 2014 and inducted into the International Who’s Who of Professionals in 2009. Under Calvin’s leadership, Eden Strategy Institute has been awarded as The Most Innovative Management Consultancy in APAC Insider’s Singapore Business Awards; Corporate Livewire’s Management Consultancy of the Year; Global 100’s Most Innovative Management Consultancy (Singapore); Corporate Vision’s Best Social Innovation Consultancy (Singapore); and was also the winner of the National Business Award in the Consulting category by the Singapore Business Review.
His work has appeared in Asian Banking & Finance, Asia Pacific Biotech News, BusinessWeek, The Star, the Straits Times, the Singapore Business Review, Today, and the Wall Street Journal, and he has featured at the ASEAN Smart Cities Network, ASEAN Social Entrepreneurship Forum, The Economist Social Innovation in Action, the Regional CEO & CIO Summit, Asia-Pacific CFO Summit, Business & Nature Forum, Institutional Investors APAC Summit, Singapore Business Federation, TEDx, Education Innovation, Prepaid Mobile Asia, Private Healthcare Asia, and Biomedical Business Conference.
Calvin has been a Judge, Reviewer, and Mentor at the President's Challenge Social Enterprise Award, The Grand Challenges Explorations (GCE) Program of the Bill & Melinda Gates Foundation, MIT Inclusive Innovation Challenge, MIT Emerging Technologies Innovators Under 35, the Youth Social Enterprise Programme Grant Committee, The DBS-NUS Social Venture Challenge Asia, The Grameen Creative Lab, the Lee Kuan Yew Global Business Plan Competition, Social Innovation Camp Asia, Start-up@Singapore, and The University of Chicago Booth School of Business Global New Venture Challenge. He has also served as an iAdvisor with IE Singapore, an Executive Advisor at NUS Enterprise, and on the boards of BioFourmis, Bettr Lives, Conjunct Consulting, Rotary Club, and the World Toilet Organization.
Highlights
- [00:01:00] Profit of course is important. It is critical to be able to drive any kind of outcomes that we're looking for. But at the same time, we also want to have a line of sight to say that, you know, if we are working on this piece of work, for example, in healthcare or in education or in smart cities there is a prospect of creating shared economic and societal outcomes. So that's what we call social innovation.
- [00:09:00] Digital trust is basically the confidence that we can give to stakeholders, that you can do business with each other, whether consumers or citizens or fellow businesses, in a trusted, frictionless, dependable manner.
- [00:11:00] What would be the right governance structures for a company holding data when its supply chain is parking the data in many other jurisdictions under the regimes of other governments?
- [00:14:00] A big bank and a big telecom company, both sitting on a lot of data, if they could only come together, it is magic, right?
- [00:17:00] Trust in organizations was never lower than it is right now. And this is a function of geopolitics… And that's on a global scale, right? So, there's definitely a need to do a lot more of this.
- [00:21:00] I could be Chief Marketing Officer, Chief Operations Officer, or Chief Technology Officer and I should interpret the sustainability mandate within my own job description. That is the only way that companies, these elephants, can dance, right?
- [00:26:00] We need to think of a system. How do we have a circular strategy? … So again, getting data, analysing, optimizing these things… these are very vital to help people see these linkages rather than to take a very blunt, a mental shortcut, right? If I have EVs is good, if I go vegetarian is always good… Everything has its repercussions.
- [00:30:00] We take a more humanistic approach to really understanding why are people not moving and how is it that we can actually motivate them internally to want to move.
- [00:36:00] And while it can be tempting to think of (social) concerns as if they were costs, when you can angle them and flip them and reframe them the right way, they all end up being assets for you to make altogether better strategy.
Resources
- Eden Strategy Institute: https://www.edenstrategyinstitute.com/
- Digital Trust white paper and context: https://globalfutureseries.com/digitrust/wp/download-white-paper/
- https://techwireasia.com/2021/09/heres-why-sgtech-set-up-a-digital-trust-committee/
- Edelman Trust Barometer: https://www.edelman.com/trust/2022-trust-barometer
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.
Managing today's gargantuan volumes of data has its challenges. These include silos, regulations, data, quality, Extract, Transform, Load (ETL), pipelines, storage, and so on. While organizations recognize the importance of investing in data engineering solutions and embark on ambitious data warehousing or data laking platforms, many organizations flounder in the data deluge. The problem is, engineering doesn't have all the answers. Very few organizations can claim to be in command of their data journey. In this podcast, we try to understand why. Raghuram Bhatt gives us an inside view of the challenges and what it really takes to solve them in the banking and financial services industry.
Raghuram S Bhat, Singapore
Raghuram leads Cognizant’s Banking & Financial Services Consulting practice for ASEAN countries and is based in Singapore. He has worked extensively with regional and global financial institutions across multiple markets in APAC, Europe & USA. He has advised clients on topics related to large scale business and technology transformation programs, organization design, IT & digital strategy, business process reengineering and data & analytics. He has more than 18 years of experience and a proven record in C-level client management, consulting sales & delivery, people management, thought leadership and P&L management.
The opinions expressed within this podcast are solely the author's and do not reflect the opinions and beliefs of Cognizant.
Highlights:
[00:02:53] Traditional banks have made significant investments, but very few have succeeded in diffusing and scaling artificial intelligence and analytics technologies throughout the organization.
[00:05:46] Failures are a gold mine of information, but there are no incentives doing a deep dive on the failures in most banking and financial services institutions.
[00:08:33] Several of the clients that I've worked, success has taken longer than anticipated. It happens when data and analytics is not native to your Genesis.
[00:10:38] I follow radical gradualism, which is have a vision, but take small steps towards that direction.
[00:12:38] Failure's not being dramatic. Several platforms that firms have invested tens of millions of dollars, nobody frankly uses.
[00:18:33] The data and analytics strategy is often not owned by one person.
[00:22:45] Data from the new platform does not reconcile with the old one. So using the new numbers from this platform means you having to restate some of your earlier performance numbers and financial reports.
[00:34:40] Be it the art or framing the problem, there's generally a lack of understanding about what and how data can help in running businesses.
[00:37:04] The ambit of engineering is much broader. This is a fundamental capability required to industrialize analytics.
[00:39:24] Have a data lab, but also take the output from the lab and industrialize it.
[00:40:27] The future of finance is underpinned by deep technology and data and analytics is at the core of it.
Welcome to The Art of Analytics podcast. We talk about the things that don’t get much air time. Isn’t it more important to choose the right questions, and to frame them right, than to know how to compute the answers? To know where to go to, than to get directions from maps? Because we worship success, do studiously avoid learning from failures?
About 10 years ago, a local authority in the UK developed a really useful database of the local population to identify people who were at risk of being admitted to hospital. When they got down to using it, though, people didn’t seem to be in as acute a level of need as they expected them to be. What went wrong? Douglas helped them understand: “I don't know how long it would've taken them to figure out that that was what was happening. But the model brought them to that conclusion much faster.” Health policy is fashionable in the post-pandemic world. In this episode, we feature insights about analytics from someone who has been modeling health systems since 2002.
Stora Enso, headquartered in Finland, is a manufacturer of pulp, paper, and other forest products, with a revenue of about US$ 10 billion. The oldest preserved share certificate in the world dates back to the year 1288 and is of Stora Enso. This makes it, probably, the oldest limited liability company in the world. What can a company like this have to teach us about analytics? Plenty, if you listen to Marko Yli-Pietilä. In this episode, Marko talks about using analytics for smarter manufacturing. Where do product ideas come from? How do you develop a pipeline of successful use cases? And what do failures mean? Listen for very generalisable insights from a very unique setting.
A lot of analytics is about answering the questions of who, what and when, but the question of why has not received a lot of attention. Ajith Sahasranamam has started a company just to explore that question and has created a lot of value with his approach.
The Internet of Things (IOT) has been poised to deliver an unparalleled surge of productivity growth… perhaps for too long? Analytics is foundational to IOT, and the adoption of IOT holds the promise of unleashing a new age of pervasive data. How do we make sense of the hype around IOT and what it can actually achieve? As a highly respected analyst, Phil is uniquely qualified to talk about the potential of IOT and its avatars in the Factory of the Future and in Smart Cities. With Phil’s deep experience in hands-on modeling, this episode provides two-track insight as he talks about the impact of analytics, but also his own analytical approaches.
Implementing an end-to-end AI product requires a variety of skill sets, especially if the product has to take on a physical form. IOT engineering, building the AI models, data engineering, and compute optimization are some of the technical challenges involved. At the same time, designing for success needs to take on the tougher challenges of working with the eco system and at times, even shaping it. An AI startup, Vulcan.ai has dealt with every one of these issues and can share with us the reasons why such ambitious projects might fail.
Better decisioning in the cash collection cycle would mean a lot to the CFOs of the world. Analytics led efficiency improvements would lead to substantial profitability gains. While the modelling is one part of the analytics, significant work needs to be done in translating the model insights into action. Debi Guha is a seasoned finance professional and she has packaged her extensive experience in managing the cash cycle into a service on the cloud. She is the CEO and founder of Two Dot Seven.