Thriving on Enterprise Data and Analytics · Thriving on Enterprise Data and Analytics 3 An exciting new wave of analytics-enabled business innovation is making it possible for organizations

Thriving on Enterprise Data and Analytics

Transforming to a Digital Enterprise


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Organizations that thrive on change use data and analytics as a competitive asset. They adapt quickly and predict trends by continuously curating and analyzing data and developing insights that drive new value. These organizations have a high “Analytics IQ,” and they will be the disruptors, not the disrupted, in the digital revolution. Those that successfully harvest vast troves of data can improve productivity, make faster and more accurate decisions, reduce costs, increase competitive advantage, discover new business models and innovations, and better engage customers, employees and partners.


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An exciting new wave of analytics-enabled business innovation is making it possible

for organizations to continuously deliver better products and services, improve

operations, better manage risks and develop new business models to stay relevant in

an age of disruption.

To cash in on the promise, organizations must incorporate data and analytics

into their front-line operations and at points of customer interaction. The creation

of embedded analytic solutions requires a modern, hybrid data management

architecture and an analytic platform to enable data-driven decision making and the

creation of analytics-empowered products and services.

Surviving and prospering in an age of disruption is the most pressing point of strategy

an organization will address in the coming years. Those that put analytics at the

core of their strategy and operations stand a good chance of benefiting from the

accelerated pace of change.

Analytics-enabled business modelsIT has long played a critical role in helping organizations deliver better products and

services, improve operations, better manage risks and develop new business models

to stay relevant. That’s still true. Core technologies such as cloud, mobility, modern

applications and networks continue to evolve. But IT’s impact on the enterprise is

raised to a whole new level when an organization introduces advanced analytics

and redoubles its focus on information. Advanced analytics embedded in each

interaction, transaction, information flow and process step is driving the next wave of

productivity and growth.


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A Spotlight on SecurityIn an age where security events can go

undetected for hundreds of days, security

professionals are looking for ways to

drastically reduce the risk window and

allow organizations to operate more

securely. To do this, they are rapidly

turning to analytics as a way to shine a

light on traditionally hidden data.

Analytics can help mature security

operations remain secure — for example, by

using the latest methods to measure and

monitor the behavior of users and other

entities, as well as changes in application

and device usage over time. They can

provide high-fidelity alerts to otherwise

overutilized security analysts, reducing

resource costs and ensuring that your

best analysts stay focused on the most

advanced cyberthreats to your business.

Big data analytics gives defenders two

bites of the cherry. While analytics

provide a way to detect known attack

patterns in a real-time signatureless

manner, the attackers move fast and alter

their techniques constantly. Fortunately,

big data provides the means to marry

fresh intelligence on attacks with the

latent intelligence stored in data lakes for

future use — giving organizations their

first sight of compromise.

Adversaries have recently taken to

exploiting algorithms to outwit human

analysts, producing attacks at such a rate

that they saturate resources. Embedding

analytics into security operations enables

defenders to algorithmically go toe-to-toe

with their adversaries.

Using analytics to fuse the various

sources of security information — such as

identity usage, threat and vulnerability

management, and external intelligence

— enhances the modern enterprise’s

situational awareness, enabling it to

operate securely at the pace and in the

markets that its industry requires.

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Advanced analytics complete the feedback loop between business strategy and IT

resources, creating a capability that is so central to the operation of the enterprise, it

actually becomes the business model. For example:

• As an early adopter of advanced analytics, the securities industry is now defined

by automated, algorithm-centric trading and artificial intelligence-based advisors

that outperform professional money managers.

• Insurance carriers, which famously clung to decades-old legacy hardware and

software, are wholeheartedly embracing analytics-driven systems to target

profitable market segments, speed claims payments, reduce fraud and increase the

number and degree of fully automated processes.

• Analytics derived from data generated by infrastructure sensors are helping

to optimize manufacturing processes throughout the value chain, enabling

organizations to manufacture smarter, faster and greener.

Advanced analytics are far more than just another name for business intelligence

3.0. The maturation of technologies such as machine learning, deep learning,

artificial intelligence and advanced neural networks, coupled with a boundless

supply of data and new ways of interacting with systems, is creating entirely new

capabilities and opportunities.

Just like the microscope, which reveals a world unseen, advanced analytics are fast

becoming “digital microscopes” that enable organizations to reveal hidden insights

and promptly act on them. The collection and analysis of huge amounts of diverse

data generated by humans, machines and enterprise applications are enabling a

better understanding of continuously changing organization ecosystems that’s not

possible by human intelligence and perception alone.

Advanced analytics are emerging as a crucial competitive weapon, taking advantage

of a wealth of unstructured and sensor data to provide predictive and prescriptive

analytics and business models, as well as rules to drive optimal behaviors across

complex enterprise ecosystems. Astute organizations are now harnessing advanced

analytics to pinpoint individual consumer preferences, to profitably upsell and cross-

sell, and to more efficiently develop popular new products and services.

Advanced analytics are also being used to reduce production and overhead costs,

and to mitigate risk throughout the product and service consumption life cycles.

Advanced analytics enable qualitative improvements with each iteration, leading

to automated and prescriptive solutions. This allows organizations to establish

continuous testing, learning and deployment of analytic models as the new normal.

By 2019, 25% of companies will use intelligent robots and robotic process automation (RPA) to provide actionable insight to factory managers and coordinate production planning and execution processes.Source: IDC FutureScape: Worldwide Operations Technology 2017 Predictions, Doc #AP40535016, November 2016


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Armed with their digital microscopes powered by advanced analytics, organizations

can accelerate discovery, testing and implementation of analytic solutions and

enable continuous productivity and operational improvements.

The future is bright …By 2020, successful organizations will be thriving on change and using data and

advanced analytics as competitive assets everywhere. They will adapt quickly and

predict trends by continuously discovering value from data and turning it into insight

to drive value. These organizations will be recognized as disruptors in the digital

revolution, capable of driving exponential organizational value and continuous

improvement. They will be branded as high Analytics IQ organizations.

High Analytics IQ organizations possess these attributes:

• Ability to discover, combine, analyze and share enterprise ecosystem data

• Well-defined processes for planning, developing and deploying analytic projects

• Enterprise-level understanding of the organizational needs driving analytics,

decision management and closed-loop, continuous improvement of analytic

models and decision making

• Consistent information and analytic insights delivered on demand across multiple

channels and devices

• Predictable outcomes delivered through analytics integrated into applications

and processes

• Organization-wide access to a broad range of data formats and sources regardless

of location

• Informed, augmented and automated decision-making models enabled by a flexible

analytic platform powered by business intelligence (BI), predictive analytics, machine

learning, deep learning, robotic process automation and cognitive computing

• Well-established policies, procedures, processes and controls for managing

analytic models as assets

• Executive and organizational stakeholder sponsorship on all levels for analytic

programs, trust in results of analytic outputs and active pursuit of new insights

• Analytics- and technology-savvy organization leaders

• End-to-end, seamlessly integrated information governance

By 2019, APIs will be the primary mechanism to connect data, algorithms and decision services distributed across digital economy value chains, clouds and data centers.Source: IDC FutureScape: Worldwide Analytics, Cognitive/AI, and Big Data 2017 Predictions, Doc #US41866016, November 2016


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Analytics: The LEF PerspectiveArguments rage about what is hype and

what isn’t, the impact of digital technology

on gross domestic product, whether the

effects of mobile devices on society are

helpful or damaging, and so on; but what

seems incontrovertible, in research by

DXC Technology’s Leading Edge Forum

(LEF), is the steady penetration of digital

technology in the world.

Whether it’s the amount of data storage,

volume of internet traffic, number of

people with smart devices, number of

devices per person or proportion of

income spent on digital stuff, the digital

world continues to grow — and grow in

importance — apace. Figure 1 shows

how fast and how far digital activities

are increasing, and with every physical

object potentially becoming both a

sensor and controllable, there is a real

sense that “we ain’t seen nothing yet.”

The availability of processing power,

data, analytics and intelligence

everywhere, in every device and object;

the capability to sense and control

almost everything in the world — these

changes are having deep impacts on

business, community, government,

sports, the arts and more.

The biggest companies in the world

are increasingly asset-light. An article

in the January 2016 edition of Fortune

magazine noted that Wal-Mart and

Amazon were valued at $250 billion

each, but Wal-Mart employed $154

billion of capital to create that value,

whereas Amazon, increasingly a

platform company, used only $35 billion.

Meanwhile, the global geopolitical

landscape is shifting, most notably

with the growing role of China in the

global economy. Interesting questions

are arising about the role of states

versus companies, and blockchain

technologies make us question the

need for central authority in some

areas of our work and lives.

All of this has an impact on the types

of skill — and how much of each skill —

businesses need. New digital skills — such

as digital anthropology, some forms of

data science and machine learning —

are becoming important, but they are

scarce. There is also an increasingly real

issue of software and hardware robots

replacing conventional human skills in

some blue- and white-collar roles.

At the same time as the effects of the

digital world on the human tribe are

changing, with different demographics

becoming more accepting of and

comfortable with digital channels,

there are also deeper changes in how

we interact with one another and how

we use our time, mediated by digital

capabilities. In short, almost everything

is changing, from the very macro to

the very micro, in the face of digital

opportunities and threats.

In One Minute

Total mobile

data traffic

Terabytes/minute

U.S. digital

media users

Millions of

minutes viewed

Tweets sent/

minute

Apps

downloaded/

minuteUS$

Million sales/minute on

single day

Messages

received/minute

Hours of new

video uploaded/

minute

4

2,16047,000

100,000

277,000

342,000

0.8

37

58

93

1.21.5

38,000

51,000

4,528

8,9516.5

9.9

100300

400

Figure 1 2013 2014 2015

Data prepared by Martin Lee, DXC. Sources: Domo, Statista, Ericsson, Comscore


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High Analytics IQ organizations in 2020 and beyond will have strategy, culture and

continuous-improvement processes in place to enable them to identify and develop

new digital business models to better serve their customers and remain competitive.

… But there are challengesThat all sounds great. Many organizations are experimenting, and yet many are

struggling to see the impact of these analytic projects across the enterprise. As

analytics begin to create real change, organizations are realizing they need to move

from ad hoc analytic environments — where data scientists and business analysts

experiment, and analytics are simply showcased for their potential — to industrial-

scale analytics that penetrate the organization’s processes to a greater depth and

deliver proven strategic and incremental benefits.

For example, in ad hoc analytic environments, predictive analytic models are often

developed and managed on analysts’ desktops with poor documentation, versioning,

traceability, archiving and content management. Analytic models are often hard-

coded into IT scripts that are not easily extractable for versioning and refresh. Putting

these models into production often requires rewriting the model from its original

source code so it can be embedded into organizational applications, with frequent

loss of traceability.

This chasm between ad hoc analytic projects and organizational impact is driven less

by the quality of the analytic methods than by the inherent organizational ecosystem,

cultural resistance to change, and suboptimal processes that support integration of

analytic insights into enterprise operations and applications. It is no longer sufficient

to produce robust analytics; analytics need to be operationalized as well.

Organizations with a high Analytics IQ understand these challenges and have taken

steps to cross this chasm from ad hoc to operational analytics.

At the same time, companies such as Apple, Amazon and Google are disrupting

established markets by developing new product categories and serving underserved

customer segments. Think of Apple assembling an autonomous-vehicle organization,

Tesla moving into power supplies, or Amazon moving into the IT cloud business.

Aggressive, asset-light and agile startups with analytics-driven business operations

are quickly taking revenue and growth out of traditional organizations.

To alleviate these existential threats, organizational leaders today must not only

respond to changing customer demands, but also develop strategies and make

investments to develop new business models, optimize their processes to stay

competitive and ensure that their organizations survive in the age of disruption. This

is no small feat for long-established organizations burdened by monolithic data and

application architectures and systems that have grown organically over many years

without an overarching architectural blueprint. For these organizations, finding and

deploying innovative analytics and big data technologies is a lengthy and complex

process, prone to mistakes.

What makes it even harder is that one organization’s architectural blueprint won’t

necessarily be the right fit for other organizations, even in the same industry or in the

public sector.


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Raising your organization’s Analytics IQ is a journeyThe journey to a higher Analytics IQ begins by developing a strategy that allows an

organization to best leverage the analytics and technology advances known today,

as well as those yet to come. An analytics-powered digital transformation strategy

comprises three essential steps: 1) accelerate the digital transformation agenda; 2)

build a data-centric foundation; 3) operationalize analytics across the organization.

1. Accelerate the digital transformation agenda

Rapid innovation and productivity breakthroughs require an accelerated digital

transformation strategy that melds people, business processes, advanced analytics,

artificial intelligence and new human/machine interaction technologies. To be

successful, stakeholders across the entire organization must commit to enhancing

analytics insight-driven decision-making capabilities, leading cultural changes, and

applying systematic approaches for optimizing their information models — focusing

on the value that information and analytics can deliver in business differentiation,

productivity and growth. By creating their own information value domain maps,

organizations can start managing their information assets based on their value,

governance and privacy requirements, location and system distribution, timeliness,

velocity and usage characteristics. Information and systems not directly contributing

to business growth can be cost-optimized as highly standardized commodity services

— that is, as a utility. With the cost savings, organizations can use those freed

funds to deploy advanced analytics technologies — and to operationalize analytics,

providing actionable analytic insights to front-line applications and processes.

Advances in analytics can help not only in achieving productivity breakthroughs, but

also in identifying cost-saving opportunities, value and return on investment from

new products and services, and value from operationalizing analytic models and

automating decision making.

By 2020, 66% of enterprises will implement advanced classification solutions to automate access, retention, and disposition of unstructured content, making it more useful for analytics.Source: IDC FutureScape: Worldwide Analytics, Cognitive/AI, and Big Data 2017 Predictions, Doc #US41866016, November 2016


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Get to Know Industrial Machine LearningSo, you’ve earned a spot at the head of your

company’s analytics initiative. Now what?

The options for moving ahead can be

overwhelming, and there are some

definite pitfalls to avoid. You run the risk

of prioritizing the wrong efforts, being led

by technology rather than by business

goals, being ignored by the enterprise and

spending too much time solving the wrong

problem. There is a clear path through

— a set of best practices for building

machine learning solutions that will make

a difference to your enterprise. At DXC, we

call it Industrial Machine Learning (IML).

The term “industrial” refers to the fact

that IML is about moving past small, siloed

implementations of machine learning into

the kinds of deployments capable of putting

analytics everywhere in the enterprise.

Here’s IML in a nutshell:

1. Build a data strategy

2. Pick a data story

3. Build a data discovery environment

4. Run agile experiments

Without a strategy, you’re subject to the

tyranny of action. You’re likely to find

yourself taking on an analytics initiative

simply because that’s what others are

doing. It may sound reasonable in the

beginning, but will eventually lead to a

series of cookie-cutter projects. Start

instead with maps of your business priorities

and current speeds to insight. Use those

maps to select your most important data

stories. Data stories describe the purpose of

the analytics initiative in the language of the

business, rather than the technology.

With a data story selected, you’re ready to

build a data discovery environment. It’s just

a platform designed to access data, ingest

and clean it, run automated experiments

and generate insight. We call it a data

discovery environment because we expect

it to be a place for the enterprise to plug

in a variety of data sources and receive

continuous streams of actionable insights.

With a data discovery environment in place,

you can begin the task of transforming

the enterprise. But keep in mind that the

nature of enterprise-scale analytics is

experimental. You won’t know ahead of time

whether the problem you’ve chosen is truly

worth solving. Avoid biting off an analytics

transformation all at once. Instead, run

small experiments that make it easy for

you to recover from mistakes. Create a

hypothesis about what you think might

make a real difference (here’s where having

a real data strategy comes in handy). Test

those hypotheses using small experiments.

Learn and adjust as you go.

The basic practices of IML are where the

rubber hits the road if you want to master

advanced analytics on an enterprise scale.

The amount of new data being created is

staggering. And most industries have only

scratched the surface in capturing this new

source of business value. Now is the time

to establish and increase your company’s

overall Analytics IQ — we’ve even built an

assessment designed to help you get started.

Find out more at www.dxc.technology/

analytics_iq.

— Jerry Overton, Data Scientist, Senior

Principal, DXC Technology

Data stories DXC has built as part of the IML offering

All Banking and Capital Markets

Energy and Technology

Insurance

Retail

Healthcare

Manufacturing

• Perform real-time anomaly detection and preventive maintenance

• Plan and optimize asset maintenance

• Understand customer segments

• Predict trade risk

• Personalize financial advice

• Anticipate driver behaviors that will lead to loss

• Predict outages and degraded performance of IT infrastructure

• Predict service support requests for IT infrastructure

• Discover the root cause of claims and warranty labor requests

• Predict the incidence of new claims and warranty labor requests

• Perform cluster analysis and market segmentation

• Model and predict buyer propensity

• Optimize logistics and supply chain

• Predict and reduce hospital lengths of stay

• Use environmental factors to predict emergency room admissions

• Predict community health risk and create outreach plans

• Perform real-time anomaly detection and preventive maintenance

• Predict equipment performance

• Simulate and predict flaws, costs and performance

https://marketing.itcs.hpe.com/?assessment=analytics&cname=DXC.com#/assessment-start

http://www.dxc.technology/analytics_iq

http://www.dxc.technology/analytics_iq


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To take advantage of the power of digital transformation and the innovation it

enables, organizations need to:

• Create analytic capabilities to deliver new digital business models that can either

disrupt the marketplace or defend their organization against disruption

• Craft innovative and intuitive service models and experiences along a customer journey

• Deliver a fundamental operational process transformation that results in meeting or

exceeding performance goals

Organizations should define their long-term objectives, clearly understand where and

how new value will be created, and design their digital journey maps.

2. Build a data-centric foundation

The next step is to build a data-centric foundation that can scale with growing

organizational needs, enable innovation, increase agility, encompass ecosystem

data, increase predictability, improve forecasting accuracy, detect new behavior

patterns and deliver information insights in context to processes and applications.

Companies such as Apple, Amazon, Google, Uber and Airbnb have shown how to

build such data-centric foundations and disrupt traditional markets.

DXC Technology recommends building a data-centric foundation by adopting

the Hybrid Data Management (HDM) approach and reference architecture, and

implementing the operational analytic platforms based on it:

Hybrid Data Management (HDM). Forward-looking organizations take a modern

approach to data and organization intelligence — one that enables them to gain data-

driven insights from new kinds and higher volumes of data — and to transform that

information into tangible enterprise results such as optimized operations, new business

models, and data-driven products and services. HDM is the foundation of a modern

approach to BI and involves optimizing traditional BI and data warehousing, blending

in big data analytics and embedding prescriptive analytics from both sides into

operations and business processes. HDM provides a strategic direction for instituting

industrial-scale analytics integrated into organizational processes and systems that

leverage all data and enable organizations to become data-driven and agile.

Hybrid Data Management Reference Architecture (HDM-RA). An organization

must establish the HDM-RA as a foundation for building technical design blueprints

for BI and analytic solutions. In essence, HDM-RA is an end-to-end architecture, a

selection of recommended technologies and implementation roadmaps for each

functional domain, and use-case-based design patterns to deliver HDM solutions

in as-a-service, on-premises and managed deployment models with security and

information governance that meets compliance and regulatory requirements.

Operational Analytic Platform. An operational analytic platform is an integrated

and complete infrastructure, software and services solution based on the HDM-

RA. It manages data and analytic models and generates business analytics to

empower decision makers who need timely information to do their jobs. It analyzes

all organization-relevant data from any source, in any format, and from any

location — with extreme speed, security and scale. It also gives organizations the

flexibility to move seamlessly between cloud and on-premises deployments to meet

dynamic requirements.


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The operational analytic platform enables augmented and automated decision

making through its standard components, including predictive analytics, artificial

intelligence (including machine and deep learning), cognitive computing and robotic

process automation.

While HDM-RA is a logical construct, the operational analytic platform is its

fit-for-purpose physical instantiation based on a design pattern consistent with

the specific use-case requirements it supports. For example, the operational

analytic platform can be instantiated to serve one end-to-end organizational

process at a time, eliminating the organizational, technical and process

complexities required to serve all organizational units and processes at the

same time. A predictive-maintenance operational analytic platform that serves

a manufacturing operations process will differ from the operational analytic

platform that serves social media analytics and engagement processes, even

though both platforms are based on the same HDM-RA.

3. Operationalize analytics across the organization

DXC defines operational analytics as the interoperation of multiple disciplines that

support the seamless flow of data, from initial analytic discovery to embedding

predictive analytics into organizational operations, applications and machines. The

impact of these analytics is then measured, monitored and further analyzed to circle

back to new analytic discoveries in a continuous improvement loop, much like a fully

matured industrial process.

Operational analytics builds on HDM, HDM-RA and the operational analytic platform

to help organizations implement industrial-strength analytics as a foundation of their

digital transformation.

Organizations that wish to raise their Analytics IQ and gain a competitive advantage

through analytics should follow these core operational analytics process steps:

Data discovery includes the data discovery environment, methods, technologies and

processes to support rapid self-service data sharing, analytic experimentation and

generation of information insights.

Analytic production and management focuses on the processes required to

support rigorous treatment and ongoing management of analytic models and

analytic intellectual property as competitive assets.

Decision management provides a clear understanding of, and access to, the

information needed to make decisions at the right time, in the right place and in the

right format.

Application integration incorporates analytic models into enterprise applications,

including customer relationship management (CRM), enterprise resource planning

(ERP), marketing automation, financial systems and more.

Information delivery of relevant and timely analytic information to the right users,

at the right time and in the right format is enabled by self-service analytics and data

preparation. This improves the ease and speed with which organizations can visualize

and uncover insights for better decision making.


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Analytic governance is the set of multidisciplinary structures, policies, procedures,

processes and controls for managing information at an enterprise level to support an

organization’s regulatory, legal, risk, environmental and operational requirements.

Analytic culture is key, as crossing the chasm from ad hoc analytic projects to analytic

models integrated into front-line operations requires a cultural shift. Merely having a

strong team of data scientists and a great technology platform will not make an impact

unless the overall organization also understands the benefits of analytics and embraces

the change management required to implement analytically driven decisions.

Thrive on changeRaising an organization’s Analytics IQ enables it to thrive on change. It allows the

organization to use data and analytics as competitive assets, adapt quickly and

predict trends by continuously discovering value from data and turning it into insight.

Ultimately, it helps the organization become a disruptor in the digital revolution.

Considering the impact that using advanced analytics can have on an

organization’s most critical goals, it doesn’t pay to take a wait-and-see approach

to determine whether this is a capability worth having. The fact is, organizations

that raise their Analytics IQ are far more likely to successfully manage a chaotic,

dynamic business environment.

Those that choose to wait are far more likely to find themselves with a very different

fate, destined to become the latest members of the ignominious “Whatever happened

to …?” club.

By 2020, 30% of Global 1000 CEOs will be strategically planning significant resource shifts from human to intelligent systems, cutting across multiple functions and processes.Source: IDC FutureScape: Worldwide Intelligent ERP 2017 Predictions, Doc #US41870215, November 2016


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How DXC Technology Can HelpDXC Technology is a trusted partner

helping organizations to realize their

highest Analytics IQ.

DXC Analytics offers a complete

portfolio of analytics services to rapidly

provide insights and accelerate the

digital transformation journey. We help

customers thrive on change with a full

suite of services — from advisory services

to technology and industry solutions.

Our robust partner network allows

organizations to build and leverage the

advanced analytics solutions that drive

desired outcomes.

DXC Analytics consultants

advise, support and manage the

transformation, unlocking the insights

needed to deliver and manage

advanced analytic initiatives.

DXC Analytics services and offerings

are comprehensive and take into

account an organization’s technical and

financial goals, as well as its current

readiness or maturity level in analytics,

IT culture, operational practices and

compliance requirements.

Learn more at www.dxc.technology/

analytics.

http://www.dxc.technlology/analytics

http://www.dxc.technlology/analytics


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Dragan Rakovich, DXC Technology’s chief technology officer

for Analytics, leads the company’s analytics technology and

innovation strategy. Dragan brings strategic advice and

thought leadership to customers in actionable analytics,

business intelligence, machine learning, Internet of Things (IoT)

and analytics platform domains to create advanced analytic

solutions. Prior to this role, he served as Hewlett Packard

Enterprise Services CTO for Analytics and Data Management.

Dragan has more than 20 years of experience in analytics,

business intelligence, management consulting, solution

delivery, enterprise architectures and software engineering.

[email protected]

Martin Risau, DXC Technology’s senior vice president and

general manager of Analytics, is responsible for ensuring that

the company’s analytics offerings create value for customers

through advanced solutions and services that leverage industry

and technology expertise. He creates a culture of analytics

by focusing on actionable analytics, business intelligence,

machine learning, IoT and analytics platforms. Prior to this role,

Martin served as Hewlett Packard Enterprise Services practice

lead, Analytics and Data Management.

[email protected]

Authors

DXC Technology’s ResearchNetwork contributed to this paper.

mailto:[email protected]

mailto:[email protected]

© Copyright 2017 DXC Technology Company

Learn more at www.dxc.technology/digital_enterprise

www.dxc.technology

DXC Technology (DXC: NYSE) is the world’s leading independent, end-to-end IT services company, helping clients harness the power of innovation to thrive on change. Created by the merger of CSC and the Enterprise Services business of Hewlett Packard Enterprise, DXC Technology serves nearly 6,000 private and public sector clients across 70 countries. The company’s technology independence, global talent and extensive partner network combine to deliver powerful next-generation IT services and solutions. DXC Technology is recognized among the best corporate citizens globally. For more information, visit dxc.technology.

http://www.dxc.technology/digital_enterprise

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Thriving on Enterprise Data and Analytics · Thriving on Enterprise Data and Analytics 3 An exciting new wave of analytics-enabled business innovation is making it possible for organizations