Monday, December 2, 2019


Digital Transformation – The Buzzword Simplified

A series of posts that I have written around Digital Transformation, I will take a deep dive into Digital Transformation – the word/feeling/term that has taken the world by a storm. I have this word being referred to at lunches, coffee catch ups, and informal meets round the street corner and not to forget in almost all formal leadership summits. Hence I felt it will be very useful for me to indulge further upon this subject for the sake of all you netizens.

In this post, I will look through the evolution of the digital economy and then follow it up with its elements, of digital transformation specific implementations,

Sunday, October 27, 2019

The World of IoT

A series of posts that I have written around Internet Of Things (IoT). This covers all relevant topics covered under the gamut of IoT. The posts are broken into 4 blogs - excerpts and links to each one are provided below.

IoT is an umbrella term for a broad range of underlying technologies and services depending upon the use cases and in turn are part of a broader technology ecosystem which includes related technologies such as AI, cloud computing, cyber security, analytics, big data, various connectivity/communication technologies, digital twin simulation, Augmented reality and virtual Reality, block chain and more.

We are going to dwell a little deeper on key definitions and approaches to the Internet of Things. The Internet of Things is a reality in business and beyond: In several industries and companies, tangible value creation by leveraging the power of IoT is happening since quite some time as ample real-life IoT examples show. However, it will still take until the next decennium(2020 and beyond) before hype, roadblocks and misunderstandings regarding the Internet of Things fade away and uncertainties and challenges in several areas are solved. Moreover, a radical new approach to security will be needed. 

We are going to take a deep dive into the growth and trends in the IoT space. The exact predictions regarding the size and evolution of the Internet of Things landscape tend to focus on the number of devices, appliances and other ‘things’ that are connected and the staggering growth of this volume of IP-enabled IoT devices, as well as the data they generate, with mind-blowing numbers for many years to come. 

We will discuss the Industries where IoTs have been successfully implemented. According to Internet of Things spending data and forecasts, published early 2017 by IDC, the 3 main industries in terms of IoT spending in 2016 were, respectively, manufacturing, transportation and utilities. Consumer Internet of Things spending ranked fourth. While globally in the period until 2020, manufacturing will remain the major industry (except in Western-Europe) there will be global changes in this top 3. Among the fastest growing industries in the period until 2020 are insurance, healthcare, retail, consumer and, as mentioned, cross-industry initiatives.




Friday, October 25, 2019

Machine Learning – The Primer

A series of posts that I have written around Machine learning. This covers all relevant topics covered under the gamut of Machine learning. The posts are broken into 5 blogs - excerpts and links to each one are provided below.

In this series of posts, I will emphasize primarily over the world of Machine learning. We’ll start with an overview of how machine learning models work and how they are used. This may feel basic if you’ve done statistical modeling or machine learning before. By giving ‘computers the ability to learn’, we mean passing the task of optimization — of weighing the variables in the available data to make accurate predictions about the future — to the algorithm.

We are going to continue to learn more about the steps in machine learning process and how it can be enhanced and made more efficient. There is a huge significance to be on top of Domain knowledge in your said industry/process. This will filter the right data set to be considered for machine learning. The core of this discussion goes around Data and its structure that exists in your organization.

We are going to continue to learn more about the tools/techniques and hardware that will be required for these processes to excel. While the majority of us are appreciating the early applications of machine learning, it continues to evolve at quite a promising pace, introducing us to more advanced algorithms like Deep Learning. Why DL is so good? It is simply great in terms of accuracy when trained with a huge amount of data. Also, it plays a significant role to fill the gap when a scenario is challenging for the human brain. So, quite logical this contributed to a whole slew of new frameworks appearing. Please see below the top 10 frameworks that are available and their intended use in the relevant industry space.

We are going to dwell a little deeper into the Algorithms that are at the core of the ML/DL space and how humans are at the helm of this impact of machines. Algorithms are becoming an integral part of our daily lives. About 80% of the viewing hours on Netflix and 35% of the retail on Amazon are due to automated recommendations by the so called AI/ML engines. Designers in companies like Facebook know how to make use of notifications and gamifications to increase user engagement and exploit & amplify various human vulnerabilities such as social approval and instant gratification. In short, we are nudged and sometimes even tricked into making choices by algorithms that need to learn. In case of the products and services we buy, the information we consume and who we mingle with online, algorithms are playing an important role in practically every aspect of it.

We will discuss the applications/organizations who have successfully implemented the AI/ML frameworks and how they are benefiting out of it. Have you ever thought how Google Maps predicts the traffic so accurately or how Amazon recommends products for you or even how self-driving cars work? If yes, then let us see the top 8 applications of Machine Learning.

AI / ML – Past, Present & Future

A series of posts that I have written on the world of Artificial Intelligence intertwined with Machine learning. This covers the past, present and future of the related subject. The posts are broken into 8 blogs - excerpts and links to each one are provided below.

The World has seen development/growth primarily driven by the Industrial revolutions. Each of these revolutions changed the way we looked at a time of economic dislocation; when old ways of production become defunct and they had to give way to far better/newer ways of production that could harness the improvement brought in by new machines. The First Industrial revolution was powered by the invention of the loom the second by the steam engine and the third by the assembly line, the fourth however will be powered by the machines that seem to think. We are HERE in the fourth one.

The impact of the new age machines that are learning and thus by utilizing AI/ML mould the world we are going to experience. In this excerpt we are going to understand the machine in itself, the raw material that constitutes it and how the world of AI/ML comes alive. New Machine: A system of intelligence that combines software, hardware data and human input: -Software that learns -Massive hardware processing power -Huge amounts of data

We are going to quickly look through the making of Data that makes an AI system successful and then dwell upon the design and delivery of Digital business models and solutions thereof in part b of this third post. As we learnt earlier, each industrial revolution has been catalyzed by a new raw material: Coal, Steel, oil or Electricity. This time around, data is the primary raw Material. Today organizations are able to know precise information on a varied amount of genres – right from how their engine is performing during a particular journey to a specific student’s performance on one of the lessons in a class. All this is possible because of the Data they have access to.

We are going dwell upon the design and delivery of Digital business models and solutions thereof. The ‘first wave’ of data creation, which began in the 1980s and involved the creation of documents and transactional data, was catalyzed by the proliferation of internet-connected desktop PCs. To this, a ‘second wave’ of data has followed — an explosion of unstructured media (emails, photos, music and videos), web data and meta-data resulting from ubiquitous, connected smartphones. Today we are entering the ‘third age’ of data, in which machine sensors deployed in industry and in the home create additional monitoring-, analytical- and meta-data.

We are going dwell upon the different ways to harness the new age machine through Automation and Instrumentation As I have pointed out repeatedly in the previous parts of this blog series on AI/ML, industry is riding on the cusp of a huge new wave of automated work that is going to fundamentally change what millions and millions of people all around the world do, Monday through Friday, 8 hour work day. The attempt at automation of existing parts of your business with the new machine provides an opportunity to change the cost structure of your firm, while at the same time increasing the velocity and quality of your operations. We need to understand what automation actually is, which part of your business are best suited to be automated, which jobs will be most impacted, the benefits you can expect and the problems to avoid.

We are going dwell upon the different ways to harness the new age machine to enhance human experience. Let us recognize that all these scenarios in one way or the other are enhancing the human experience. Today driving places is so easy when compared to following directions from a print out. With smart GPS systems, whether as an app on our smart phones or embedded as an instrument in our vehicle’s dashboard, it’s far more difficult to get lost nowadays. The GPS systems we now take for granted provide a preview of coming attractions on how the new machines are enhancing more and more of our work and personal lives.

We are going dwell upon the different ways to harness the new age machine in enhancing Market competitiveness. The loom led to excessive clothing, the steam engine to excessive travel, and the factory model led to excessive refrigerators and televisions finding their way into homes all around the world. Before the revolutions that spurred them, these products were rare luxuries. So the concept of excessiveness is really quite simple, and old – as prices go down, demand goes up. As the new machines drives the price down, markets of excessiveness will be established, driving sales up to unimagined levels. The question now becomes, will you seize the advantage with the new excessiveness that is available or fall victim to it?

We are going to dwell upon the different ways to harness the new age machine in by and large the innovation quotient that we need to invest upon. As we have seen throughout this series of posts, the innovation related to the intelligent systems and digital economy is both a catalyst for and an outcome that will allow your organization to discover opportunities that were never before visible or addressable. Innovation being the center stone, it can’t be a side project which is nice-to-have but its central to remaining relevant in the great digital build-out that we are experiencing and of course lies ahead of us. While machines will do more and more of our work, the process of innovation will allow us to discover entirely new things to do that are impossible to imagine and hard to predict but they will be at the core of what we do in the future.












People call me aggressive, people think I am intimidating, People say that I am a hard nut to crack. But I guess people young or old do like hard nuts -- Isnt It? :-)