Unexpected: Five Ways Technology Will Challenge Conventions

Unexpected: Five Ways
Technology Will Challenge
Conventions
A journey into the advanced technologies and changing
business models that will radically alter the ways in which we
live and work, today and tomorrow.
DIGITAL SYSTEMS & TECHNOLOGY
Technology Vision
Cognizant Global Technology Office
December 2017

2
PREFACE
Data in the digital era is the key element that can unlock great promise to significantly
enhance our personal and professional lives. In our “Technology Vision,” we explore in five
themes the relationship between this valuable commodity and the technological advances
and evolving business models that are changing the way we live, work, and collaborate.
Consider the shape, form, and value that a single piece of data can take, or, the disruption
it can cause. Take, for example, an innocuous piece of data: your weight. Each morning you
may jump on a scale, your weight wirelessly transported to a fitness app that enables you
to monitor gains, losses or health goals.
Now imagine the transformative journey that this one piece of data can then take.
Traditional scales used to be “dumb,” and in the opinion of many, always wrong. Today’s
new digital scales are smart devices that can link to apps, fitness platforms and health
repositories. Once your weight is recorded in your favorite app, it can trigger a message
or a motivational video that encourages you to stay on track with your goals. This piece of
data is now providing value back to you.
In our first theme, Blur, we uncover that, more and more, products (the scale) are being
combined with value-added services (messages, videos, education, etc.) and use data (your
weight!) to provide greater insight and an enhanced experience. Business models that
support this new product and service paradigm are emerging and wise decision makers
need to plug and play today, or forever be left behind.
In giving up our personal data, we assume it will be used ethically and to our benefit.
However, there is also a chance it can be used against us. For example, when signing up for
a discounted life insurance policy, you may unknowingly commit to stay within a certain
weight range. Your discount may now be at risk. In Know Me, Forget Me we explore how
freely we give up our personal data, expecting that doing so will be to our advantage. But
our data may also be used without our knowledge, monetized without our approval, or even
used against us. We must become more aware of how our personal information is used,
and take control by ensuring that it will be forgotten once it no longer benefits us.
Whether the scale is right or wrong, it tells you that you have gained two pounds. Your doctor
is instantaneously alerted that you have fallen outside your agreed-upon healthy weight
range and immediately sends you an alert regarding what percentage of macronutrients
2
Digital Systems & Technology
| Unexpected: Five Ways Technology Will Challenge Conventions

*Cobots are smaller, more nimble robots designed to work in safe collaboration with humans.
3Unexpected: Five Ways Technology Will Challenge Conventions |
you should consume. You quickly change your breakfast order as you stand in line at
Starbuck’s. Excellence at the Edge is about extracting the value of data while it is freshest,
and most relevant. Data collected at the network’s edge by IoT devices or from mobile apps
can be processed, analyzed, and acted upon in real time — enabling us to work smarter,
respond faster, and be better informed in making vital decisions.
Your data and how you harness it will make you unique. As automation becomes a core
tenet of the work environment, we will have the opportunity to differentiate ourselves
from our peers, and the robots with which we will work. Automation at Work explores the
implications of human and robot collaboration in the workforce of today and tomorrow.
Companies such as Amazon have already made significant strides in collaborative
automation (humans and robots working together) to extend workplace productivity and
improve bottom-line performance. As “cobots” become commonplace, organizations must
re-wire how they manage and measure workforce performance.* Moreover, humans must
seek to define what will truly set them apart in the new collaborative work world.
Your two-pound weight gain may be part of a trend that shows that you are slowly gaining
weight. This may mean nothing more than needing to get back to the gym. Or, it could point
to an ailment or hereditary issue that you were not aware of. In Augmented Humanity, we
see how augmented technology is helping those afflicted with a serious injury, hereditary
disease, or disability to walk, see or strive to become disease-free. With these advances,
the prospect of human enhancement is very real. A digitally-augmented human could
apply technology to determine their personal growth, increase their lifespan, or eliminate
a genetic disease for future generations. The possibilities are endless.
Technology is changing quickly, so quickly that it’s hard to keep up. Our annual “Technology
Vision” is one way we bring rhyme and reason to the technological advances that are
accelerating all around us. This white paper is intended to provide food for thought to
business and technology leaders seeking a path forward. Enjoy!
Aan Chauhan
Global Chief Technology Officer
3

4 | Unexpected: Five Ways Technology Will Challenge Conventions
A watch that once told the time and date can now
monitor your heart rate and warn you if timely care is
needed. A car that once transported you from A to B
is now highly connected and semi-autonomous, with
the ability to self-diagnose when issues arise. And a
thermostat that once controlled the ambience of a
room now knows what the weather is outside and can
adjust a home’s climate accordingly.
Moore’s Law1
is based on the premise that as microprocessors dramatically increase in power, there will be an
exponential decrease in relative cost. Moore’s Law has held true for 50-plus years, and is now driving develop-
ments around the fast-proliferating Internet of Things (IoT). Devices of all kinds have become smarter, smaller,
more portable, and more connected than ever — making them less expensive to manufacture, easier to acquire,
and easier to integrate. These devices provide the volumes of data for advanced analytical engines, which allow
for the mass aggregation of data to perform sophisticated analysis.
As the IoT becomes mainstream, in parallel there has been an
accelerated shift to a more service-based culture. New ways
of delivering or consuming a product or service have given
rise to new business models. As usage- or access-based pric-
ing models emerge, we no longer need to buy a product or
service; we need only to pay for what we consume. New and
varied “as-a-service” models provide ways to acquire access to
an application, business process, or infrastructure.
4
THEME 1
The Blur

Today, the shift to smarter, more connected products combined with value-added services offers busi-
nesses and consumers a more intuitive, engaging, and valuable experience. At the same time, this new
model has made it harder to understand what the original product actually was. This loss of distinc-
tion between products and services has created what we call the Blur.
The Blur has begun to disrupt business models and change the way products and services are pro-
vided and consumed. It has fundamentally changed the way consumers acquire and engage with a
product or service, providing even greater consumer insights. It has also changed the very notion of
ownership of goods.
Technological progression has accelerated this product and service transition. Global communication
networks provide connectivity in all but the most remote locations. Cloud computing provides infra-
structure to support increased data requirements and processing, enabling greater operational agility
and adaptability. Near-ubiquitous smartphone access provides a user interface for even the smallest
devices/objects. Advanced analytics identify the value that is derived from IoT data that is collected,
which enables more personalized insights to be generated. Cutting-edge technologies, including aug-
mented reality and virtual reality, can create a new realm of value-added services. For example, virtual
or augmented reality can present a view of the new home that you would like to buy, or a vacation
that you are considering.
Blur brings challenges to organizations, industries, and consumers looking to adopt this new model.
As the lines between products and services blur, companies must increasingly focus on product and
service support. Data security will be crucial to protect your customer, or the end consumer, in order
to provide the best customer experience.
Yet there is opportunity to learn from early adopters, and apply how they have harnessed new and
advanced technology to fundamentally shift their business models.
As the lines between products and
services blur, companies must
increasingly focus on product
and service support. Data security
will be crucial to protect your
customer, or the end consumer,
in order to provide the best
customer experience.

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ON THE FOREFRONT
Beyond the Car
Tesla set out to create the first highly-stylized, mass-produced electric car. Elon Musk further outlined
Tesla’s master plan to design cars with self-driving technology that is safer than manual driving, and
implement ride sharing to allow Tesla owners to earn money while lending out their cars. From day
one, Tesla used analytics as its competitive advantage, applying more than 1.3 billion miles of car and
driving data it had collected. This data is used for continual car improvement, to further driver enjoy-
ment, and to position Tesla as first to market with autonomous driving. Today, Tesla commands a level
of brand affinity, customer satisfaction, and loyalty not seen elsewhere in the auto industry.2
As a digital leader, Tesla set out to transform the auto industry by combining its first electric car with
a heavy focus on analytics. It has continued to lead with its semi-autonomous car, advanced data
analytics, and planned entry into the “gig” economy.
Beyond the Pill
A leading pharmaceuticals company created a patient-centric ecosystem designed to remove the
obstacles patients encountered when prescribed the company’s top-selling rheumatoid arthri-
tis drug. Through surveys and discussions, the pharma learned that patients often experienced
anxiety upon receiving the prescription, since obtaining and administering the drug presented
several challenges. There was often a delay in receiving the drug; it came with a high out-of-pocket
expense; patients often feared injecting the drug; and many were concerned with its potential
adverse side-effects.
| Unexpected: Five Ways Technology Will Challenge Conventions
SERVICESPRODUCT
Going Beyond the Pill with a Value-Added Service Solution
Taking a pill or personally administering an injection has now become a service-wrapped experience
for the patient. In “Beyond the Pill,” a nurse is now made available to educate a nervous patient on
how to inject a medicine. An alert system has been set up in case a patient has an adverse reaction
to the medicine. And a process is now in place to help get the medicine in the patient’s hands quicker.
Figure 1

To respond to these challenges and to support its product, the company set out to better inform
and engage with patients. A more efficient drug-onboarding process reduced the time to obtain the
prescription (from three to four weeks to three days). Patients were provided outreach and person-
alized services that included direct training from nurses on how to take the injectable. Additionally, a
one-touch support system was made available to patients who experienced an adverse side effect or
reaction to the drug.
This leading pharmaceuticals company understood the challenges patients experienced with its prod-
uct, and responded with a “beyond the pill” approach and value-added service solution. As a result,
the company experienced a 95% increase in patient referrals and a 70% higher adoption rate among
health care providers.
OPPORTUNITIES ABOUND
Product to Service & Platform Integration
The proliferation of smart devices has enabled everyday products to become smarter. In turn, the
addition of value-added services can provide the ultimate value to businesses and consumers. As
companies seek to expand their product-to-service capabilities, they can do so organically or partner
with other innovators to develop a highly integrated, compelling offering.
Health and wellness has been transformed by the development of platforms (e.g., HealthKit3
) that
integrate any number of smart devices and consolidate an individual’s health data in one place. A
person can now use a connected toothbrush to track their oral hygiene, understand the nutritional
content of food with a sensor and smart scale (DietSensor4
), wear shoes that measure calories burned,
and attach a sensor to a pillow that monitors sleep patterns. For caffeine addicts, the smart coffee
maker can start at the optimum time for waking up to freshly brewed coffee. This data can then be
aggregated with other health indicators to provide a single view of a person’s overall wellness.
Greater Customer Insight
In this new product and value-added service world, greater insights can be gained to better under-
stand consumer behaviors, needs, and wants. We discussed this in our book, Code Halos.5
More highly
customized and personalized services can be delivered by organizations that make meaning from the
intersection of code that pervades people, processes, organizations, and things. Companies will bene-
fit from truly understanding their customers and ensuring their needs are met.
Digital leader Nest was a first mover in the smart thermostat space. It has since transformed its prod-
ucts and services and developed the “Works with Nest”6
program to provide an integrated platform
for developing a connected home strategy. Nest’s product line of smart thermostats, smart alarms,
and indoor/outdoor camcorders learn the habits and usage patterns of its users to adjust the ambi-
ence of a home, or warn residents of pending danger.
For caffeine addicts, the smart coffee maker can
start at the optimum time for waking up to freshly
brewed coffee. This data can then be aggregated
with other health indicators to provide a single
view of overall wellness.

New Business Models
The market has a long-held notion that ownership of a product is necessary to enjoy it and gain its
full value. The emergence of the “sharing economy” and “as-a-service” business models, has dispelled
the notion that individuals must acquire, own, and maintain a product over time. New models now
provide for consumers to pay only for what they consume.
In media and entertainment, access-based models are already prevalent. Rather than buying a prod-
uct, a consumer can rent a book or a video. Digital companies today are adopting this same model
across all industry segments. With Rent-the-Runway,7
a designer dress can be loaned for a special
event. It’s analogous to software as-a-service (SaaS); organizations do not buy software, they rent it.
Achieving a Longer Revenue Tail
When products and services blur, companies can define new ways to create and monetize value long
after the cost of the original product or device is accounted for. Value-added services can be tiered,
where a base offering is free and more premium services are monetized. In turn, consumer stickiness
is achieved, which creates the potential for a deeper and wider relationship and a longer revenue tail.
For instance, nutrition has gone digital with a host of startups offering personalized solutions. One
such startup, Habit,8
sells a test kit that analyzes DNA composition to understand an individual’s
personal nutritional needs. Once the test is taken, the user is classified as one of several types of
“eaters” and is given a customized eating plan. Ready-to-eat customized meals can be delivered daily.
Habit has taken an initial product ($299 DNA test) and extended the length of the consumer rela-
tionship (and hence, revenue tail) by providing personalized nutrition analysis (service) and healthy
meal delivery (product and service).
SERVICESPRODUCT
The Retail Clothing Blur
The original product, a piece of clothing, is now part of a shopping experience that includes having
your own remote or virtual stylist (with machine learning) who will curate a wardrobe for you. The
items are then shipped: you choose what you like, and ship back pieces you do not like.
Figure 2

ON THE FOREFRONT
Upgrading the Home Goods Shopping Experience
IKEA is taking a very IKEA approach to creating its version of the smart home. With “Home Smart,”9
it will design products that can be connected to a smart home strategy. The products will be easy to
install, easy to understand, and of course, inexpensive. Starting first with its range of lightbulbs, IKEA
designed and produced smart versions that can be regulated either though remote control, a mobile
app, or a voice assistant (Alexa, Siri, etc.).
IKEA has also upgraded the home shopping experience with its recent introduc-
tion of an augmented reality shopping app, “IKEA Place.”10
With a smartphone,
consumers can browse through IKEA’s product catalogue in advance of a pur-
chase and position a digital picture of a furniture item in a room or space to
see how it would fit. Knowing in advance if a piece of furniture will fit into a
space will transform how IKEA customers shop.
IKEA has created an effective Blur. It has taken its original products (furniture,
home accessories), combined them with value-added services (augmented
reality, smart home capabilities), and formed a unique and possibly less stress-
ful buying experience for its customers around the world.
A New Grocery Experience
Following Amazon’s recent acquisition of Whole Foods, the e-commerce giant is blending a more tra-
ditional brick and mortar player into its product and service equation. With the acquisition, Amazon
gains a network of 465 stores where it can quickly monetize innovations in grocery selection, pay-
ment, and delivery. Amazon will leverage its mobile checkout-based technology, as well as provide
quicker delivery of groceries and other goods that Amazon sells.
When products and services blur,
companies can define ways to create and
monetize value long after the cost of the
original product or device is accounted for.
Value-added services can be tiered, where
a base offering is free and more premium
services are monetized.

With the Whole Foods acquisition, Amazon can effectively extend the Blur. The original value of the
product (groceries) will increase with value-added services (mobile checkout, food or meal delivery
akin to Blue Apron) — providing a higher level of value and a better buying experience for the end
consumer.
OVERCOMING THE OBSTACLES
Industry & Organizational Readiness
Digitally-born companies have the product/service Blur encoded in their DNA. They have built their
operations and infrastructure from the ground up to support this new business model. Established
organizations may face implementation challenges when their legacy systems or organizational hier-
archy are not agile enough to adapt. While heritage companies such as GE have worked hard to
make the shift, others continue to struggle.
Again, consider Tesla. One of this company’s competitive advantages is its ability to provide over-
the-air (OTA) software updates to its cars without the restrictions of a third party. This is not the case
for most traditional carmakers. While manufacturers have the means to technically provide updates,
their OEMs are locked into prohibitive legal and contractual agreements with dealer networks that
forbid them from doing so. If the OEMs or manufacturers were to go direct for upgrades and service,
it would cut into the service revenue that dealerships generate from the cars they sell. Ford and GM
have recently made progress, with both announcing plans to perform OTA updates in the near future.11
Product & Service Accountability
With Blur, companies must be accountable for each product and service element that is part of their
new offering. Who is responsible and who will provide service for each aspect of the solution must be
well defined. As new features are added to a base product and those features seek to engage with and
gain more information from consumers, it is the responsibility of the company to communicate those
changes. As more engaging consumer interactions are established, organizations must take steps
to be more communicative and create a higher degree of trust.
In the fall of 2016, an IoT botnet launched a large distributed denial-of-service (DDoS) attack against
KrebsOnSecurity. A month following, the source code of the botnet was released publicly.12
This would
have allowed for the potential of new attacks from new botnets powered by other hackable devices
such as routers, IP cameras, or digital video recorders. Companies that either provide a partner’s IoT
as a product, or add into their product composition, must be aware of all possible internal and external
factors that can have an impact on the consumer experience.
Data Sharing & Privacy
As more data is collected from smart products, there is increased exposure to data security and pri-
vacy issues. Google set the right tone when it included “don’t be evil” in its code of conduct in 2000.13
The intent is to not exploit users, and protect and ethically use the data that they share.
Consumers must understand that the value they gain by sharing data may come at a price. They must
be given the ability to opt-in or opt-out of data sharing. When customers “give to get,” they must be
made aware of how their data will be used and if it will be monetized in some way. Consumers need
to demand transparency and security.

With Blur, companies must be accountable for
each product and service element that is part of
their new offering. Who is responsible and who
will provide service for each aspect of the solution
must be well defined.
iRobot, the manufacturer of Roomba, recently came under fire for an article that claimed the com-
pany intended to sell customer data to third parties. Roomba uses simultaneous location and mapping
(SLAM) technology to map and remember the layout of rooms. Shortly after the article was pub-
lished, the CEO communicated that the company will not sell the data. This incident raises a red flag.
Consumers need to be aware of the devices or sensors they use in their environments, what data
is being collected, and for what purpose the data will potentially be used.
The Pace of Adoption
The pace of digital adoption can, in some cases, be slow as consumers gain comfort with new offer-
ings that combine intelligent IoT sensing and data analysis capabilities with value-added services. For
example, some consumers may want a watch that just tells time. Consumers will want to see the value
in the product and service mix before adoption can hit a critical mass.
The connected home concept, for instance, has proceeded at a glacial pace because consumers have
been reticent to adopt smart appliances.15
Manufacturers have produced smart refrigerators, ovens,
dishwashers, and washers and dryers, yet consumers do not seem passionate about buying them.
Most often, people replace appliances only after they break down, which could be years after purchase.
Consumers will only purchase smart appliances en masse when they can see measurable cost savings,
reduced energy bills, and increased convenience. Having Wi-Fi access on the door of a refrigerator has
proven to be a less than compelling value proposition.
CHALLENGED TO COMPETE
The Price of Failure
Jawbone has become the second largest venture-backed failure in history.16
Having established itself
in the audio equipment space (Bluetooth headsets and Jambox speakers), the company shifted its
focus to the already crowded fitness-tracking space. However, Jawbone was unable to establish itself
as more than a product company. Plagued with product failures and customer service complaints, the
company faced insurmountable financial challenges.17
Jawbone’s inability to differentiate itself and provide value above and beyond its fitness devices
proved fatal. The company was recently liquidated amid questions concerning ownership of the per-
sonal fitness data that was collected from its trackers.18

12 | Unexpected: Five Ways Technology Will Challenge Conventions12
A Product/Service Customer Fail
In Nebraska, a 27-ton case tractor worth $250,000 is center stage in a tussle between John Deere and
local farmers. Farmers have taken their case to the state government, where they are fighting to pass
a “Fair Repair” bill.19
They are pushing John Deere for their “right to repair” the farm equipment that
they purchased from the company. The bill would require companies to provide consumers and inde-
pendent repair shops access to service manuals, diagnostic tools, and parts so they are not reliant on
expensive third-party repair solutions, and can make the fixes themselves. John Deere has taken the
position that farmers do not actually own the tractors that they pay for, but instead receive a “license
to operate the vehicle.” Locked into license agreements, farmers are forbidden to modify the software
of the tractor to fix a problem.
The company’s close-to-the vest approach put it at odds with its customers and created unnecessary
legal exposure, not to mention a good deal of negative press.
LOOKING AHEAD
1. There will be a land rush between established companies with extensive customer and product
footprints, and digitally-born innovators seeking to disrupt an industry, to create products
that combine with higher-value services. Some will excel at achieving a strong product/service
value proposition. Others will struggle to understand key customer pain points and will face usage
and adoption challenges.
2. Fragmented ecosystems and a lack of harmony with product and service integrations will
exist for some time. Platforms that allow for industry and technology standardization and inte-
gration, which have taken shape with IoT early adopters (fitness and healthcare), will develop more
broadly.
3. Expect more frequent and impactful service breaches by coordinated rogue actors. Increased
connectivity and expanding product/service integration will yield more frequent and severe inci-
dents. Rogue actors who once operated ad hoc or independently will organize higher-impact
breaches. Guarding against these breaches will require proactive planning and sound security
policies/procedures; taking ownership when they do occur; providing transparent communication;
and making amends immediately with a reparation strategy.
MOBILIZE NOW
• Consider the Potential: Take a proactive approach and define ways to embed intelligence and
value-added services into an existing product. Be on the look-out for “edge” competitors that
weren’t previously considered as players. Define the value of having a product become smarter,
more intuitive, and more service-enabled.
• Build a Roadmap: Develop the necessary roadmap to transform to the Blur model. Consider how
to provide services, and make clear what is being provided and what the user agreement entails.
Understand that your organization must not only be accountable for the product that emerges
in the near term but the Blur that it will become in the not-too-distant future. Allow for continual
iteration as consumers interact and share feedback.

13Unexpected: Five Ways Technology Will Challenge Conventions | 13
• Explore Partnerships: Look to the market for potential partners or acquisition targets to make
your product and service offering more robust, more innovative and more valuable. New partner-
ship models can take shape. Acquisitions can be used to disrupt a market or an industry.
• Adapt Your Model: Determine how your business model must change, and how IT and business
operations will need to evolve. As companies move to “Zero-IT” with product-less services, serv-
er-less computing, and low-code platforms, more IT can be automated and put into the hands
of the business. (For more on this, read our white paper “The Future of IT: A Zero Maintenance
Strategy.”)
• Protect the Consumer: Take steps to protect customer relationships by providing a safe and
transparent environment in which they can engage with your organization. Be transparent about
the data your organization collects, and why. As we discussed in Know Me, Forget Me, safeguard
data that is stored, discard data that goes unused or has aged out.
REFERENCES
• “Analytics Drives Tesla Customer Loyalty,” Datanami, August 2014.
• “Amazon to Buy Whole Foods for $13.4 Billion,” The New York Times, June 2017.
• “Roomba’s Next Big Step is Selling Maps of Your Home to the Highest Bidder,” Gizmodo, July 2017.
FOOTNOTES
1 www.intel.com/content/www/us/en/silicon-innovations/moores-law-technology.html.
2 “Tesla Owners Are Insanely Happy with Their Cars,” Business Insider, December 2016.
3 www.apple.com/healthcare/.
4 www.dietsensor.com.
5 www.cognizant.com/code-halos.
6 https://nest.com/works-with-nest.
7 www.renttherunway.com.
8 www.habit.com.
9 “IKEA’s Smart Home Dares to Make Sense,” Wired, August 2017.
10 “IKEA’s New App Will Flaunt What You’ll Love Most About AR,” Wired, September 2017.
11 “What is the Car Industry’s Problem with Doing Over-the-Air Software Updates?” Ars Technica, July 2017.
12 “KrebsOnSecurity Hit With Record DDoS,” www.krebsonsecurity.com, September 2016.
13 https://en.wikipedia.org/wiki/Don%27t_be_evil.
14 “Roomba is No Spy: CEO Says IRobot Will Never Sell Your Data,” ZDNet, July 2017.
15 “Where the Smart Is,” The Economist, June 2016.
16 “Jawbone’s Demise a Case of ‘Death by Overfunding’ in Silicon Valley.” Reuters, July 2017.
17 www.theverge.com/2017/7/6/15931080/jawbone-going-out-of-business-report.
18 “You’ve Split from Your Fitness Tracker. Can You Get Your Data Back?,” USA Today, July 2017.
19 “A Right to Repair: Why Nebraska Farmers Are Taking on John Deere and Apple,” The Guardian, March 2017.

Consider a typical day in what has quickly become
your connected life. You might wake up and step on
a smart scale, your weight instantly updated on an
app. You brush your teeth with a Bluetooth-connected
toothbrush while you ask Alexa to place your mobile
order at the coffee shop. You skip lunch to hit the gym,
where your workout is logged by way of your GPS-
enabled watch. Ready to head home, you do a web
search for a recipe you want to make that night. After
dinner, you jump on a streaming site and watch the
latest episode of your favorite show on your voice-
enabled smart TV. Your watch then vibrates to let you
know you should be ready for bed.
Each day, knowingly or not, people share data about virtually every aspect of their personal and professional
lives. As we have seen in Blur, the proliferation of IoT combined with 24x7 connectivity, data about an individ-
ual’s location, financial activity, health and wellness habits, friends and associates, purchasing behavior, social
views and opinions, and entertainment choices is continually captured.
With widespread “smart” access and more advanced data-collection technology, businesses gather, combine,
analyze, sell and use billions of data points related to our daily lives. This has given rise to a huge treasure
trove of personal data, enabling companies to develop unimaginable insights into a person’s needs, wants, or
intentions.1
Eager to provide a highly customized and deeply immersive experience for their customers, businesses
use advanced algorithms to analyze this data, develop a deep knowledge of consumers’ preferences
14
THEME 2
Know Me, Forget Me

and predict their future behavior. Personalization has evolved from building crude cohorts of
people, to developing highly individualized and targeted models. Companies find themselves
in a Catch 22: the models and data are powerful and provide positive business results, but the
people whose data they collect have a vested interest in privacy. While they know that those with
the best models and the most accurate data will have the competitive edge, getting there is not with-
out risks.
GOOD NIGHT!
GOOD MORNING!
A Day In the Life of Personal Data
Each day, we give up personal data. We may do this knowingly, such as updating our fitness app with
our daily weight or workout results. Or unknowingly by ordering coffee, doing a web search, or
browsing streaming sites for TV shows. The data that we give up may return value to us in the form
of an enhanced service, or it may be used against us in the form of discrimination or identify theft.
Personalization has evolved from building
crude cohorts of people, to developing highly
individualized and targeted models. Companies
find themselves in a Catch 22: the models and data
are powerful and provide positive business results,
but the people whose data they collect have a
vested interest in privacy.
Figure 3

Holding on to valuable personal data comes with vulnerability to government and legal demands,
rogue actors, and hackers. Any government can go to court to gain access to personal information,
and law agencies can (and will) demand data from any company if they can prove they need it. Rogue
actors can come in the form of companies, individuals, or the dark web who set out to acquire your
data legally — or illegally — for their own profit. And, cyber and ransomware attacks have grown in
frequency, complexity, and scale. Each of these pose security threats that are harmful to both con-
sumers, and the business holding their data. So, to “know” consumers better and hold their personal
data may make business sense. And once the General Data Protection Regulation (GDPR)2
becomes
law in Europe next year, companies must be able to “forget” personal data that is not required. As a
result, data that was once seen as an asset can become a liability.
As such, Know Me, Forget Me expresses the consumer’s viewpoint: “Know me, to give me an immer-
sive, personalized experience. But, forget me once my data no longer gives value back to me, and to
safeguard it against falling into the wrong hands.”
We are at a tipping point as cyber and ransomware attacks become more varied, harder and longer
to detect, more widespread, and more dangerous. U.S. companies and government agencies suffered
a record 1,093 data breaches in the last year (a 40% increase from 2015), according to the Identity
Theft Resource Center.3
And data that was once used passively to encourage us to make a purchase
now has the potential to be used to harm or penalize us.
Individuals say they want both privacy and security, but they turn over their personal data for trivial
purposes. We can assign a value to any asset that we possess, whether it be money, property, or
jewelry. When it is lost or stolen, there are mechanisms in place to log a complaint or try to recover
the item. This is not the case with personal data. We may not attribute any value to it until something
goes wrong. At this point, the data becomes highly valuable, yet there is often no recourse for those
affected by its theft or publication.
Businesses need to move to a model where individuals know the value of their personal data and can
take control of it. Such a model includes consent management, which allows a person to specify who
has access to their data, on an as-needed, temporary basis.
Know Me, Forget Me expresses the
consumer’s viewpoint: “Know me, to give
me an immersive, personalized experience.
But, forget me once my data no longer
gives value back to me, and to safeguard it
against falling into the wrong hands.”

Companies must also identify the risk associated with data. They must practice the ethical collection
and holding of data, and know when and what to expunge, to encrypt, to anonymize, to obfuscate, to
data mask, etc. They must be proactive in their data security practices, and be quick to react when
challenges are presented.
ON THE FOREFRONT
Healthcare an Industry Leader in Data Protection & Security
Healthcare has long been at the forefront of data protection and security. The concept of Know Me,
Forget Me is prevalent in healthcare due to HIPAA (Health Insurance Portability and Accountability
Act). HIPAA protects the privacy and security of an individual’s protected health information (PHI),
which includes the individual’s health status, healthcare, and any payment associated with healthcare.
With consent management, a healthcare company, provider, or insurer cannot disclose an individual’s
records without their approval, and must notify them about how their data is being used.
Israel, a Leader in Cyber Security
With the global push toward digitization, worldwide spending on security-related hardware, software,
and services rose to $73 billion in 2016, and is expected to hit $101 billion by 2020, according to IDC.4
As a result, there has been a significant increase in funding of data security startups. Investments in
cyber security in the U.S. and Israel, long a global leader in cyber security, have increased exponen-
tially.5
U.S.-approved legislation will further expand joint cyber research between the two countries.
Data Can Be Good — If Used in the Right Way
By collecting personal data, companies create opportunities to understand their
customer or prospective buyer, on a much more granular level. With today’s stron-
ger analytical models, hyper-personalization can help to identify an individual’s
wants and desires. Data-driven companies are using insights gained through hyper-
personalization to create fine grain personas. These personas anonymize the iden-
tity of an individual, to avoid both regulatory noncompliance and potential risks to
the data.
In healthcare, personal details collected from a person’s genetic composition, medical history, life-
style, and/or eating habits can be used by care providers to better diagnose, treat, and even possibly
predict the future health of that individual. With access to better and more timely health data, a pro-
active approach can lead to better health.
Technology Innovation Can be Good — If Used in the Right Way
Today, technology innovation for data collection and synthesis are developing at a rapid rate.
Chatbots can understand natural language, and provide an easier and unobtrusive way of conversing.
Artificial intelligence (AI) can be used to create anonymous personas. Machine learning uses complex
algorithms that “learn” from vast amounts of data to recommend or predict the best products and
services. Blockchain can be used to protect the transfer, usage and authenticity of data. (Visit our
website for more on AI and blockchain.)

Life sciences has been on the forefront of using data analytics to seek safe and effective treatments
using the latest technology innovations in genomics. In addition, the industry has seen strong invest-
ment in areas of AI, virtual reality, and machine and cognitive learning.6
Individuals Can Take Control of Their Data
Individuals need to take ownership of their personal data, and better control what they provide and to
whom. Individuals have the option to share their data, or not. Data captured by way of mobile devices,
the web, or even through IoT devices is mostly voluntary. In giving up their data, individuals must
always assume that it can leak at some point.
An early-stage start-up out of Silicon Valley, BitClave,7
has set out to use blockchain technology to
enable what it calls “decentralization” of online search. The intent is to allow consumers to control,
manage, and monetize their personal data. Using smart contracts, a consumer could opt in to offers
direct from advertisers. Once the blockchain has confirmed that an ad has been viewed, the contract
would release payment. Time will tell if this model is successful, as blockchain technology moves
mainstream.
ON THE EDGE
How Social Media Data Is Used
With over 2 billion users worldwide, the world’s largest social media
site (Facebook) has collected more than 300 petabytes of personal
data.8
An individual’s likes, shares, status updates, and even par-
ticipation in quizzes are all used to calculate an individual’s and a
population’s social status, ethnic affinity, political affiliation, and geo-
graphic representation.
Facebook takes the massive amount of data it collects and combines it
with data purchased through third-party sources to create micro-tar-
geted ad campaigns. While some believe this mass data accumulation will lead to a highly-personalized
experience, others believe Facebook’s practices are invasive.9
An Accidental Data Breach
The financial services industry continually evolves to meet the needs of compliance, data privacy
rules, and data security requirements, while managing third-party risk, anticipating customer expec-
tations, and staying ahead of emerging and advanced security risks and threats. While the industry
has been on the forefront of new technologies for data acquisition, fraud prevention and security,
attacks or self-imposed data security issues are still prevalent.
Take Wells Fargo, which inadvertently leaked thousands of details of 50,000 of its wealthiest indi-
vidual customers.10
By accidently releasing customer data, it violated numerous state and federal
consumer privacy laws by divulging these individual’s names, social security numbers, and size of
their portfolios.

When Data Can Be a Challenge
The challenge for individuals comes when they have given their
personal data and it is unclear how the data is being used or if
it is being used in a manner that they did not intend. With the
weaponization of data, your own data can be used against you
to play on your emotions, manipulate your mood, penalize you,
or influence your decisions.
When the Genetic Information Nondiscrimination Act (GINA) was passed in 2003, healthcare com-
panies were banned from denying coverage based on the genetic test results of an individual. But
the provisions did not cover life insurance, long-term care, or disability insurance. Legal cases have
shown that when adverse test results were discovered in the underwriting process, insurers have
denied coverage.
When Technology Innovation Presents Problems
The same technology innovations that can be used for good can also be intrusive and untrustworthy.
Chatbots that make communication (and hence, data collection) easy and fun can also lull a person
into a false sense of security, answering questions, or providing data that they would not normally give
away. Drones can now provide sophisticated video surveillance of almost anything. AI and machine
learning can use data to hasten decision making, but are also seen as having the potential to create
significant harm if algorithms are left unchecked.
Elon Musk has been outspoken about the dangers that artificial intelligence poses. His view, in a
nutshell, is that AI can start with good intentions, but produce something evil either by intent or by
accident. Out of this concern, he co-founded and invested heavily in OpenAI, a nonprofit that is work-
ing to achieve safer AI.11
Rogue Actors Exist
Some third parties (marketing firms, the dark web) use aggressive predatory practices to obtain per-
sonal data for either their nominally legal purposes, or to use in unethical or illegal ways. Many times,
this happens without the data owner’s knowledge. As a result, companies that you may not know are
using your data, out of context and without your consent.
With the weaponization of data, your own
data can be used against you to play on
your emotions, manipulate your mood,
penalize you, or influence your decisions.

A data mining and analytics company, Cambridge Analytica, is at the center of a probe by the UK’s
data privacy watchdog that claims the company may have breached privacy laws by illegally trading
third-party data without consent during the Brexit campaign.12
Cambridge Analytica is also being scrutinized in the U.S for tactics used to sway votes in the 2016
presidential election. It is claimed that the company obtained a vast dataset from Facebook by paying
users to take a personality quiz. This in turn opened users’ (and their friends’) Facebook profiles,
which provided valuable psychological insights on thousands of potential voters. The company then
crafted individual messages that served as emotional triggers to sway their votes.13
Putting Half the U.S. Population at Risk for Identify Theft
In May of 2017, Equifax became the target of one of the largest data breaches by compromising the
personal data of 143 million individuals (half of the U.S. population).14
Over the years, Equifax has
collected a treasure trove of data, often without individuals’ knowledge. This data has included social
security numbers, current and former addresses, years of utility bills, debt information, etc. The data
functions as inputs to an individual’s credit report, which can influence if the individual receives a
loan, or gets a job.
It was later learned that Equifax had not only known about the breach for several weeks prior, but
it had not disclosed a minor hack by the same perpetrators earlier in the year. The breaches could
Rogue Actor Using Your Data
Rogue actors exist who wish to acquire your personal data and use it for their own gains. They may
be using your data to steal your identify, sway your opinion, or to sell it for their own profit. A rogue
actor may be someone you least suspect, or a company that you have never even heard of.
Figure 4

have been prevented had Equifax taken a more proactive approach to data security and had imple-
mented stringent data security measures (including necessary security patch updates). In addition,
the company was also widely criticized for its response efforts. A separate site that was set up
for consumers to check their vulnerability was found to be buggy. Adding insult to injury, Equifax
inadvertently directed consumers to a phishing site that had been set up.
Citizens’ Data at Risk
For 12 days in the heart of the 2016 U.S. Presidential campaign, an analytics company, Deep Root
Analytics, accidentally leaked the personal details of 198 million U.S. citizens. Roughly 60% of the
U.S. population’s information was stored on publicly accessible servers without password protection.
The leaked data included 1.1 terabytes of citizens’ home addresses, birthdates, phone numbers, and
opinions on political issues.
Lost Data & the Growth of Hackers
2016
2015
LEGEND ■ 2015 ■ 2016
500M Govt Health-
care
Tech-
nology
Retail Education Financial Other
Nuisance Existential
Data
Account
Access
Financial
Access
Identity
Theft
State
Sponsored
Hacktivist Malicious
Insider
Accidental
Loss
Malicious
Outsider
1B 1.5B
Data Records Compromised
1.4B
708M
1,200
1,000
800
600
400
200
0
Data Breaches by Type
500
250
125
0
Data Breaches by Industry
1,400
1,200
1,000
800
600
400
200
0
Data Breaches by Source
15%
28%
11%
12%
9%
12% 13%
8%
4%
11%
18%
59% 68%
19%
9%3%/471%/22
269
143
79
190
330
1,050 1,223
333
164
189
215
157
214 229
493
%
Figure 5
Source: breachleveldata.com

While the information did not appear to be hacked, Deep Root Analytics is now facing its first class-
action lawsuit, which stipulates the company “failed to secure and safeguard the public’s personally
identifiable information,” and left individuals vulnerable to identity theft and at risk of loss of privacy.15
LOOKING AHEAD
1. We are at the tip of the iceberg with third-party data aggregators and their potential impact
on business and individuals. Without our knowledge, third-party aggregators are collecting our
personal data. We do not know to what extent they are collecting it, or what the data will ultimately
be used for. The data could be used for their own monetary gain, or to harm or influence us in
some way.
2. We are in the early days of harmful, widespread, and more coordinated data breaches. The wea-
ponization of data is in its infancy. The scope of attacks will increase in breadth, and in scale. They
will be more coordinated and severe, have greater impact, and potentially be more catastrophic.
3. There will be a global move to develop a common framework for data protection and privacy.
Countries will look to Europe and the implementation of the GDPR in 2018 for their move-forward
cues. With implementation of GDPR, the EU seeks to strengthen and unify data protection by
giving back control of personal data to its citizens and harmonizing data protection regulations
across the EU for one’s private, professional, and public lives.
MOBILIZE NOW
• Acquire Transparently: Companies must make individuals aware at the point of acquisition what
data is being collected, for what purpose, and for how long it will be kept. Companies can adopt
differential privacy, where they capture and use only required data. Opt-outs must be provided,
and personal data that can be threatening to an individual should never be collected.
• Hold Securely: Companies need to evaluate and consider the risk of data. By determining the
risk, companies can determine what data to acquire, what is essential to hold, and what can be
erased after use. They must proactively have expiry dates on all data, and set out to design a
comprehensive data security program to protect against threats and to meet growing regulatory
requirements. While companies can take all the steps necessary to proactively safeguard personal
data, they should factor in potential security breaches and plan for solid redressal mechanisms.
• Use Ethically: Companies must establish and maintain the trust of consumers by using stored
data in an ethical manner, and not in a way that endangers them. See Google’s “don’t be evil”!
Data should not be used for any other purpose than what was originally communicated. And when
required, companies should comply with requests from governmental agencies to share only
specific data in the stipulated time.
• Proactive Amnesia: Understand that consumers may want the right to be forgotten. This could
mean that personal details are erased upon request. Or it could mean that companies estab-
lish predefined policies that ensure the timely erasure of data that is either no longer in use, or
has expired.

REFERENCES
• “Technology That Augments Human Thinking,” MIT Technology Review, June 2014.
• “Nearly 200 Million US Voter’s Personal Data Accidentally Leaded by Data Firm Contracted by
RNC,” Fortune, June 2017.
• “GOP data firm that exposed millions of Americans personal info is facing its first class-action
lawsuit,” Business Insider, June 2017.
• “Target Pays Millions to Settle State Data Breach Lawsuits,” Fortune, May 2017.
• “BitClave Wants to ‘Decentralize’ Search, Let Users Profit from Their Own Data,” VentureBeat,
July 2017.
• “Armed and Ready: How Your Data is Being Weaponized Against You,” Wired, 2014, https://www.
wired.com/insights/2014/08/armed-ready-data-weaponized.
• “As Equifax Amassed Even More Data, Safety Was a Sales Pitch,” The New York Times, September
23, 2017.
FOOTNOTES
1 www.cognizant.com/code-halos.
2 https://ico.org.uk/for-organisations/data-protection-reform/overview-of-the-gdpr/.
3 Identity Theft Resource Center, Data Breach Report, 2016 Year End Report.
4 Here’s How Much Businesses Worldwide Will Spend on Cybersecurity by 2020, Fortune, October 2016.
5 Israeli Cyber Security Industry Grows as Global Threat Multiplies, Bloomberg, January 2017.
6 “Big Pharma Turns to AI to Speed Drug Discovery,” Reuters, July 2017.
7 www.bitclave.com/en/.
8 “How Facebook’s Tentacles Reach Further Than You Think,” BBC, May 2017.
9 98 Personal Data Points that Facebook Uses to Target Ads to You,” The Washington Post, August 2016.
10 Wells Fargo Accidently Releases Trove of Data on Wealthy Clients,” The New York Times, July 2017.
11 “Elon Musk’s Billion Dollar Crusade to Stop the AI Apocalypse,” Vanity Fair, July 2017.
12 Data Firm Behind Trump Victory Hits Back at Brexit Data Breach Allegations, CNBC, March 2017.
13 The Great British Brexit Robbery: How Our Democracy Was Highjacked, The Guardian, May 2017.
14 “All the Ways Equifax Epically Bungled Its Breach Response,” Wired, September 2017.
15 “Deep Root Analytics Sued After Data Breach,” Business Insider, June 2017.
16 https://en.wikipedia.org/wiki/Don%27t_be_evil.

A hurricane approaching the coast was just upgraded
to a category 4 storm. The storm is expected to make
landfall within hours as businesses and homeowners
prepare for its impending impact. Location-based data,
combined with in-the-moment weather data, provides
instant analysis of the storm’s potential impact. Having
real-time information to act on can help determine
how much damage a house, a business, or a community
will sustain. It could mean the difference between a
life, and a death.
When is data at its most valuable or most useful? When is there a risk
of data becoming old and no longer relevant? And how quickly does
data lose its value?
Today, massive amounts of data are being collected and held, often
without a full understanding of what it will be used for or when it will
have maximum value. Foresight and planning is needed to understand
how this valuable commodity can be harnessed to provide business
insights and inform appropriate actions.
Data has a time-based value. It is most useful when it provides context,
insight, and meaning that can inform better decisions and add more
value for businesses, their partners, and their customers.
24
THEME 3
Excellence at the Edge

Data’s ubiquitous nature creates the opportunity to extract value. It comes from an increased variety
of sources (IoT, mobile geolocation, streaming data, and static online and offline), and a growing
variety of formats (structured, semi-structured, unstructured). Data from sensors and IoT devices
represent an opportunity that can deliver immediate value and response. IoT data can be acted upon
to trigger an immediate response to an alert.
Yet data alone holds no value. Data becomes valuable when it is harmonized in real time, and made
meaningful through analytics that reveal inner truth. Advanced analytics (including predictive
analytics, data mining, and location intelligence) play a key role in helping industries apply data to
stay ahead of the curve. Analytics can expedite the analysis of proliferating data formats and struc-
tures — enabling companies to retain more customers, serve them better, improve products and
services, strengthen their market standing — or even disrupt an entire industry.
As data ages, its utility and value diminishes. Data loses its value when it can no longer be decoded
to provide valuable insights. Missed sales opportunities or poor customer experiences result when
data is not used at its most valuable point, e.g., it is used too late. Take the car dealership that calls
incessantly trying to extend the warranty of a car traded in years ago. Aged data can also present risk.
(In Know Me, Forget Me we highlight rogue actors who gain access to data for wrongful purposes, and
how to prevent them from doing so).
Today, edge computing and distributed analytics can provide real-time analysis at or near the point
where the data is collected. In most cases, this is where and when the data is the most valuable, and
can be applied to spur immediate action or speed response.
Excellence at the Edge enables organizations to monitor the physical and cyber worlds, and reduce
the time between data generation and application. Edge analytics can reduce the bandwidth needed
to move data from an IoT device to a central repository. It shrinks latency, reduces transmission costs,
and improves overall quality of service.
The ability to capture and quickly use data while it is fresh, combined with the network effect can
improve products and services, attract more customers, help grow revenue, and add more value to
the information generated by the business.
Data becomes valuable when it is
harmonized in real time, and made
meaningful through analytics that
reveal inner truth.

ON THE FOREFRONT
Minimizing Losses in a Natural Catastrophe
Globally, natural catastrophes cost insurance companies $50 billion in payouts in 2016.1
Having the
right data at the right time as a storm approaches can mean the difference between life and death or
significant loss of personal or business property.
Insurers use geo-based data to track the path and intensity of an oncoming storm in real time.
Pre-event, the insurer can warn policy holders of an impending threat. During the storm, the insurer
can mobilize emergency response vehicles in areas that are hardest hit. And post-storm, historical
analysis can provide ways to minimize damage from future storms.
We partnered with a leading property and casualty insurer to create the Interactive Exposure Map
(IEM). IEM is a spatial query engine that uses local data combined with Google Places and Maps APIs
to provide a data-driven real-time view of an approaching storm.
Responding in Real Time with Speech Analytics
Real-time speech analytics can provide insight into a customer’s attitude, frustrations, and how they
may react as they interact with your company. By monitoring an individual’s voice, companies can
better understand their emotional state based on their tone, intensity, and intent.
By intervening at the most critical time — when a customer and a call center rep are still on the
phone — the rep can receive an alert based on sentiment analysis of the customer’s tone of voice and
gain actionable advice on how to redirect a call when it goes in an unintended direction.
Real-Time Customer Crisis Management with Speech Analytics
With real-time speech analytics, a customer service representative can monitor a customer’s level of
frustration or anger, and turn a negative experience into a positive one. Real-time speech analytics
can improve customer satisfaction as well as positively impact customer retention rates.
Figure 6

Speech analytics can be used to improve a customer’s experience, support the ability to make a sale,
cross-sell or up-sell, and reduce customer attrition by reacting in real time to an individual’s concerns.
Act on Greater, Real-Time Insights
Greater and more impactful insights can be gained, and then acted upon, by using the freshest data.
While not all data needs to be processed at the point of collection, it is important to determine what
data can reveal the most meaningful insights immediately, and what can be held for later analysis.
The autonomous car is a data center on wheels. Outfitted with hundreds of sensors, it analyzes data
on the fly to make immediate driving decisions. Ultrasonic sensors immediately sense obstacles in
the way of the car and make necessary driving adjustments. Radar sensors judge how far an object
is from the car, warning if another car is approaching too fast or is in close range. Image sensors take
snapshots of objects and act as the car’s eyes. An image sensor sees a red light and prepares the car
to stop. Combined with data from camera sensors, light sensors identify actual images in range of the
car (e.g., a human being).
The Autonomous Car & Sensors on the Edge
The autonomous car exemplifies Excellence at the Edge. Sensors provide data, insights, and alerts in
real time to ensure a safe and accident-free driving experience.
Figure 7

The Monetization of Data
The value of data is derived when it is processed and used as quickly and as meaningfully as possi-
ble. Data that is useful for one company can also be of value to another, but for completely different
reasons. By tapping into its full potential, companies can make their data a revenue-generating asset.
Data can be sold in aggregate, or embedded with tools in a product or service. Industry-specific data
products or analytical solutions and services that provide valuable customer insights can emerge
from bits and bytes of metadata.
In seeking ways to monetize data, organizations must practice ethical data handling by first informing
customers how their data will be used. The advent of GDPR will bring greater data privacy and protec-
tion, as well as guidelines on how data can be monetized.2
A data marketplace or exchange can create an opportunity to more fully realize the value of data by
providing a platform to connect providers of data with consumers of data. Terbine3
is a startup that
hopes to capitalize on the vast amounts of IoT data that is generated each day. Its IoT data market-
place, currently in beta, will act as a broker to connect those generating data with those needing it.
To establish a market price, Terbine ranks the data based on quality, and prices it based on how
“fresh” it is. Customers will benefit by having access to larger volumes of quality data from sources
they would not have been connected to previously.
Achieving Extreme Business Value
Extreme business value can be achieved by cultivating data, converting it into valuable insights, and
providing significant value back to the end customer. Today, only a small percentage of data that is
gained through IoT devices is actually applied.4
Data that is harnessed and used at its freshest and
most relevant can be the catalyst for creating a richer customer experience, designing a killer product
or service, growing a business exponentially, or disrupting an entire industry.
Take the well-known story of Netflix, which has achieved extreme business value — growing to over 100
million subscribers worldwide — by extracting strategic insights from customer data. The company’s
Data can be sold in aggregate, or
embedded with tools in a product or
service. Industry-specific data products
or analytical solutions and services that
provide valuable customer insights can
emerge from bits and bytes of metadata.

streaming video service collects, distills, and analyzes data on subscriber behavior and preferences
to inform product development and make viewing recommendations as a way to keep viewers happy
and loyal. By analyzing the completion rate of watching a show, film, or series, for instance, Netflix can
determine what a person is more likely to watch next or in the future. For example, big data analysis
was used to influence the development of one of Netflix’s most popular series, House of Cards.5
By knowing a subscriber’s behavior before, during, and after a movie, Netflix can offer instantaneous
recommendations once the credits start to roll. This near and real-time analysis is Netflix’s competi-
tive advantage, which has allowed it to outmaneuver well-entrenched competitors such as HBO.
ON THE FOREFRONT
Racing at the Edge
A Formula 1 race car is one of the most heavily instrumented machines in the world. As the car races
around a track at 200 miles per hour, it can transmit 2GB of data per lap, with up to 3TB of data during
a full race. With over 150 sensors installed on the car’s chassis and tires, and throughout its engine,
data engineers can track the status of tire pressure, fuel efficiency, wind force, GPS location and brake
temperature — in real time.
For instant analysis, within milliseconds each sensor transmits data to the racing team’s engineers,
either at the race track or to the team’s headquarters. Teams combine edge and predictive analytics
to run real-time race simulations to determine what adjustments must be made to the car, the driver,
or to the strategy of the race.
Edge analytics and simulations help teams make educated
decisions instantly. The results can coach a driver on the race
conditions, when to change a tire, or when to alter race strat-
egy. If a car has malfunctioned or crashed, instant analytics can
determine the extent of the damage and if the car can or should
continue.
Edge and predictive analytics are combined with a driver’s
instincts in real time to help Formula 1 teams push their heavily
instrumented cars into better pole positions and win more races.
Take the well-known story of Netflix, which has
achieved extreme business value — growing to over
100 million subscribers worldwide — by extracting
strategic insights from customer data.

Missed Opportunities
Data has a time value, and with the passing of time, it loses its value and its relevance. Data that is
realized late or not acted on with immediacy can lead to lost opportunities, lost revenue, or even
a catastrophic event. By proactively determining what data can help inform decisive actions, lost
opportunities can be minimized.
Not knowing a restaurant patron just left negative feedback online about the dinner she was served
can result in a lost customer and a poor reputation for the restaurant. Losing a shipment can lead to
lost revenue during a busy holiday season. Not knowing the severity of a brush fire that is about to
spread can cause catastrophic property losses.
Disparate Data Sources
Many organizations store large volumes of operational data that is distributed across business lines.
This data, which typically resides in enterprise systems of record, can be correlated with freshly
generated data at the network’s edge (e.g., from IoT devices) to provide significant insights to custom-
ers or the organization.
While distributed data may not be practical due to organizational or business-line demands, gaining
access to disparate data sources is oftentimes difficult, when corporate politics or lack of foresight
don’t allow for cross-pollination. Data that is stuck in silos loses the potential to ever have its full value
extracted.
In March of 2017, Brussels was hit with three coordinated terrorist attacks that killed 32 and wounded
300. It was found that the attacks could have been prevented had it not been for intelligence fail-
ures within Belgium, and between various EU intelligence agencies.6
Several of the Belgium terrorists
had been part of previous attacks in Paris, yet commonalities were not identified as the terrorists
hid in plain sight, living in Belgium.
There was a significant gap between Belgium intelligence and political worlds, and between EU
intelligence services. Within Brussels, there are 19 municipalities with six police forces, and central
government and local municipalities are often at odds with one another. Across the EU, there is little
coordination between various intelligence services, as agencies had the terrorist on their radar, while
others did not. Information was not shared broadly, specifically between France and Belgium. This
disparate data and lack of information sharing led to Belgium’s worst terror attack, and the loss of
many lives.
By proactively determining what data
can help inform decisive actions, lost
opportunities can be minimized.

Mass Hoarding of Data
As data volumes increase exponentially, data lakes have grown in popularity. Data lakes are huge stor-
age repositories that are built to hold enormous amounts of data until the data is needed. The massive
amount of data generated by IoT devices is one reason for companies to deploy large Hadoop-based
platforms7
for this purpose. With a data lake, organizations can cost-effectively aggregate huge data
sets in one place and analyze them when needed.
While a data lake can have value, it can also have a negative effect on the vibrancy of data. Companies
tend to collect data en masse, not always knowing what it will be used for, or what value it can ulti-
mately bring. More data is not necessarily better. Unused or aged data offers little value, and can
present an unintended expense or risk. Data hoarding can create an economic drain and, as previously
noted in Know Me, Forget Me, data that is held hostage can pose a risk if acquired by rogue actors, be
vulnerable to hacking, or part of ransomware attacks.
Algorithm Biases
Data scientists use algorithms to drive analytical outcomes, and develop predictive and distributed
analytics that can have game-changing impacts on decision making, product and service direction,
and customer experiences. The weakness with algorithms is that they are only as good as their design
and the data sets that inform them. Some algorithms have been shown to unknowingly inject bias
into results, which can lead to unintended consequences.
Algorithms that have been studied have picked up race and gender prejudices from language pat-
terns, data sets, and pictures used in their analysis. Some have shown a sexual bias, correlating
women with homemaking activities or shopping, while men are associated with sports, leadership,
and higher incomes. Algorithms that go unchecked can have a negative impact on sexual and racial
discrimination if important decisions pertaining to hiring, qualifying for a loan, or legal matters arise
from their results.
Algorithms must be monitored for fairness at the individual, corporate, and government levels. Those
creating algorithms and analyzing their data need to look for biases and correct them as they occur.
Companies can proactively set up internal ethics committees that provide oversight for algorithm
development. Governments can play their part by exploring possible regulations that measure fair-
ness in algorithms used in key decision making. Groups of concerned individuals are also acting. The
AI Now Initiative, formed by a group of researchers working with the American Civil Liberties Union,
was created to serve as a watch dog of sorts for algorithmic biases.8
Algorithms must be monitored for fairness at the
individual, corporate, and government levels. Those
creating algorithms and analyzing their data need
to look for biases and correct them as they occur.

LOOKING AHEAD
1. Those organizations that harness the value of fresh and relevant data for immediate action
and response can make non-linear leaps that will lead to exponential growth. Data — how
and when it is processed and then acted on — will become a company’s number-one competitive
advantage.
2. Edge computing and analytics will grow in prevalence, and will become a vital part of business
and IT architectural strategies. Hybrid models will leverage the power of the edge combined
with cloud and centralized computing capabilities.
3. Global data marketplaces and trading exchanges will take shape and provide valuable plat-
forms to buy and sell data. Corporations will make data monetization a strategic priority, while
consumers will take control of their personal data and act as their own data brokers on the
exchanges.
MOBILIZE NOW
• Create a Data Plan: Develop a data acquisition and retention plan. Understand what data is
needed and at what point it will be at its most valuable. Develop a tiered view of data to define
what data is best to use at its freshest, what data can be stored to extract future value, and what
can be discarded. Data at rest will be used after an event has occurred for predictive or historical
analysis. Data in motion will be used for edge computing and distributed analytics.
• Consider a Hybrid Model: Create a hybrid data processing architecture that takes advantage of
the strengths of edge computing while leveraging cloud and central processing. This model can
be used when analysis and insights need to be conducted and acted upon very quickly. The cloud
can be used to aggregate and improve AI and machine learning. Centralized computing can be
employed for storing data, and for predictive and historical analysis.
• Don’t be a Hoarder: Data in volume does not translate into data that is most relevant and useful.
Holding data that has little or no intended value can become an expense, or a risk. Hold data that
can realistically be used at some point in time to add value. Be deliberate about what to use, what
to hold, and what to discard.
• Plan for Oversight: Understand the implications and potential negative consequences of algo-
rithms that may inject bias and produce misinterpretations. As algorithms grow in volume,
complexity, and impact, create checks and balances to ensure fairness.

REFERENCES
• “Machines Taught by Photos Learn Sexist View of Women,” Wired, August 2017.
• “Internet of Things Helps Fuel Growth of Data Lakes,” NetworkWorld, June 2017.
• “Will Analytics ‘At the Edge’ be the Future of Big Data?,” Forbes, August 2016.
• “Data is Giving Rise to a New Economy,” The Economist, May 2017.
• “Brussels Attacks: How Extremism Flourished Amid Lack of Integration in Molenbeek,” The
Independent, March 2016.
FOOTNOTES
1 “Global Natural Catastrophes Cost Insurers $50B in 2016,” Insurance Journal, January 2017.
2 https://en.wikipedia.org/wiki/General_Data_Protection_Regulation.
3 www.terbine.com.
4 “Unlocking the Potential of the Internet of Things,” McKinsey Global Institute, June 2015.
5 “Giving Viewers What They Want,” The New York Times, February 24, 2013, www.nytimes.com/2013/02/25/business/media/
for-house-of-cards-using-big-data-to-guarantee-its-popularity.html?mcubz=1.
6 “Brussels Attacks: How Extremism Flourished Amid Lack of Integration in Molenbeek,” The Independent, March 2016.
7 https://en.wikipedia.org/wiki/Apache_Hadoop.
8 http://ai-initiative.org/.

The future of automation is no longer man vs.
machine, but man and machine. Human and robot
collaboration is already upon us as humans work with
robots and software agents of different shapes, sizes,
and computational composition.
Physical robots that can talk, move, and emote are becoming more advanced and commonplace — working
with humans in the office (robotic assistants), on the factory floor (cobots), or in retail locations (human-
oid robots). Software agents or bots already impact most business processes, across every industry. These
intelligent agents can process reams of data and give computational analysis and insight to support critical
business decisions.
As robots continue to advance and take their place in the work environment, humans will need to adjust their
way of working to accommodate these new work partners. With human-robot collaboration, core tenets of
running a business will change to support this new work dynamic. Imagine how talent acquisition and onboard-
ing would have to adapt. Imagine the implications of productivity and performance measurement. Imagine
how inevitable conflict that will arise between humans and their robot counterparts will need to be managed.
Imagine if there are performance problems, or if work demand decreases. What will happen if a reduction in
this new workforce is required?
In the new human and robot workforce, workers will be identified based on
increased workload, seasonal demand, or to supplement attrition. Supply and
demand may determine if a human or a robot is hired. As the right human needs
to be identified who can best fit a role, staffing companies of sorts may exist
to supply permanent or temporary robots based on demands. These staffing
outfits will know the best source for robots, and find those that best fit a com-
pany’s needs.
Upon entering the workforce, robots will require the same type of introduction
that humans receive. Team managers may run onboarding orientations where
humans and robots are trained together on their new roles. During orientation,
34
THEME 4
Automation at Work
TEAMWORK

they will be introduced to their new environment, where they will meet the humans and robots with
whom they will work. Together, they may partake in team-building exercises that will jump-start their
new working relationship.
Expectations of these new co-workers, human and robot, will need to be established. New ways to
measure productivity and performance will be defined as robots and humans are held accountable
for how they perform relative to their colleagues. Both may be assessed by how they contribute to
the company’s bottom line. Performance reviews will be conducted that will measure the progress
and growth of each. Robots may report to other robots, or humans. Humans may report to humans,
or robots.
As work moves forward, anxiety, frustration, and grievances may occur among these new co-
workers. Power struggles may surface. A human may feel that a robot is not pulling its weight, is doing
something wrong, or has been offensive. The same may hold true for the robot. The robot may think
that humans are relatively slow and make mistakes. An empathetically-programmed robot could feel
obliged to make up for their inefficiencies. Management teams that combine human and robot points
of view will arise and represent the needs of both parties.
Peak season will have ended, or there is an overall slowdown in business. A reduction in the human
and/or robot workforce is required. Performance reviews and productivity stats of each will be con-
sidered, as will the commitment to maintain predetermined human-to-robot ratios. Humans may be
reskilled, or let go. Robots may be reconfigured, or terminated. But, as with humans, what becomes of
all the knowledge that was gained by a decommissioned robot?
The collaboration between humans and machines will be more powerful than humans or machines
working alone. This new collaborative dynamic will fundamentally change the nature of the work-
force and work environment. As they become more advanced, robots will augment humans in ways
that increase productivity and workplace satisfaction, helping companies to reach new performance
New ways to measure productivity
and performance will be defined
as robots and humans are held
accountable for how they perform
relative to their colleagues. Both may
be assessed by how they contribute
to the company’s bottom line.

thresholds. Business processes will need to be reimagined, and the human anxiety of robots taking
over all work will need to be alleviated.
With human and robot collaboration, the nature of work relationships and the work environment will
inevitably change, it will evolve, and eventually normalize. Companies of today must embrace this
inevitability, and understand the impact that this new human and robot workforce will have across all
business line and functional areas.
ON THE FOREFRONT
Collaborative Automation in the Automobile Industry
Since its beginning, the automotive industry has been on automation’s forefront to cut the time and
cost it takes to move a car from design to production. Robotic automation is now commonplace,
where hundreds of robots of different variations can be found in any modern automobile manufac-
turing facility.
Automobile manufacturers are increasingly adopting collaborative automation, where humans and
robots work together synergistically to leverage the strengths of each other to supercharge pro-
ductivity. Cobots, which are smaller in size than their industrial robot counterparts, are core to the
collaborative automation approach. Cobots are lighter in weight, more agile, and easier to operate.
They have had their strength, force, and speed altered to more safely work with humans. Sensors
installed on the cobot can sense a human in close range, and either change its location or shut down
if need be. (Read our related white paper, “The Robot as Coworker.”)
At BMW, humans are working collaboratively with cobots on the factory floor, where the cobots take
up more mundane and labor intensive activities, and humans take up higher value activities like car
customization. An MIT study of BMW’s collaborative automation and the use of cobots reported that
human and robot teams were outperforming both human-only teams, as well as all-robot teams.1
Collaborative Automation in a Digitally-Born Enterprise
Amazon is among the companies that have successfully automated its operations. Everything from
Amazon’s supply chain to customer relationship management is optimized through big data, artificial
intelligence, and robotic automation. In its fulfillment centers, Amazon has embraced robotic automa-
tion, employing 100,000 robots in areas such as initial product arrival and the dispatch of customer
orders. There, human workers have transitioned from performing the bulk of manual labor, to working
collaboratively with robots in the stacking, picking, and shipping of customer orders, or overseeing
how robots do their work.2
With human and robot collaboration, the
nature of work relationships and the work
environment will inevitably change. It will
evolve, and eventually normalize.

While Amazon’s focus is on efficiency and automation, it views its people as central to its technology
and automation strategy, and has been at the forefront of employing human-to-machine collabora-
tion. The company represents an example of how automation can be combined with human labor to
increase productivity and yield significant business results.
Advances in Automation, Robotics, & Cognitive Computing
Intelligent process automation, robotic process automation and cognitive computing (including
artificial intelligence, machine learning, and natural language processing) are playing an extremely
important role in powering automation.
Technological advances that support the workforce include innovations for customer self-service
(kiosks, interactive touch screens), AI-assisted robotic process automation (bots to automate work-
flows and processes), industrial robots (physical robots, including cobots), humanoid robots (customer
facing in retail), virtual assistants (personal digital concierges like Siri or Alexa), and sensory arti-
ficial intelligence (ability to understand and express emotions via image and video analysis, facial
recognition).
Collaborative Automation in the Workforce
The workforce of the future is one where humans and robots of different shapes, sizes, and composi-
tions will work together as team members to achieve common goals.
Figure 8

With these advances, Moore’s Law is taking effect in automation. Smaller, less expensive cobots are
now replacing larger industrial robots. These robots offer more flexibility, are safer for humans to
work with, and are less expensive. Barclays Capital has projected that cobot units will grow from
2,500 units today, to 15 million units by 2020, representing a $3.1 billion market.3
New Business Constructs
As automation transforms industries and robotics become easier to integrate into work environ-
ments, substantial changes in business processes and business models can help companies enter new
markets, achieve competitive advantages, and realize significant economic gains.
Advances in technology support the ability to completely rethink business processes by finding new
ways to do things better, cheaper, leaner, and more quickly. Automation will bring new business pro-
cesses that are either re-engineered to retrofit automation into an existing way of working, or newly
imagined processes that can take advantage of the advances in automation. As an example, 3-D print-
ing of a component can supplement the activities of a complex assembly line.4
Advances in automation and technology will create new ways to interact with and deliver value to
the customer. New and disruptive business models will combine the power of automation with the
strengths of humans. Technology’s ability to perform rote tasks and its growing cognitive capability
will be combined with humans’ ability to be strategic, creative, empathic, and understanding.
Automation will also bring new ways of delivering products and services. Ford has recently partnered
with Domino’s to test-market the delivery of pizza by autonomous cars. The automaker’s goal is to
test the comfort level of consumer interaction with a driverless car, compared with pizza delivered by
a human.5
Industries, Reimagined
With human and robot collaboration, industries can be reimagined and rewired today, for tomorrow.
Automation can be applied to increase scale, speed of work, and time to market. While most industries
have already started to embrace automation to enhance front-, middle-, and back-office operations,
we are only at the beginning of what is possible.
With these advances, Moore’s Law is
taking effect in automation. Smaller, less
expensive cobots are now replacing larger
industrial robots. These robots offer more
flexibility, are safer for humans to work
with, and are less expensive.

• In financial services, automation of rote processes has begun to transform front-to-back opera-
tions. Banks and other financial services organizations are streamlining business processes and
reducing operational costs by way of process automation in areas that include customer on-board-
ing, loan origination, and account reconciliation. Further automation advancements in regulatory
and compliance management (known as regtech6
) can support process efficiencies and reporting
accuracy.
• A growing and aging population, combined with an increase in new patients in the system, has put
significant strain on the healthcare industry. Automation can further help care providers be more
efficient and effective while enhancing the patient experience and quality of care. Automation has
begun to improve areas that include patient and medical flow, allocation of medical resources,
emergency procedures, and surgery management. Our TriZetto Healthcare Process Automation
Solution can process hundreds of claims automatically, resulting in a 65% reduction in claims
backlog in a week, our clients report.
• In retail, automation and intelligence are significantly enhancing the customer experience as
retailers embrace edge analytics (per Excellence at the Edge), cloud computing, and IoT to facil-
itate frictionless retail. In-store experiences are combining human and automation synergies to
create unique customer experiences. Chatbots provide consumers with product recommenda-
tions, in-store assistance, and promotional on-the-spot offers.
• Machine learning and chatbots are transforming the partnership between accountants and their
clients in tax and accounting. HR Block has partnered with IBM Watson to enable accountants and
tax preparers to more easily identify credits and deductions.7
TurboTax has introduced a tax bot
that can be the front line to directly answer customer’s tax questions.
• The food service industry is being transformed as technology redefines how people get their food.
Fast food restaurants have incorporated robotic processing into food production, and several
large chains like Wendy’s, McDonald’s, and Panera now provide food kiosks where customers can
order, pay, and then receive food without the assistance of a human. With automated processes, a
restaurant can run more efficiently, which in turn will lead to speedier service and more satisfied
customers. As robotics supplement the work of humans, a restaurant can seek to achieve overall
lower operating costs.8
ON THE FOREFRONT
Robots Across Industries in Japan
Japan is in the midst of a population crisis where birthrates have stalled, people are aging, and
the total population is significantly decreasing (estimates show that the country’s population will
decreases from 128 million to 100 million by 2050).9
Faced with a chronic labor shortage, Japan is
looking to robotics as a work-around. In 2016, Japan launched the “Robotic Revolution Initiative,”10
a five-year plan to support the growth of both industrial as well as humanoid robots. The initiative’s
focus is to transform robotics to assist in every part of work, from manufacturing to caregiving, in
organizations of all sizes, across industries.

While robotics in Japan are most prevalent in the automotive, semiconductor, and electronics indus-
tries, robots are now filling the void from lack of caregivers. With a rising number of seniors, the
government of Japan has dedicated significant funding to robotic advancement to care for the
nation’s elderly. These robot caregivers monitor health, deliver food, turn off lights, and transfer the
elderly from beds to wheelchairs.
An Industry in Transition
The composition of legal teams is changing as robots begin to augment the work of lawyers. Advances
in artificial intelligence, natural language processing (NLP), and bots applied to a variety of legal activ-
ities are forever changing the legal industry.
NLP is used to scan and predict what documents could be important to a case. Analytics are trans-
forming legal strategy by making predictions on the outcomes of cases based on previous cases and
the profiles of the judges and lawyers involved in the case. Bots can now draft rudimentary responses
to legal questions that humans can then use to write legal memos.11
Innovation in this space is booming. Over 280 legal startups have raised approximately $757 million
during the past three years12
and law firms are launching their own accelerators to stay on top of
technological advances as well as create their own innovative solutions.
Collaboration Automation in the Courtroom
As the legal industry is transformed by automation, the courtroom of the future will be one where
humans and robots work together to plan, prepare, and present cases.
Figure 9

Robotics add more efficiency to legal activities and provide support for case management. By reduc-
ing legal research and contract review time, a lawyer can spend more time on higher-value activities
which can mean the difference between a favorable or unfavorable ruling.
The Market Will Get Tougher Before It Gets Better
In What to Do When Machines Do Everything, the authors (Cognizant’s Malcolm Frank, Ben Pring, and
Paul Roehrig) not only reference that jobs will be lost, but new jobs will be created as a result of AI and
automation, with unemployment rates staying relatively the same. The authors reference three ways
automation will impact the workforce: automation will eliminate 12% of existing jobs, 75% of existing
jobs will be either enhanced or altered, and 13% will be net new jobs created.13
This same view can be attributed to the Automation Paradox14
or Jevons Paradox.15
Both are based on
the premise that when demand increases in response to lower prices or efficiency of a resource, the
rate of consumption will increase, and hence human demand (employment) will increase.
Adopting automation at scale on an organizational level will take time. There will be disruption in the
job market along the way, and it will get tougher before it gets better. There will be a short-term skills
gap, and some degree of job loss is inevitable. The responsibility is now on individuals, companies, and
governments to have the foresight to plan and prepare for the coming changes.
Individual Adaptability & Responsibility
The onus to some degree is on individuals to understand the impact automation will have on their
careers and to make the necessary educational and career decisions to prepare for it. In this new era,
people will need to be more adaptable, more flexible, and open to change as working with robots will
become the norm.
“Humans need to double down on being humans.”16
Individuals should evaluate their own capabili-
ties and skills, and identify which will give them an edge over machines in the coming years. In the
workforce, roles that will be in demand will require more empathetic skills, including the ability to
understand, motivate, persuade, and interact with others. While robots will excel at manual and repet-
itive tasks, humans will excel at roles or tasks where they can be most human.
Corporate Responsibility
Companies are incented to drive shareholder value, yet they will face tougher social challenges as
automation advances and takes on greater importance and responsibility within the organization. In
this new era, organizations will need to be more ethically aware and responsible in supporting their
employees in the development of the skills necessary to transition into new roles.
While robots will excel at manual and
repetitive tasks, humans will excel at roles
or tasks where they can be most human.

The educational system will need to
be modified to support impending
automation transformation by teaching
the skills necessary for humans to excel
in the new era. While there are still
many unknowns regarding which roles
and skills will be in demand, it is fair to
assume it will be those that machines and
robots are not good at today (empathy-
based roles, strategic thinking roles).
A 2017 McKinsey report on automation reveals that 60% of all occupations have at least 30% tech-
nically automatable activities.17
Companies that embrace automation will see increased productivity,
leading to lower costs and higher margins. This will leave these organizations with larger budgets for
product development and process innovation. These gains can be leveraged to build a stronger work-
force, or retrain and re-skill displaced employees, the report notes.
Societal Support
During previous industrial revolutions, the U.S. government provided societal support that facilitated
a relatively smooth transition from era to era. This support came in the form of new job creation (con-
struction, infrastructure), governmental retraining programs, and enhancing workers’ rights.18
As automation continues at an unrelenting pace, governments the world over will need to consider
providing these types of societal safety nets to make it easier for workers to educate or retrain them-
selves, acquire new skills, or switch jobs if need be.
In the future, this social safety net may include financial support for those who may be displaced
or have experienced wage depression as a result of automation. The concept of a universal or guar-
anteed basic income (a concept in which governments would provide unemployed citizens with a
guaranteed amount of money to live on) has been floated as a way to contend with the possibility of
a workless future brought about by increased reliance on automation. This money could be used to
maintain standards of living, while allowing individuals the ability to seek activities or passions that
provide for more social good and personal advancement.
The educational system will need to be modified to support impending automation transformation by
teaching the skills necessary for humans to excel in the new era. While there are still many unknowns

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