DIGITAL TRANSFORMATION

Week 1: How Technology changes Businesses

Moore’s Law: Every 18 months your computer has twice as much power to process
information.
Butters’ Law: It states that the amount of data communicated through a single optical fiber
doubles every 9 months. Variation of this law is used in ADSL, VDSL, 3G, LTE and more
recently 5G.
Kryder’s Law: Amount of data stored per square centimetre square of a hard drive will
double every 13 months (practically every 16-17 months).
Key Takeaways:
 Exponential processing, communication & storage.
 Mind is linear, technology is exponential.
 Gap between linear and exponential developments is filled by disruptors.

What is possible through use of

technology & its integration.

Company’s current output

___________________________________________________________________________
Vertical Integration has been the right strategic answer to managing the flow of goods &
information because of two reasons:
i. High Transaction Costs. (Less transactions across the value chain meant lower costs
and faster time to market).
ii. Traditional Competitive Advantage was driven by economies of scale.
Majority of the reasons so stated are about information: accumulation, exchange and
processing. And according to the above laws, it becomes cheaper & cheaper to process, store
and communicate information in digital form. As a result, the vertically integrated value
chain became looser & started to break up. This independence and inter-operability
allowed each layer to evolve with its own success factors. Some layers consolidated &
monopolized, whereas other layers remained fragmented.
Key Takeaways:
 Shift from vertically integrated value-chain to stack based structure.
 Different success factors: scale at the bottom, innovation at the top.
 New players disrupt incumbents by attacking specific portions of stack.

Solow Computer Paradox: “You can see the computer age everywhere but in the
productivity statistics!” – Does NOT refute the impact of technology but just paints that
some of it is non-productivity oriented, some of it develops with a lag, and some of it is
covered by the increased complexities of the modern times.
Refers to the fact that IT spending increased by 20X between 1980 and 2015, however, the
global GDP barely tripled. So, is it fair to say that these tech investments did not help us
create more economic value?!
Theoretical discussion:
i. Maybe tech did have an impact just not on GDP, which is a narrow measure. – Real
Time Communication.
ii. Maybe it did have an impact on GDP, but it’ll reflect after a time lag. – For any
massive change, only small fringe of society are early adopters.
iii. Maybe it did have an impact on GDP in the short term, but it was neutralized by some
other business phenomenon. – Change in expectations over the years (1950 - Car
Safety || 2021 – Fuel Efficient, Cheap, Tech Integrated, Safe).

Week 2: The Mechanics of Disruption

Disruptions need not necessarily mean they are technological in nature; they may easily be
architectural in nature as well (using existing tech and packing it differently).
Ex: Airbnb, Zoom Car.
  High-End Disruption: Come in with entirely different set of features at the high end
of the market. (iPhone).
 Low-End Disruption: Come at a fraction of competitors cost, leverage superior
control which allow them to do creative things that resonate with the market
(Nintendo Wii/ Wikipedia).
 New Market Disruption: Redefining a whole set of industries (Smartphones
disrupting the cameras, maps and the like).
 Value-Chain Disruption: A disruption in one part of the value chain disrupting a
whole another industry altogether (Craigslist).
5 Domains of Disruption:
1. Data: Leveraging data to create value. Ex: Using cellular data to predict traffic jams.
2. Innovation: Speeding the innovation cycle by allowing companies to run real-time
experiments in a very inexpensive way. Ex: Facebook can run new features &
offerings in a way so as to learn real time.
3. Competition: Increases entry to fields. Companies which were previously partners
are now rivals. Ex: Google and Samsung.
4. Value: New ways to deliver value to customers. Ex: Uber.
5. Customers: Customers are becoming more sophisticated and less brand loyal. Ex:
You go into a book shop and then search its price on Amazon.

Old sources of Competitive Advantage are largely disappearing. Ripple effects are felt
through:
 Natural Monopolies: Hardly exist. Ex: A retailer going online may not care a lot
about its geographic offices.
 Economies of Scale: Scaling in a digital environment is costless.
 Learning Curves: Less and less pronounced as internet has given people access to a
lot of information.
 Vertical Integration: Makes it easier and easier for entities to disintegrate and
specialize in special components.

4 Underlying Factors driving Digital:

 Network Externalities: A good or service may improve as others begin to buy/ use
it. Ex: Telephone, facebook.
 Winner-takes-all Markets: Leverage network externalities to create quasi-
monopolies. Ex: Apple, Microsoft etc.
 Platform Technologies: An underlying technology having a great value across wide
number of sectors, allowing different companies to plug-in in different ways. Ex:
Cloud Computing, Mobile etc.
 Complementary Capabilities: Offering some specific capabilities to leverage these
platforms.
The Competitive Lifecycle: Similar to a product lifecycle, but viewed at an industry level
instead of a product level.

3 Phases of Technological Disruption

Era of Ferment: This refers to the idea that early on things are a bit exploratory. Innovation
focuses on different product features and different underlying technology that might
eventually become the dominant form within the industry. It's at this period we often see
small entrepreneurial firms entering in the industry, in many cases pioneering the
industry. And to the extent that profits are made, they're often made more through
differentiation and niche placement, than they are through, for example, a low-cost
structure within the industry.
Now over time what we typically see, is a dominant design start to emerge. We see, not
necessarily one technology, but in many cases of limited number of technologies emerge, that
become the dominant form factor we see within the industry. Now, as we see this occur,
innovation begins to shift from innovation and experimentation around different form
factors to things like manufacturing, processing, delivery, service. And this is where a
shakeout typically occurs, where we might be left with just a few large efficient firms. And
interestingly enough, many of the pioneering firms might actually wither away.

Technological Push vs Demand Pull Disruptions.

The Liability of Incumbency:

So, let's start with the question of why do incumbent firms often fail when faced with these
disruptions?

 There is no better position when new entrants when a new innovation or new
technology comes along. In essence, the innovation renders existing capabilities
valueless, either technologically, organizationally, or market-wise.
  They could actually be in a worse position than other entrants in the
industry. Incumbent firms might actually fail to see the value of the new
innovations, and have a difficult time adapting. This is what is sometimes referred
to as core rigidities. The idea being, what made you successful in the past, what may
have been your core capability, actually becomes a core rigidity as the industry shifts,
because you are unwilling to make the change.

 The firms might simply select not to change. Maybe there's a fundamental trade-off
between the long run competencies they need, and the short-term advantages that they
have. Maybe they're worried about cannibalizing their existing products. Take
Blockbuster, for example. Carl Icon, the Activist/Investor, argued that Blockbuster
shouldn't try to make the transformation to be an online streaming business. They had
no inherent capabilities there. And it was better for them to take their resources,
and basically milk the cash cow that they had, which was their retail stores, until they
basically went out of business, planned obsolescence.

Why Firms may succeed!

 Capital & Expertise.
 Assurance (as customers wanted big names and consider them to be more reliable).
 Complementary Resource.
 Dynamic Capability (to reinvent themselves in changing market conditions).

The Economics of Innovation:

Balance between Exploitation and Exploration.

Exploitation: Investing in R&D to incrementally improve existing products & services.
Exploration: Investing to try and advance the technology significantly and bring about
disruptions.

Who appropriates value to intellectual property:

1. Legal Protection.
2. First Mover Advantage
3. Standardization (Being the dominant design can lock you in)
4. Imitation
5. Diffusion (being a fad in a large lucrative market)
So overall when we think about these competing forces and when you time entry into a
market, we want to think about these two forces, the strength of the intellectual property
protection, and the role of complementary assets and reflect where we as a company
have those various strengths or weaknesses and that might determine how we might compete
in a digital transformation.
 Is there likely to be a single dominant
design or multiple designs?

Either one design emerges or multiple

coexist.

Is the market winner-takes-all,

duopoly or contested?

Week 3: Digital Trends Past & Future

7 Important Trends:
1. Big Data:
Not only we as individuals create data but a lot of it is also created from the value chain
of any industry. Big Data is defined by 3 Vs: Volume, Variety and Velocity. Data is
not only collected in structured interactions (like filling out an online form/ use your
credit card) but a lot of it is in unstructured difficult-to-mine formats (images, speeches in
different languages or videos).
Just because a company has access to large data dumps does not mean it is a big data
business. It needs to translate that data into competitive advantage to create value and
business impact from it.
Ex: Netflix uses Big Data as a competitive advantage by collecting enormous amounts of
data, analyzing customer watching habits to generate personalized recommendations &
offerings.
Use Cases:
 Personalization of Offerings (Netflix, Amazon, Supermarkets offering
discounts).
 Fraud Detection (Credit card companies like Visa analyze billions of transactions
and reduce fraud real-time).
 Predictive Maintenance (collect data about operations to predict performance
issues before they actually happen).
 Data misuse can cause a customer to reduce their spending with a company by one-third. If
you want customer data to be your competitive advantage, trust is the one thing you
wouldn’t want to lose.
2. Shift to the Cloud:
Why Shift?
The exponential growth of processing power and communication speed made everything
faster, your computer, the supercomputer and connectivity between the two.
 But investments in advanced processing power tend to flow more in the
supercomputer than your personal computer.
 Communication speed almost doubled its growth rate in the 21st Century, however
the growth of processing power stayed almost the same.

For managing a specific business process (like

payroll management, legal filings etc) it is
possible to outsource the same entirely in the
4 Layers of Cloud Services: form of BPaaS.

Ease of Use For managing growing customer base, a

company may have relationship management
software like Salesforce.

Ideal User: Customer.

Companies want to engage with customers on

their mobiles, they build their android/ iOS
apps and put it on the relevant platform. That
is using platform as a service.
Complexity
Ideal User: Developer.

Companies don’t necessarily need to own the

server they store upon. One can outsource the
same from a cloud service provider like AWS.

Ideal User: System Admin.

-May help in building reliable disaster recovery

mechanisms.

-Allow seamless upgrades and are device agnostic.

- May help in reducing upfront capital expenditure.

- Reduce TCO of hardware and/ or software.

- Can reduce the risk of over-investing at the start.

- Can reduce 20-50% of related IT Cost.

- Hyperscalable solutions are key.

3. Internet of Things:
IoT is a network of connected objects or things, that can build an aware,
autonomous and actionable system. A complete IoT system would have functions
of sensing, processing and action. IoT adoption is driven by specific use case
scenarios rather than by industries. Overall, 50% IoT spending is generated by just 3
industries: transportation & logistics, discrete manufacturing and utilities.

Ex: The self-driving car, is aware of its surroundings. For a data, a GPS system and
probably a few cameras. It is autonomous and analysing its position, and drawing a
map of its surroundings. It can also communicate with other cars or via a central
server, to check the traffic conditions along its route. It is actionable when it decides
from all those inputs when and where it will go next. And it can translate that into
commands for the engine, the brakes to steering its wheel.

This system as a whole, can get pretty complex with multiple components interacting
with each other. Let us try to make it simple. Imagine there are 3 different layers
interacting with each other all the time.

1. The first layer is that of connected objects.

The rada, the cameras, the intrusion detectors,
but also your utility meter, your watch, all
those devices need to interact with a central
platform that can consolidate the data and
decide what to do with it.
2. That's the second layer – The Central
Platform.
3. On the other side, as a third layer, your few
services that can understand the data
aggregated by the platform and translate it
into action. Between those layers, data is
constantly exchanged using networks, which
can be anything from Wi-Fi to satellite, or
fixed to mobile operator lines. So, we have
some processing, some transmission, and some
storage.

The more modular the solution is, the greater likelihood that it is durable as well.

Will we see the days of full interoperability between devices, services and
platforms?

4. Additive Manufacturing (3-D Printing):

 In traditional manufacturing, also called subtractive manufacturing, shapes are cut out
of blocks of material. Additive manufacturing is basically the opposite. Usually, the
process of three key phases.

a) A digital representation of the object is spelled, either by building it into the design
software, or by scanning a real-life object.
b) The digital model is sliced into multiple layers of less than 100 micrometres each.
c) And finally, the sliced model is sent to a 3D printer that is creating a three-
dimensional object through successive adding of layers of material.

That material can be plastics, ceramics or even metals. New 3D printers can now print
parts in minutes instead of hours. Also, AM does not require a lot of customer set-up
costs, can be more precise and generate less waste.

Use Cases: Fused Deposition Modelling (FDM), Stereolithography (SLA) and Selective
Laser Centering (SLS).

5. Cyber Security: Primarily driven by financial gain, hence it makes sense that
financial institutions and public sector are generally the targets of most of these
attacks.

 Ransomware:
It locks access to the files on any infected computer and asked the user to pay a
certain amount of money to get the unlocking keys.
Ex: WannaCry Ransomware, 2017 locked the NHS Servers in UK.

 Phishing:
This is a fake email, text message, or website created to look like they're from a
legitimate source. Some have the purpose of acquiring information, so they will ask
you to enter your credit card details or confirm your password. Others will install
malware on your computer once you open an attachment. And there are some that
pretend to be emails from your boss or your friend asking you to transfer a certain
amount of money. This is a very easy to build attack.

 Malware:
It's a software downloaded either from a phishing e-mail or by clicking on a link from
an advertising website or even directly from a USB stick. Such software is designed
to get access in an unauthorized way to your system. It can alter, delete or steal
information from your device. It can also potentially spread to other users on your
home or company network. There are multiple subcategories under this wide umbrella
of malware: viruses, spyware or ransomware like the WannaCry example.
 Distributed Denial of Service (DDOS):
In this attack, a server is targeted by an overwhelming number of requests with
the goal of ultimately shutting it down. This would cause any website hosted on that
server or any system relying on it to become non-operational. This is a relatively more
sophisticated attack because it usually involves the hacker having access to many
 infected devices then directing them to send the request simultaneously to a single
targeted server. I only said relatively more sophisticated because if you remember our
discussion in the IOT video, the number of connected cameras, meters, cars is
increasing and many of them are not protected well enough.
 Physical Breach

6. Artificial Intelligence:
AI is a system that is able to exhibit traits of human intelligence like
reasoning, learning from experience or interacting with humans in natural
language. This definition feels intuitive, but it is a bit ambiguous. Mainly because,
what we can consider intelligent, changes over time.

To make it slightly more objective, we can distinguish two types of AI: General AI
and narrow AI.
o General AI is typically what you see in the movies, a complete system that is
indistinguishable from a human. It knows or can learn anything humans can
learn, has emotions, even has a purpose in life.
o Narrow AI is less ambitious. It is when a system exhibits human-like
intelligence traits on a specific field or task. We have become very good at
building narrow AI because of sufficient processing power, lots of available
data and the right set of techniques and algorithms.

AI maybe decomposed into 3 fundamental elements: computing power, learning

algorithms and training data. AI algorithms are also susceptible to making biases on
the basis of input data.
Ex: An AI algorithm may see past recruitment data and learn that it is acceptable to
dole out lesser offers to female candidates. Some issues maybe controlled with better
understanding and control of AI algorithms; however, some may require policy &
regulatory changes.

7. Blockchain:
Blockchain YouTube Link
Blockchain is a distributed ledger technology that revolves around an encoded and
distributed (open) database serving as a ledger where records regarding
transactions are stored. Blockchains are tested and deployed across several use cases
of digital trust and exchanges in all industries, far beyond cryptocurrencies. They are
the contractual backbone of a decentralized digital age.

o What does open mean?

It means that anyone can have access to it and either read from it or write
new transactions into it. This fact makes it a bit more problematic to
maintain trust in the ledger contents. So, in the ledger, you need to have a
protocol which accepts only the transactions that make sense.
Ex: You can only transfer the ownership of a house if you yourself happen to
own it.
 To verify this transfer, every record or transaction that is written in the
ledger includes a digital signature that uniquely identifies who has written
it. The combination of few transactions and their signatures is what we call a
block. If the transactions within the block are allowed by the ledger
protocol, the block is then signed by a unique key that validates the records.
When the next transaction is created, a reference to the first block is included
at the start of the second black to guarantee that the sequence of transactions is
respected. Then, the same validation process starts again and again. Through
this process, dependencies between the records are created. Like in a
chain, that's why we call it blockchain. If at any point in time someone
wants to change the content of a block, he needs to change its key. Then
change the keys in all the following blocks.

If you make it hard enough for a key to be generated for a block using
cryptography, then tampering with the ledger becomes very difficult, even
impossible.

o Why make it distributed?

First of all, the reason you want to make it distributed is precisely to get rid of
the intermediary authority. If everyone has a copy of the ledger, then you
don't need the intermediary to store it.

In a distributed ledger, whenever someone wants to add a record, he or she

would need to announce it to the full network. All copies of the ledger are
then updated accordingly. In practice, though, it is not that simple. To
validate a transaction, you make the network members compete to solve a
difficult random mathematical problem requiring a lot of computation. For
each block, the winner of the competition validates the transactions, signs the
block, and adds it to the chain. In this construct, if a single person wants to
insert fraudulent transactions in the block chain, he needs to have more
computing power than the rest of the network combined. This is how
blockchain as a distributed open ledger got a lot of traction. Not simply
because it stores information, but because it creates trust without the
need of any third-party intermediation.

Blockchain currently suffers from a capacity limitation, in context of

number of transactions it can process per second.

3 Key Use Cases of Blockchain: Identity, Asset Tracking and Smart Contract.

Week 4: Strategy driven by Digital

Predictability means that, by having enough understanding of the different market

variables, you can know with certainty how the market will evolve in the future. This is why
planning makes sense. The lack of malleability, on the other hand, means that you can only
influence your own business trajectory. Not other players. Definitely not your competitors
against whom you are racing. Although market satisfying those two conditions are
becoming rare, they do exist.
 Traditional players like MARS have emphasized
planning & efficiency amidst a predictable &
stable environment.

Long-term planning does not work and

strategists need to take a more adaptive
approach. An approach with constant goal
refinements and much shorter iteration cycles
based on experimentation. Basically, it is a
“Trial & Error” approach.

Markets at early stage of their maturity, where

existing value propositions are weak. Little
competition & limited regulation. A single offer
can change the status quo and create a bold
vision at the right moment. Create a more
predictable future. This strategy takes efforts,
resources and a lot of persistence.
A single company won’t be able to control all
environment and stir it predictively. Herein, a
company works with an ecosystem of
suppliers, customers, policymakers to make a
vision.
Renewal Strategy is needed in harsh environments. When a company suffers from severe
underperformance in growth, margin, or free cash flow in a way that even threatens its survival. This
is typically what incumbents will go through an additional disruption. This is Nokia versus Apple, few
years ago or the taxi companies versus Uber in many cities today.

Here, a renewal strategy requires the company to proceed in two steps; conserve resources to ensure
its survival, and then, choose a new approach to rejuvenate growth. This can sometimes mean
abandoning the core business and going to somewhere less crowded.

Digitization needs to fulfil a business purpose not because it is fashionable. This purpose
can be efficiency, getting to the operational excellence. The current economic environment
in many parts of the world puts more and more pressures on company to become leaner, and
digitization can help with that. However, I would argue that this must go hand in hand with
enhancing customer experience.

In fact, a study from the MIT Sloan School of Management found that companies that
increases both digital operational excellence and customer experience outperformed
industry average net margin by up to 16 percentage point. The study also shows that
enhancing customer experience alone doesn't get you to the industry average
profitability, but focusing on operational excellence doesn't make you an industry over
performer neither. It is doing the two together. However, only 25 percent of companies
managed to excel in both dimensions. It is hard, but rewarding. This is why it's critical to
have an end-to-end approach to digitization. One that isn't limited to automating
existing processes, but completely rethinking how value is delivered to the end user.
This approach is equally relevant for products and service innovation, for back-end
operations, for go to market, and even re-thinking support function. The difference is that, if
we are talking about, which are for example, the end-to-end digital journey is the internal
employee's experience during recruiting, on boarding, skills development, etc.
How can a large complex organization achieve strategic ambidexterity?

Let me walk you through three very practical ways to balance exploration and
exploitation.

 The first intuitive answer is to encourage switching. By that I mean to proactively

take a team from exploitation stage to exploration stage. Typically, a manager
would do that at the end of a product adoption cycle, and the early stages of a
substitution technology.
Ex: Corning is a great example here. Since 1960, they have been experimenting with
chemically strengthened glass, which was used for example, in cars or in the aviation
industry. But the lack of demand led to a series of failures, that in turn led to the
company hitting their lowest share price in 2002. They then revived experimentation
to see if their glass know-how could be leveraged in consumer electronics. And once
approached by Apple, Corning rapidly focused on its so-called Gorilla Glass
product, now found in more than five billion mobile devices worldwide. Switching is
very hard in practice, because it relies on getting the timing right in a very narrow
window of opportunity.

 A different way is to create separate units to serve separate objectives. This

usually leads to the formation of a small team of explorers on the fringes of the
organization with atypical profiles, more digital natives, people whose background is
different from the legacy business. Their mandate is not to deliver immediate revenue
target, but to help think about alternative sources of growth in the long run. In one of
my clients, we call them growth hackers.

 Last but not least, a company can rely on its ecosystem. Sourcing ideas externally is
a pragmatic approach for businesses that are unable to manage ambidexterity with
internal resources. In practice, this can be executed in a variety of forms, acquisitions,
partnerships, incubation, or other more informal exchanges of ideas.
Ex: Google's acquisition of Deep Mind, or BNP Paribas Open Innovation Fintech
incubator are great examples of this approach.

Traditional ways to organize for digital transformation:

Well, there are many different models but here are the three common archetypes.

 In the decentralized archetype digital activities integrated in each of the existing

business units. Each BU has its own digital team. With this model, companies can
develop BU specific digital strategies and execute them more or less independently.
 In the centralized archetype, companies create a separate digital entity, may be headed
by a Chief Digital Officer to lead the transformation. This centre of excellence defines a
priorities, steals allocation of resources and executes the digital transformation programs
in collaboration with the BUs.
 Then, there's the excubator model where the digital activities run in parallel, sometimes
in competition with other business units. In this configuration, the CEO and surest
orchestration and potential arbitragers needed between the traditional business and the
new ventures.
In reality, companies often choose to run hybrid models. Having both a central unit as
well as a separate incubation lab. Or both business unit teams and a centre of excellence for
support. Depending on the company's priorities and starting point, those structural solutions
can have put the right emphasis on digital and support the transformation agenda.

However, this doesn't fully change the legacy processes that are crippling many large
organizations today. Beyond structural change, those organisations need to adopt a whole
new way of working suited to the needs of the digital age. A way similar to how Sub Bass
and Spotify have worked to maintain the competitive edge despite strong efforts. This is what
we call agile scale.

Two Core Concepts Agile is trying to reconcile: Autonomy and Alignment.

Traditional organizations ensure alignment for a hierarchical structure, a surgical line of
command probably inherited from military strategy. But this doesn't allow much autonomy
to the troops and makes movements slow. This is not what you need when you want to
keep up with exponential growth. To allow for more autonomy, Agile at scale relies on
small execution units.

Ex: Spotify for example, calls them squads. Squads a fully autonomous multidisciplinary
teams usually 10 to 15 people. They are fully responsible to deliver a certain product.

Benefits of having Agile Teams:

 The risk of taking the organization in the wrong direction decreases quickly.
 The focus on MVP is at every stage keeps high visibility on what value every team is
creating.
 This allows for adjustments at any point in time.
 It also keeps the organization adaptive overtime. In case an unforeseen disrupt or new
opportunity arises.
 And last but not least, business impact can be realised much earlier, with early releases
and faster time to market.

Implementing Agile is a multi-year journey that requires a complete transformation of

a company’s operating model.

Design Thinking is relevant across all 4 building blocks of the BCG Framework:
Product & Service Innovation, Operations, Go-To-Market and Support Functions.