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.

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.

Manik Bhandari was the ASEAN Leader for Analytics at EY, based in Singapore. He has been a Management Consultant with Accenture and EY and was part of the founding leadership teams. He has worked with the Singapore Government, especially through EDB to set up multiple Digital and Analytics Hubs and Innovation Centers which has contributed more than $100 million to the GDP. He also serves as the Vice President of the Business Analytics Chapter of the Singapore Computer Society and actively contributes through his business network to build local capabilities in the Singapore eco-system.

Kamal Mannar is an accomplished AI leader with experience in innovating and bringing data driven solutions to industries in the public and private sectors. As head of AI in Vulcan-AI he leads a team in developing innovative products that leverage the latest in AI, IoT and data sources like remote sensing to create breakthrough capabilities. He has led development of innovative capabilities like deep learning, reinforcement learning, computer vision and optimization. He has multiple patents and publications related to application of AI in healthcare, prognostics, health management of assets, smart grids, and precision agriculture.

[00:04:52] There's a Venn diagram that said an ideal AI person has software engineering skills, AI skills, business acumen. It's not possible to have all the skills in one person.

[00:15:07] You can take a few moonshots when you know that your cashflow is okay.

[00:16:40] We want to embed our AI algorithms on a very small footprint.

[00:27:40] You have to build your model from scratch because the requirement is not just accuracy. Is how much power is it consuming? And how long does it take to run on an edge device?

[00:28:35] The idea is you're not building the hardware for the AI, you are custom purposing the AI to the hardware.

[00:29:11] How many flops, which is floating point operations per second, is each model consuming, how much power is it consuming? And therefore, how do I optimize it?

[00:33:54] Basically we were tracking, this is our target cents per hectare.

[00:37:04] Every piece of code is always profiled, not just in terms of the accuracy, but in terms of run time, in terms of logs, for consumption and so on.

[00:41:10] ML engineers: these they're more, pure software engineers and especially because the data we handle is quite huge, it's streaming data, it's images.

[01:10:33] Companies will start differentiating their AI solutions based on, a green mark platinum or something based on the energy consumption.

Vulcan AI’s website: https://www.vulcan-ai.com/

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 a two-track insight as he talks about the impact of analytics, but also his own analytical approaches.

Phil Marshall, New Zealand

Dr. Phil Marshall is a co-founder and the Chief Research Officer of Tolaga Research, where he supports thought leadership and product development. Before co-founding Tolaga in 2009, Dr. Marshall was a Vice President at Yankee Group for nine years, leading its global service provider technology practice. In addition, Marshall has held various management and engineering roles at Verizon International, Spark NZ, and BHP NZ Steel. He has over 25 years of experience in the ICT industry and has supported projects in over 40 countries. He has a Ph.D. degree in Electrical and Electronic Engineering and is a Senior Member of the IEEE.

Highlights

[00:05:00] “I think the art in analytics is underestimated. In effect for that matter, I would say the art in mathematics is underestimated… think … how art would be treated in schools, if you treated it more like mathematics and it's almost like an artist would end up spending all day painting fences.”

[00:12:00] “We've had challenges, right? But it doesn't mean that the IOT market is a failure. Absolutely not. I see a huge role for it more broadly, if you'd think about it as the instrumentation of things.”

[00:15:00] “With predictive maintenance 99.9% of your data is irrelevant. It's just that 0.01, which is incredibly valuable when you know, something has gone wrong. And so you have a data sparsity problem, right?”

[00:23:00] “Nine times out of 10, the forecast is wrong. It's not what the number is, it's what the inputs are, what the assumptions are, how you built it…”

[00:26:00] “There's a real balance in automotive manufacturing in terms of where you place that automation, where you place the digital intelligence, and how you develop it.”

[00:31:00] “There are some aspects of manufacturing… where it just makes no sense to automate… So there's a real art of being able to understand where [advanced technologies] are most appropriately implemented and where you draw the line.”

[00:35:00] “To progress smart cities, think of the day in the life of a typical citizen and where are the areas or what are the things that are important? I think the priorities should become pretty clear when you do that.”

[00:41:00] “I underestimated the power of status quo inertia around traditional solutions, risk aversity, stakeholder priorities... it was pretty much across the board. I'd missed all of those.”

Resources

Tolaga’s website: https://www.tolaga.com/

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.

[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.

Stora Enso, headquartered in Finland, is the renewable materials company 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.

Marko Yli-Pietilä, Finland

Marko Yli-Pietilä is an experienced Digital Transformation professional. As Head of Smart Operations at Stora Enso, he works on digitalising operations to improve operational efficiency. Prior to joining Stora Enso, Marko has held international positions at Nokia and Teradata. He has also worked with renewable materials companies and their digitalisation related development programs for years as a management consultant. Marko holds a Licentiate of Technology postgraduate degree and he is also a certified vocational teacher.

Highlights:

[00:02:00] We utilize modern technologies in a sensible way to improve our operations efficiency. OEE, overall equipment efficiency is the most important KPI set we use to measure success in industrial digitalization.

[00:10:00] We have nearly 40 solutions in smart operations that are operational at least in one mill. And roughly 20 of those…we are scaling out to multiple mills even globally.

[00:15:00] We can predict 10 to 12 hours into the future how the quality variations will be.

[00:26:00] There are so many ways you can utilize modern methodologies to find a solution. There's not one or 10 or even 100, there might be like thousands of different alternative ways to come to the right solution.

[00:36:00] No matter how good a solution you have in your hands, if the end-users are not happy to use those tools, the value will be zero or even negative.

[00:38:00] One approach doesn't fit all. You need to be ready to listen, to adapt, customize at all levels: solutions, your methods… all that you need in order to take industrial digital forward.

[00:40:00] If you're not having these sorts of cases [failures], then you're doing something wrong. You're not kind of challenging yourself enough. You're not doing, let's say, crazy enough things if you always succeed.

[00:40:00] It is iterative and sometimes you take one step back, but after that you might take three steps forward.

Resources:

https://www.storaenso.com/en Stora Enso website

https://seeds.storaenso.com/ The Stora Enso Expanded Design System

https://hyperight.com/dair-awards-2021/ The DAIR awards

Better decisioning in the “Quote-to-cash” cycle means a lot to the CFOs of the world. Analytics-led efficiency improvements in this cycle have the potential to make bottom lines better. While modelling is a part of the analytics, translating model insights into action often remains a challenge. 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 talks about using analytics to get better at the abstruse problem of collecting and managing money.

Debi Guha, Singapore

Debi Guha is an Enterprise SAAS company founder building tailored analytic solutions for CFOs to navigate an ever-changing world. She transitioned from the private equity and private debt investing worlds to set up TwoDotSeven. Today, her company is providing AI/ML led solutions to help solve wicked problems in working capital including credit risk and demand forecasting for inventory management.

Highlights:

[00:04:00] If a company can successfully reduces net working capital, by about 5%, every year in a five year timeframe, without any external capital, you can potentially release enough cash to grow your sales four times and your valuation eight times.

[00:05:34] we are an analytics first software company. Predictive analytics creates focus, which are then fed into appropriate decisioning grids that are then actioned through our solution.

[00:22:00] We typically create multiple grids, for different customer segments for different geographies, because what the grid has to reflect is, what is the company's strategic intent?

[00:24:36] During COVID last year, most companies decided to extend, their DSO appetites, because they realize that companies paying abilities have got handicapped.

[00:30:31] the terms of engagement- the contract, what are the terms that they're signing on has a significant, bearing on what their payment behavior is going to be.

[00:39:56] today you have significantly better data and tools at your disposalto do a superior job.

[00:40:42] providing forecast is not good enough, but creating a framework to which this can be implemented becomes important.

[00:44:15] What, however changed, was the line. If the best customer was a seven day payer, he slipped to becoming a 15 day payer.

[00:44:31] last year during the pandemic, when we were creating grids, the cutoffs had to be moved higher.

[00:45:04] And short, predictable cash conversion cycles, have become paramount because of COVID because everybody was running short of cash.

Resources:

Two dot seven website: https://twodotseven.com/