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Digital Transformation

Sukumar Rajagopal

Sukumar 2The phrase Digital Transformation has taken the  world by storm. To us, as people working in the area of Transformation for the last 2 decades, it is a big positive. However there is a dark underbelly to this shiny new phenomenon - the driving forces of digital transformation are poorly understood resulting in many initiatives failing to hit the mark. There are several reasons for this and I would like to cover one of the key reasons.

Incorrect Understanding of Disruption

I request you, the reader, to think of a few Disruptive Innovations in your mind. Most likely you thought of Uber, iPhone, AirBnB, Tesla, Swiggy, Amazon Alexa. Unfortunately none of those are Disruptive Innovations. We use Disruption/Disruptive without clearly understanding the phenomenon of Disruption and tend to use it based on its direct meaning of the word in the dictionary.

The phrase Disruptive Innovation was introduced to the world by Professor Clayton Christensen in his landmark book The Innovator's Dilemma originally published in the year 1997.

Christensen's breakthrough insight is - the principle of serving customers well, which is at the core of all organizations eventually leads to Performance Oversupply (Christensen coined this phrase also) and consequently to Disruption. Let us look at this through an example to understand better. All of you reading this article most likely use Microsoft Office software or have used it in the past. Now if I ask you, how many features of Microsoft Office do you use on a percentage basis, most of you will say less than 10%. How did Microsoft Office land into this situation where it has orders of magnitude more features than most users need?

It is because, to Christensen's brilliant insight, Microsoft was merely serving its customers well by adding features that customers in various industry segments were asking for. But by so doing they have created Performance Oversupply. This created an opening for an equivalent set of products from Google, which is free to consumers or very inexpensive to small businesses and don't have more than the few important features that most customers needed.

Now if you are Microsoft how do you respond? If Microsoft made Office free or as inexpensive as Google, then approximately 25B$ annual revenue from Office will evaporate overnight and that will be a catastrophe. Hence we can say Microsoft Office has been disrupted by Google's equivalent apps. This is the force of Disruption - it leaves the incumbent with no legitimate neutralizing response because the business model has been disrupted.

We uncovered something very interesting through our work and research in transformation - Performance Oversupply happens rarely and consequently Disruption is equally rare. Therefore focusing our transformation initiatives on disruption limits the opportunities to nearly zero.

Performance Undersupply

Fortunately, our research uncovered some good news - that the phenomenon of Performance Undersupply, the exact opposite of Performance Oversupply, is pervasive and ubiquitous. We can fairly easily show that Performance Undersupply is present and will always be present in every system or product or service that humans use. Performance Undersupply is akin to a law of nature.

Performance Undersupply is of two major kinds. The first kind is Service/Product deficiency - missed SLAs, project deadlines, defects etc. These are very easy to spot because customers complain about them. Most organizations will take these seriously and fix them because they don't want to lose the customer's business. We call these, tip of the iceberg Performance Undersupply using the iceberg metaphor. These don't lead to transformation. Transformation opportunities lie in the bottom of the iceberg and are not easy to spot.

Let us look at an example to understand this better. Water is becoming an increasingly scarce commodity across many parts of the world and hence conserving water is becoming very important. Many of us have seen people leaving the tap open while brushing teeth, having a shave or chatting with someone unmindful of the open tap leading to several gallons of wasted water. It is extremely unlikely that you have encountered a water tap in any part of the world where it shows the amount of water consumed since you opened the tap till it is closed.

Why is it that taps don't have this important feature? Because the humble water tap was originally designed during the Roman Civilization and the designs have been with us since then with incremental improvements. Even today, the designers of water taps don't consider conserving water as a customer need that the design should address, because this need is not explicitly stated and hence bottom of the iceberg. I don't believe any one of us have raised an issue with any water supply/facilities teams to make the taps to show consumption. In other words, a Performance Undersupply has become an accepted feature of the system. Our work shows that it is these types of Performance Undersupply opportunities, that are accepted features, give rise to transformations.

To reinforce the point we are making, let us look at a digital technology example - Uber. Before Uber, we were all used to, standing in the corner of a street to hail a taxi cab. Though it didn't work that effectively especially during peak times, none of us still complained to the taxi administration or the local government about this issue. That means this bottom of the iceberg Performance Undersupply which has existed for a long time had become an accepted feature.

Uber's breakthrough innovation solved this problem and that is why it is considered a world-changing digital transformation.

In the age of Digital, customer needs have advanced far ahead of organizations' ability to address them. This leaves every organization with an almost infinite number of Performance Undersupply opportunities. However, because these are bottom of the iceberg and not easily identified, we need a new set of techniques to find them. I am happy to say that we in Tiny Magiq have developed a set of methods, and the early results from our work with customers is quite encouraging.

Sukumar Rajagopal
Sukumar has 30 years of experience in the IT services industry. He Has received lifetime recognition for his work in IT through the 2014 Computerworld Premier 100 IT Leaders program. Sukumar is rated by Huffington Post as one of the Top 100 Social CIOs in 2014 & 2013. And is a Tiny Habits® Certified Coach.


Will Artificial Intelligence will be the next game changer in Commercial Real Estate?

Shashidhar Sharma

ShashidharFrom the Harappan Civilization to the Roman Civilization, from ancient temple architecture to modern high-rise buildings, construction and real estate has been through many structural changes. However, from Rajgir (India, 1700 BC) to the city of Rome (753 BC), and the construction of first institute of learning – Takshashila (India, 300 BC), the construction methodology did not change much. Only around 300 BC, the first significant game changer in the construction industry arrived with the invention of steel.

It was almost a thousand years before the second game changer in construction industry made its presence felt. This was 'Technology of Conversion' – a unique combination of technology, methods and ways of working to convert trees into planks, soil into bricks and tiles, iron ore into structural members, etc. This also triggered a clear departure from the way buildings itself were designed and constructed. The medieval craftsmen made buildings with a set pattern of construction techniques they knew and kept hidden within their family. With the rediscovery of Vitruvius, known as the First Roman Architect, and his book 'De Architectura' (The Ten Books on Architecture) in 1414, the approach to construction changed in a profound way.

With the birth of modern science in the 17th Century, the commercial real estate upgraded once again. Experimental science began to be used comprehensively to calculate the sizes of design forms of the building, among others. The manufacture of glass, cut and gauged brick work and increased use of iron in various forms, changed the way buildings were made and looked

The first Industrial Revolution triggered another change in the construction industry, which is now collectively known and traded as Commercial Real Estate (CRE). The offices, as we know it, were beginning to be built, e.g., the East India Company building in London. The working in shifts, punch cards system arrived in the office environment and with it many other systems that are still prevalent in our present office spaces. However, it was the second Industrial Revolution in the early 20th century with two specific inventions – Elevators and Cranes – that caused a tectonic shift in the way Commercial Real Estate sector worked and behaved and became the biggest game changer in the history of construction industry. High rise buildings and skyscrapers began to be constructed and as they rapidly rose to loftier heights, prefabrication and Computer Aided Designs fueled the further growth of the industry.

The world's tallest manmade structure 'race' began in right earnest with buildings like Petronas Twin Tower (1998-2004), Taipei 101 (2004-2010) and the current record holder Burz Khalifa (828 M height), and now the construction industry is poised to break the Kilometer barrier with 'The Tower' in Dubai at a rumored height of 1300 M.

Shashi article imageHowever, in the background of all this tumultuous 'growth', there was a silent game changer. It was an invention that provided the backbone for a phenomenal growth in the sector, despite not being even remotely connected with the construction industry or commercial real estate. This was the Information Technology Platform which connected the world, various sectors and businesses and made it possible for Artificial Intelligence to come of age. As we progress further, Internet of Things (IoT), Data Analytics, Cloud Computing and Artificial Intelligence are the technologies that cater to CRE requirements. As of now, there technologies are all set to drive the CRE sector towards another upgrade, which will not only be a game changer but will be quite disruptive and change the 'Business as Usual' way of working.

We have seen over the past few decades, as the technology progressed with innovations like Wi-Fi, Bluetooth, laptops and smart phones, etc., it liberated the trapped employees in giant fabric walls of 70's, prisoned in the cubicle farms of mega-offices

With further technological progress, the computing powers of systems kept on growing and the latest game changer 'Artificial Intelligence' arrived on the horizon. In more than 50 years of AI development, it has delivered many impossible innovations like language comprehension, automation, driverless vehicles, etc. I believe, with further advancement of chip technology and quantum computing, the advent of AI will soon become impossible to ignore in CRE sector as well. To begin with, AI will help coalesce various technologies and end up providing a comprehensive platform for an 'end to end' solution for CRE – Transaction, Project and Facility Management and Construction.

Futurists believe that the professions that will suffer the effects of AI revolution most will be mundane jobs like junior level lawyers, journalists, drivers, chefs, telemarketers, customer service executives, medics, construction workers, manual laborers, etc. In the near future, AI will begin to merge the roles HR, Admin, Logistics and Facility Management into a single entity, where hand held devices and voice activated command and instructional interfaces will take over the role of junior staff. On practical side, AI Systems will go a long way in managing and analyzing large data to simplify decision-making in terms of Location, Leasing and Layout.

However, the good news is that even in the era of virtual and mobile work places, people will still need human interaction. This social aspect will continue to drive multitudes of professionals to come together to innovate, create and disrupt the 'Business as Usual' model. This productive disruption is bound to create more value which will fuel the growth of CRE needs of AI – just another Catch-22 situation, where the only way to keep themselves relevant, for commercial real estate professionals, is to learn, be agile and re-skill.

Shashidhar Sharma
Shashidhar Sharma has more than two decades of top-level management and marketing experience in the field of Real Estate, Construction and Infrastructure Development and Management, Modular Furniture Products Marketing and Sustainable Building and Design solutions.


Blockchain Technology: An Overview

Dr. Asoke K Talukder, PhD

Dr AsokeThe block chain marks a disruptive innovation of Bitcoin [1]. Though original name of the technology as proposed by bitcoin was two words “block chain”, it is now known as a single word “blockchain” technology. A blockchain is an immutable append only peer-to-peer (P2P) connected databases where a transaction is replicated on multiple databases. This set of databases is also called distributed ledgers that are consensually synchronized across network of computers spread across multiple sites, organizations, and geographies. A user of this database need not share either an affiliation or any modicum of trust for the updates to remain secure. Each block is guaranteed to come chronologically after the previous block. Once a block has been added in the chain – it is computationally impractical to modify.


There were many attempts to create electronic cash [2] earlier. They suffered from one problem or the other. Satoshi Nakamoto [1] was the first to combine many time-tested technologies to construct a fabric that can offer an end-to-end solution for a currency that can remain in force without a central control as in the case of a fiat currency. These technologies are,

(1) Anonymity: In a cash system, it is difficult if not impossible to trace the trail of the cash transaction. To replicate the same power, Bitcoin uses Elliptic Curve Digital Signature Algorithm that includes a set of private key, public key, and a signature. Bitcoin network is pseudo-anonymous where a person will have KYC (Know Your Customer) done with the authority and then create a wallet with the private key. The user (or the wallet) will be linked to the corresponding public Bitcoin address. No one will ever get to know the actual identity of the owner of this private key.

(2) Double Spend and Fault Tolerance: When cash is spent it ceases to exist in the owner's hand. In an electronic environment however it is easy to copy an object when both original and copied objects coexist. Moreover, in a decentralized network, there is another challenge of fault tolerance. Blockchain protocol handles both double spend and fault-tolerance (all-or-none transactions) through a consensus protocol called “Proof of Work” (PoW).

(3) Trustless consensus protocol: Bitcoin introduced the first distributed trustless consensus protocol. Miners in Bitcoin go through all transactions of users and validates that the amount paid by a user is owned by the user and nobody else. Miners take many bitcoin transactions, solve a cryptographic puzzle (perform the hash that contains the required zero bits), and then writes into the blockchain network. As the name suggests, these data blocks created by miners are linked and arranged into a append-only Merkle tree. "Proof of Work" is a validity condition present in the blockchain protocol that is not found in other systems.


Bitcoin is a complex set of technologies combined to solve many problems in a trustless distributed cash environment. It is a peer-to-peer network where anybody can send or receive money without an intermediary or controlling authority. Ethereum introduced the concept of Smart Contract [3], which is part of all Ethereum transactions and made Ethereum a marketplace that enables P2P transactions of any commodity with a common currency for trading. This is achieved through “Smart Contract” and “ether”. Ether is a kind of fuel that is used for operating the distributed application platform Ethereum

Dr Asoke article image copySmart contracts are written in a low-level, stack-based bytecode language called Solidity, referred to as Ethereum virtual machine (EVM) code. A smart contract is a program that runs at the ledger end. Smart contract can be compared with stored procedures and triggers in a relational database. Like a stored procedure in a relational database, smart contract defines some rules that can be enforced during an operation within Ethereum network. Blockchain being an append only database, a buggy smart contract cannot be removed – this caused many Ethereum ledgers vulnerable to security attacks.

Smart contracts contain code functions and can interact with other contracts, make decisions, store data, and send ether to others. Contracts are defined by their creators, but their execution, and by extension the services they offer, is provided by the ethereum network. They will exist and be executable as long as the whole network exists. Through the introduction of smart contract, Ethereum made blockchain a generic P2P network – unlike bitcoin which can only be used for financial transactions, Ethereum can be used for any application which can be financial or non-financial application.

ERC-20 is a technical standard used for smart contracts on the Ethereum blockchain for implementing tokens. Tokens in the Ethereum ecosystem can represent any tradable good [4]. It becomes easy for users as well as exchanges to list Ethereum tokens quickly given that the tokens follow ERC20 standard. This means that your token will be instantly compatible with the Ethereum wallet and any other client or contract that uses the same standards of interoperability.


Bitcoin innovated blockchain – a peer-to-peer decentralized electronic cash system. It combined many proven cryptographic and networking algorithms to offer its disruptive cryptocurrency protocol. Through the introduction of Smart Contract, Ethereum enhanced blockchain to transform it into a general purpose peerto-peer network that can be used for non-financial use-cases as well.


1. Satoshi Nakamoto, Bitcoin: A Peer-to-Peer Electronic Cash System, https://bitcoin.org/bitcoin.pdf

2. Cryptocurrency: https://en.wikipedia.org/wiki/Cryptocurrency

3. Ethereum White Paper: https://github.com/ethereum/wiki/wiki/White-Paper Create your own CRYPTO-CURRENCY with Ethereum, https://www.ethereum.org/token.

Dr. Asoke K Talukder, PhD
Asoke is a polymath. He is a Physicist by training, Computer Scientist by choice, and Health Scientist by passion. He did M.Sc (Biophysics) and Ph.D (Computer Science); his domain is Health Analytics at Vibrant Health Limited


Smart Cities – A “Stakeholder” management maze

Deputy Program Director and Construction Manager, Vizag Smart City Project

Lt. Col. L Shri Harsha (Retd.), PgMP, PMP


Smart Cities, designed to offer opportunities of economic activities and employment to a wide section of its residents, regardless of their level of education, skills or income levels and a decent living option to its residents, is a concept as old as the history of civilization. About 2000 years ago, a similar definition for establishing the basic structure of the country on the lines of economic activities and employment had been advocated by Kautilya. Similar instances of organizing the cities on the lines of commercial sectors in the ancient city of Pataliputra have also been recorded by historians. This phenomenon of the people of various civilizations organizing themselves on the lines of social and economic activities can also be seen in the areas stretching from Egypt to Indus valley some 4000 to 2000 years before our era.

Conceptual Experiments – Growth Centres, Satellite Towns & Smart Cities

The Indian journey of setting up Smart cities, started back in the early 1980, with the setting up of the National Commission of Urbanization (NCU) to address the necessity of a planned and integrated policy formulation for urban development in and around the existing urban areas. Prof MS Thacker, Member, Planning Commission during 1962 – 67 had emphasised the development of satellite towns and growth centres to evolve a spatial structure of human settlements which can integrate the urban and rural settlements on the one hand and is conducive to the development of local, regional and national economy on the other. Accordingly, the National Commission under the stewardship of Mr Charles Correa recommended the development of 329 new growth centres and strengthening of the existing metropolitan cities. Unfortunately, the policy makers could not implement this well thought of approach to develop the landscape of the nation and gave up the efforts without documenting any shortcomings in this regard.

Table 1 compares some salient parameters of Satellite towns/Growth Centres and the Smart Cities.

Table 1

Though as a planning concept, there is no difference in both these concepts, the major differentiator of Smart cities is the intent to leverage “Information Technology” to integrate the administration of these cities to make them efficient and enhance the quality of daily life. Unfortunately, ineffective communication has resulted in confusion amongst the stakeholders on what a Smart city is or should be like even at the highest level, with the Chief Secretary of a State asking what exactly is a smart city, which challenges the implementation of this concept. Since the focus is on the project management challenges, more on the actual concept of smart cities will follow in a different article.

Project Management Challenges of Smart Cities

In today's technologically and materially advanced environment, two of the most critical knowledge areas that a project manager in smart city environment must focus are Stakeholder and Communication management. The cross section of stakeholders with whom the project manager interacts and is expected to manage varies not in terms of their professionally diversified experiences and their intellectual levels, but also in terms of their inter and intra dynamic personal relationships equations. Similarly, the subjects that the project manager deals with in a smart city project daily varies from IT & ITES projects to hardcore mud and brick projects. The reason why I say that Stakeholder and Communication management should be the focus of project managers is because this has to be done by the PM himself and cannot be delegated, while the other knowledge areas can be delegated for managing by the experts.

Though it is desirable to have a strategy in place with the various factors mapped, the changing equations makes it almost impossible to document the whole process and strategy. This lack of written and well documented approaches makes it almost impossible to delegate the task and needs the PMs full attention. While a communication plan for the routine project management can be prepared, signed off and followed, the multiple demands from stakeholders compels the PM to improvise new processes and approaches without violating the communication plan. Some of the essential skills that one needs to acquire, hone and practice daily are:

• Reading the “unsaid”: The PM should master the art of reading between lines, deciphering body languages, tracking exchanges of raised eyebrows and frowns passing across the room, and have the courage to question these “unsaid” symbols to control cross communication which can be detrimental to the projects. This is easier said than done but can be mastered with time by studying the behaviour patterns and mind set of key stakeholders. Keeping track of all these noises while focussing on the task on hand requires concentration, discipline to stay aligned and a thorough understanding of both the internal and external environment. With the elections around the corner in 2019, the intent, words and actions of various stakeholders are contradictory and complicates the management of the projects. Managing such complicated scenarios tests the capabilities of the entire team to read the minds and intent of unspoken words of stakeholders.

• Agility of mind and mastery of subjects: The job demands that the RAM and processor speed in the upper storey of the PM should have incredible processing speed which should match the best super computers in the world. No jokes, compared to the minefield of words that one treads. The meetings and deliberations range from management issues to technical and people issues on the entire list of projects being implemented, with an equally diverse audience. While the bureaucrats will not appreciate the technical talk, the technical staff will not comprehend the management deliberations and call them junk, and the elected representatives are least bothered as long as the public are happy. So the PM and his entire team managing the projects has to monitor and regulate the flow of information that emanates from all sources – the client's representatives, the vendors and contractors, the teams working at site, and many more who feel that they are competent or entitled to offer a discourse on the project. The PM is responsible for the damage caused by anyone in the environment and is expected to clear the confusion. Dropping one set of discussions at lightning speed and picking up a new thread of conversation on a totally unrelated topic to the previous one, without losing connectivity with the previous thread is as complicated as reading and deciphering this sentence. Do you feel the urge to go back and read the last sentence once again?? Do it!!!!

• Quality of communication: English being the de-facto language of communication, and the language having its own peculiarities in terms of punctuations which can mean something unintended, poses another major challenge that the PM has to manage. With contractual implications looming large, every written communication has to be precise, grammatically correct and communicate what exactly need to be expressed. Unfortunately, with the project team members who are technically excellent, written communication is a major shortcoming in our country and this activity consumes a major portion of the time of the PM. Just as a glimpse of the extent to confusion that one can create with wrong choice and sequencing of words, this sample is one of the thousands of time consuming activities that the PM has to pay attention to – “Recently instead of compactor vehicle are you engaged 7 MT normal vehicle tipper with crane attachment & without bucker attachment for O&M operations. But without bucker attachment how to lift the bins in the part of O&M operations and till date the status of vehicle has hampered.”. Literal translation of spoken Hindi to English, but a contractual loophole for underperforming and getting away.

As a parting note, the” Influence vs Impact” matrix which is one of the basic tool for stakeholder management, which in turn guides the communication management plan, must evolve continuously to keep pace with the dynamic and continuously evolving environment. People behavioural reading and prediction skills plays a crucial role in orchestrating this ecosystem for a successful signoff.

Lt. Col. L Shri Harsha
Lt. Col. L Shri Harsha has expertise in building institutions and enhancing competency of organizations through coaching, mentoring and hands on support on the ground. He also has program management expertise in multi disciplinary programs including construction projects.


Data Science and its impact on Financial Audits

Kavita Dwivedi

KavithaData Science today has changed the way every domain works - be it marketing, banking, Telecom, Healthcare, Auto Industry, Fashion Industry, E commerce etc. When there is so much talk about being a data scientist and applying the right skills at your workplace, it's important to understand how and why data science must be blended into your work area/environment, so that you get the best results and can show an impact to your business. This article throws light on some of the key areas in Financial Planning and Management that are applying data science and embracing automation.

Financial Planning and Audits have also been using different aspects of data Science in their own areas. But it is important to understand today's needs and sharpen your data science skills and add resources who can add value to your overall organizational goals

In this article we focus on few key aspects in Finance where data science helps and enhances Financial Audits. One area where data science drives data based decision making is Financial Planning and Forecasting. Time Series Forecasting and Use of machine learning models to improve forecast accuracy is key to decision making for your annual or long term planning.

Strategic Financial Management can also be made more scientific and better managed through visualization dashboards like Qlikview and Tableau. As is said “ A picture speaks a 1000 words”, the right dashboard can help you spot the green and red signs and helps you plan and take corrective action. AThese dashboards are really helpful to communicate the right message to the leadership team. With the advent of Big Data, these dashboards are becoming smarter, faster and can be updated at lightning speed. Data visualizations being made available on hand held devices enables the top management team to make decisions on while on the move.

One of the major challenges for Finance of any company, is getting everything integrated post mergers and acquisitions. This has become much faster, simpler and cost effective due to Big data technology support. Mergers and acquisitions brings in with itself disparate sources of data, information and within a short timeframe companies need to get all information centralized for smooth operations.

Predictive analytics is also changing the way we look at Risk, Compliance and Fraud. These mathematical models helps companies to measure risk quantitatively and make scientific decisions based on model outcomes. With the coming of age of open source softwares like R, Python and lots of research in area of machine learning, companies are able to make better decisions and prevent Fraud with increased accuracy and fewer false positives. The best part of machine learning algorithms is that the more they are implemented, the better the performance over time. Collective decisioning has also become much easier with these models. And it's easy to sort and identify the ones that are easy to recover.

The other major shift that we have seen in Financial Audit is almost an end of sampling. With the big data technology and Automation you can actually run the same set of audits on the entire data. Regular evaluation and testing of IT systems has also become much easier. We can actually write algorithms that can do continuous monitoring and raise red flags as and when needed instantly thus minimizing risk.

From a customer point of view, this has not only bought operational efficiency and better customer service, it also helps access and accurately calculate customer life time value. Big Data combined with Machine learning algorithms allows us to integrate data across internal and external sources at great speed. Machine learning models built have better results due to the use of large data sets that are from disparate sources (including external) and span over several years. This also helps the companies to understand their customer better, and provide a better customer experience.

The other very important aspect that can be catastrophic and lead to huge losses for a firm is loss of reputation. Companies and CFO's across the globe today use Big Data to constantly monitor and keep a tab on social media reports. A small leak, a small incident can sometimes go viral and lead to a loss of reputation which is very difficult to rebuild. Many data mining softwares including web scrapping tools, text mining, image and document scanning, all help in keeping a close monitor on any negative signs and warnings.

CFOs and their teams need to up skill themselves with knowledge of Big Data Technology, Data science and Machine learning to build competitive advantage. This also helps in building of efficiency and saving costs. Many processes which have a defined process can actually be automated and some of them can be done through robotic process automation with higher accuracy and speed. This wave leads to adoption of Artificial Intelligence and in the next year or so we will see a very different skillset required for excelling in Financial Audit jobs.

Kavita Dwivedi
Kavita Dwivedi is an analytics leader with 12 + years of core hands on experience in Presales, Partner Management, Analytics delivery and Team management across domains. She is currently heading the data Science team for Infinite Sum Modeling. She been a speaker at numerous conferences / seminars.


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