november 2015 data service management: leveraging big …security & regulatory compliance...

Point of View

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Our point of view

How we can help from strategy through execution

Our relevant approach

Current trends about data

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Competitive intelligence

AppendicesCredentials Contacts

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Data Service Management: Leveraging Big Data as a Strategic Advantage

November 2015

PwC

November 2015

Current trends about data

Point of view • Data service management: leverage Big Data as a strategic advantage

PwC

November 2015

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Section 1 – Current trends about data

Over 80% of company data consists of unstructured data

Due to the lack of analytical skills companies analyze

only 12% of data

Did you know that…

Every year data volumes explode by

40%

Big Data investments will account for nearly

$40 billionin 2015 alone

Poor data can cost businesses

20%-35% of their operating revenue

PwC

November 2015

Big Data: an overview

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01 Big Data drivers

• Analytics is the new business driver: client segmentation,product innovation, predictive analytics…

• Growing demands of real time insight: business key driver,monitoring,….

• Coming data complexity is what creates change: volume,velocity, variety and veracity

• Data storage costs: centralized and unique database

02 Big Data challenges

• Variety: the huge variation in the types and sources of Big Data• Security: securing the organization and its customers data• Technology: limitation of the traditional data base • Data management: 80% of the information created and used

by an enterprise is unstructured data content• Culture/ Changing the organization: taming/ mastering

Big Data & Analytics and developing culture and new process within the firm

Average data volume stored

In 1000+ people companies

875

870

801

536

370

319278

231

1,931

Construction

Learning

Services

Wholesale

Healthcare

Bank

1,792

Retail 1,038

Government

Transport

Insurance

Industry

Telecom

1,507

1,312

Public services

InterpersonalElectronic communications, mails, social networks…

Man/MachineDigital data, credit card archives, browser history…

Inter-machineCaptors, GPS, cameras

Volume Variety

250BILLIONSOf mails senta day

50MILLIONSTweets posteda day

165MILLIONSBank transactions in Eurozonea day

Velocity

03 Big Data needs

• Need for advanced analytics: an ever finer market/customer segmentation to compete

• Visualization: Data visualization as support of insight allows Big Data to unleash its true impact

• Increase of the demand of data scientist and related positions

• Increase the data storage capacities

Source: Gartner 2015; Internet of Things World Forum (IoTWF) 2015

Internet of things is 50 billion connections by 2020, 500 billion connections by 2030,

data will be everywhereSource: CISCO

PwC

November 2015

Big Data trends to watch today


Analytic AppsAnalytic Apps are on-demand. They deliver immediate

value on strategic job, especially data visualization as support of insight allows Big Data to unleash its

true impact (dashboard, decision support, surveillance, supply chain analytics…).

Data scientist & beyondThere are 4.4 million jobs for data scientists

(and related titles) worldwide in 2015, 1.9 million in the US alone. One data science

job creates another three non-IT jobs, about some 13 million jobs altogether.

Data governanceThe existing data governance functions are not up to date with the data growing demand.A strong and accurate governance policies into Big Data systems is mandatory.

Data as a ServiceAt the moment, companies have tons of internal data that can be used for internal business purpose or that can be sold, on demand to competitors for benchmark purposes. DaaS can enrich the business model by giving more value to their own data both internally and externally and increase revenue.

Real-time insightOrganizations are looking for real-time insight into their business-critical processes. This puts demands on designing data lakes or Big Data warehouse for responsive, scalable and on-demand analysis.

Security & regulatory compliance considerations

To manage Big Data effectively, it needs to be secure and compliant with regulatory requirements at all times. Protecting a

vast and growing volume of critical information and being able to search and

analyze it to detect potential threats is key to leverage Big Data for business.

NOSQL databasesNext generation databases mostly addressing some of the

points: being non-relational, distributed, open-source and horizontally scalable. NoSQL is increasingly considered as

a viable alternative to relational databases offering more flexibility, limiting usual costs and delays.

Security &

regulatory

Analytic Apps

Cloud Big Data

challenges

The internet of

things

NOSQL Databases

Data as aService

Real-timeinsight

Data governance

Big Data &

Analytics

Data scientist &

beyond

3

Cloud Big Data challengesCompared to an in-house datacenter, the cloud eliminates large upfront IT investments, lets businesses easily scale out infrastructure, while paying only for the capacity they use. The cloud can allow companies to quickly launch new products and services (x-aaS, i.e: Analytics aaS;PaaS; IaaS; …) that enable them to operate more efficiently.


The internet of thingsThe internet of things revolves around increased machine-to-machine communication; it’s built on cloud computing and networks of data-gathering sensors; it’s mobile, virtual, and instantaneous connection; and they say it’s going to make everything in our lives from streetlights to seaports “smart.”

PwC

November 2015

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Data sources and data types are multiple and managed in silos ……however they are more and more at the heart of business issues

Business has created software, software has created data, and now business lines must reconcile different data sources considered as raw materials today.

PwC

November 2015

How did we get to Big Data ?

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The emergence of new technologies, application and social phenomena lead us to the “data explosion”.Following are the major milestones in the resent history of sizing data volume in the evolution of “Big Data”.

Growth in the number of information and beginning of data exploitation to optimize business decisions

Design of a new architecture for the

generation of reports and business analysis

by Barry Devlin & Paul Murphy, IBM. What will be called “Data

Warehousing” later

Introduction of term “Business

Intelligence”. It refers to the

methods used to help making

strategic decisions, based on reports and data analysis

First report, published by CrystalReports, combining multiple sources of information

Creation of Hadoop,

considered as a solution to

Big Data

Explosion of the World Wide Web.

It becomes a challenge in

storage & data management

Version 1.0.0 of Apache Hadoop.

First implementations of data lakes

First apparition of the term “Big Data”, in a article written by NASA’s researchers, referring to the explosion of volume data

Version 2.0.0 of Apache Hadoop,

with YARN’s apparition.

The latest release appeared on the

April 21st, 2015

Introduction of new terms

Facts/studies/reports

New software/release available

Creation of Yahoo!

AppliancesTeradata decides

to compete as a data warehouse

vendor, followed by Oracle Exadataand HP Vertica

Apparition of Map Reduce. It was invented

by Google.

1980s

1985 1989

1990s

1992

1994

2004

1997

2006

2008

27 December 2011

25 August 2013

Sources: Gartner 2013; http://www.technologyreview.com: The-big-data-conundrum-how-to-define-it

http://www.technologyreview.com/

PwC

November 2015

Our point of view

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PwC

November 2015

Our clients say …

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Section 2 – Our point of view

I want to developnew products and services quickly that uniquely address customer and market needs.

I want to better understand my customers and markets – their needs, preferences, behaviors and loyalties.

Our organization wants to improvetimeliness, relevance and impact of decision makingWe want to unleash the potential of employees by getting rid of shadow IT.

We want to bringinsight to the datawe already have.

I want to better model and predict market, consumer, economic and demandconsiderations and to deliver “intelligence in the moment”.

PwC

November 2015

We believe Big Data and advanced analytics will enable information advantage for companies

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PwC perspectives

Big Data & Analytics at inflection point. . .

• Banks face age-old questions along the customer journey

• Big Data & related advanced analytics improve decisions, customer and business impact

. . . Comes to life through decision use cases. . .

• Focus on decision domains and use cases drives collaboration across organizational units

• Frameworks then drive decisions around data, analytics and technology

. . . Working together in an ecosystem internally and externally . . .

• Interlocking of data & technology stacks can coalesce around the decision domains

• Ecosystem extends to optimizing mix across business areas and external parties or providers

. . . As illustrated in a case study. . .

• Focus on decision domains and use cases creates the proof points

• Enabling capabilities requires hybrid of old and new technology components

. . . And informing a target operating model that continuously links strategy & execution

• New capabilities are typically required drawn out by executing ‘pilot and prove’ approach

• Organizational challenges also drawn out and addressed by executing ‘pilot and prove’ approach

PwC

November 2015

Making better big decisions means using techniques that blend ‘art’ and ‘science’

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Linking decisions to shareholder value

1

Mastering evaluation of strategic alternatives with business impacts

2

Applying a value & results lens3

Adopting a structured test & learn approach

4

Art = Leadership Judgment

Science =Analytics

Excellence

&

Art & science in decision making

"“It takes a combination of management experience, management insight into the market, in addition to the data, and I don’t see that going away any time soon.”

- David Thompson, Chief Information Officer, Western Union

PwC

November 2015

Delivering information advantage for clients requires pragmatic experience, tools and proven execution success in your organization

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Clients needs to get key elements right... ...and PwC delivers on those elements

Actionable approach with emphasis on operationalanalytics to turn insights into results

Pragmatic experience, frameworks, and execution focus (vs theoretical) working side by side with the client

Overcome barriers to update data and analytics capabilities across business units and corporate functions converged on common vision and priorities

‘Pilot and prove’ approach that exercises full stack of the solution while coalescing business and corporate organizational units

Business-led, business proven mindset– phased & iterative to try/learn/burn/adopt with executive buy-in

PwC’s Analytics Hub and Analytics Accelerators to disrupt our own business model – we ‘eat our own cooking’

Quickly translate business “Decision Use Cases” into the “Decision Use Case Specs” required to deliver insights and results

Proven tools, maturity models and accelerators, such as PwC’s Decision Health Check TM and Information Maturity Diagnostic TM to drive speed and sophistication

Bringing together experienced analytics specialists are integrated with our deep industry practitioners

Strong track record of delivering data-centric analytics projects, including :

Social listening/customer complaints analytics pilotBehavioral lending strategyMobile wallet adoption and usage simulation

Understand the “art of possible” with awareness and mining of “what’s out there”, test, and then move on with an objective lens

Information base of leading and emerging providersacross data, offers, fintech and analytics areas, yet without a ‘dog in the hunt’ to bias our views

Combination of institutional knowledge of your people, processes, systems, and culture and proven ability to deliver effective results

Proven success on large scale, complex global projects: Customer Risk Scoring Execution 2.0/Org Simplification CCAR/Full Suite/Resolution Plan

PwC

November 2015

Why using more and more data ?Internal and external data sources directs your strategy development in two main ways…

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01

02

03

04

Improve decision-making

Enable predictive analysis

Optimize customer relationship

Reduce risks

Big Data will transform

your business

model

A Big Data project is not only an IT project, it links business needs to mathematical models to obtain a wide range and type of data.

A business model that manages and capitalizes on high data quality is better able to anticipate, react and leverage emerging opportunities. It helps to organize the wealth of information and identify what can truly impact and improve business.

Big Data will transform your business model by:• Improving decision-making• Enabling predictive analysis• Optimizing customer relationship• Reducing risks

Information: burden or benefit?

“Information is a source of learning. However, unless it is organized, processed and available to the right people in a format for decisions making, it is a burden, not a benefit.”

Dr. William G. Pollard (1911 – 1989)American Nuclear Physicist & Research Scientist on the Manhattan Project

Data analytics is helping organizations to build offensive business strategies such as managing churn rate, optimizing prices and costs, calculating customers lifetime values and defining the next best marketing action, etc.

Offensive Way

Using Big Data enables also defensive business strategies when allowing organizations to respond to regulatory requirements, to optimize governance, risk and compliance solutions, and to develop testing solutions helping them to avoid sanctions, etc.

Defensive Way

PwC

November 2015

Enhance your competitive advantage and predict the future

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01 Improve decision-making

More and more company leaders describe their biggest decisions are more likely to be delayed, or the result of taking advantage of a particular opportunity, rather than being scheduled. They rely on their feelings and experience to guide them in strategic decision-making.

Moreover, during last years, the number of people involved in decision making has increased. Decision rights need to be clearly defined to minimize delays. This makes essential having access to proven analytics based on high integrity data.

Management decisions are extremely dependent on market conditions, should be taken at the right time and done with confidence, clarity, and agility.

We believe that transforming data into insights delivers when and where they’re needed to make and implement better strategic and operational decisions.

02 Enable predictive analysis

Combining a large volume of macroeconomic data, consumer data, and technology advancement data facilitate the potential market prediction ability.

Predictive analytics are an smart way for companies to identify most promising prospects sales and on which customers to focus with a thinner customer segmentation.

Data and Insights allow building predictive models based on consideration, market pricing, competitor strategies, and other third-party data.

Models should be flexible enough to rapidly evolve in accordance with new economic conditions, new competition in the market, or changes in products prices.

Evaluating different scenarios and assumptions allow companies to identify and optimize pricing strategy, maximize profits and improve return on investments.

A fundamental change in mindset is mandatory to take advantage the new Insights generated by the use of data.

Turn decision making into a competitive advantage for your

organization

Shift your business strategy from a “why did it happen?” approach to a

“what will happen?” strategy

PwC

November 2015

Retain, develop your client portfolio and manage your risks

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03 Optimize customer relationship

Companies have almost no idea which of their new products will end up being popular with consumers. Despite heavy investments in innovation, Chief Innovation Officers and efficient R&D, new products are not always successful.

The fast-moving and complex market environment, people more and more connected are some reasons for failures.

The problem is that companies don’t have a clear perception of which combination of features, packaging, prices, and even labeling will persuade consumers to purchase.

They need to change their organizational model so that the innovation function collaborates more directly with marketing, sales, and the supply chain during product development.

Insights from Big Data help building trust among consumers and improving the overall customer-experience strategy. They enhance existing market offers through better understanding of customers and the effectiveness of marketing and sales activities.

Big Data benefits can be a decrease in churn rate based on clients interactions or new client segmentation.

04 Reduce risks

Currently data in organizations are not exploited at their own value. This is due to the complex history of many companies, from merger to turn-over to the high level of complexity some organizations have reached.

The level of inconsistent data relates to a lack of a mature and effective information management strategy.

These data issues can increase exposure to various risks, whether they are compliance risks, financial risks, strategic risks, operational risks …

Companies are facing several regulatory requirements and need to deliver accurate reporting in short times.

Ensuring data quality, completeness, validity, consistency, timeliness accuracy and auditability is necessary for companies to respond to reporting requirements.

Moreover, a centralized information management strategy combined to the use of third-party data such as social media helps companies in fraud detection, exposure to risks decrease as a consequence.

Technology and new ways of thinking are necessary to keep up

with your customer demands

Improving data quality will help you to detect fraud, enhance your operational effectiveness, and

respond to regulatory requirements

PwC

November 2015

The new solution delivering benefits and mitigating risks

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• Deeper insights on data (viewership, usage, defects) previously unknown or unquantifiable

• Ability to draw correlations between large disparate data sets

• Ability to draw insight from customer behavior (digital products, audience measurement) to cross-sell / up-sell

Benefits delivered

• Wasted cycles and resources caused by lack of upfront definition, evaluation and prioritization of use cases

• Gap between expectations from the business and Big Data capabilities due to lack of clarity around Big Data capabilities

• Architectural debt due to changing technologies

Risks mitigated

Big Data environment

360 View of the Customer

• Enhanced base of customer data to facilitate analysis

• Draw deeper insights into churn and customer profitability

• Opportunity to reduce dependency on 3rd party data providers and data duplication across enterprise

• Enhance current capabilities for segmenting subscribers

• Ability to understand and draw insight from all customer interactions (i.e. promotions, call center, etc.)

• Contention of customer relationship ownership and responsibility of data needs (governance)

• Design and implementation issues resulting in poor customer information and subsequent lost revenue / lower NPS

• Data quality issues

• Integrated transactional data stores (demographics/segmentation, viewership / behavior)

• Enhance event management and reduce costs (storage)

• Enable high data volume transactional data on cost effective platform

• Provide relevant historical view of the customer across channels

• Longitudinally track customers over time

• Unclear requirements and inefficient design could lead to architectural issues in the future

• Lack of skills in house to stand up and manage environment

• Sub-optimal availability of systemsCustomer Hub

PwC

November 2015

Focus on one of the fundamentals of Big Data: data lakes !A data lake is a massive repository designed to store and leave raw data, of any form,

accessible for yet unknown analysis Data lakes are less expensive than

data warehousing, but effective use requires special tools and a

high level of expertise for the lead scouts.

Eliminating data silos makes the discovery of unknown

possible.

Data scientist explorers can create and share views of the data, making it possible for others to use the lake themselves.

With more use comes more shared clues about the data and a greater ability to expand and elaborate on initial findings.

Four main characteristics about data lakes Size and low cost: Data lakes are scalable and

less expensive to set up and maintain that data warehouses

Fidelity: Data lakes preserve data in its original form

Ease of accessibility: Whether structured or unstructured, data is loaded and stored with little or no alteration from data owners, which eliminates internal political or technical barriers to increased data sharing

Late of binding: Hadoop lends itself to flexible, task-oriented structuring and does not require up-front data models

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PwC

November 2015

How do data lakes differ from typical data warehouse?

Data warehouse Data lake

Storage costHigh costs are limiting the sizeX0 000 $ / To

Hadoop technologies reduce licensing and hardware costsX00 $ / To

PerformancesEfficient indexing, designed to handle concurrent users performing optimized queries

Batch processing at large scaleReduce processing costs by bringing analytics near to data

Use casesBusiness intelligence tasks“Specific questions identified at the design time”

Analytics and data discovery“Yet unknown analysis”

Schema anddata

“Schema-on-write”Data model is defined before data is storedLimited to structured data

“Schema-on-read”. Eases data capture but requires work when accessing dataStores any type of data : machine-generated, social networks, medias, …

Time to market

Data warehousing team needs to model the data before giving access to business users, which canresult in long development cycles.

Data lakes give business users immediate access to all data. Allows flexibility and short delivery cycles which is required in a dynamic market economy.

Data qualityData warehouse is designed to clean data beforestorage, and to enforce consistency

Raw data is stored in the data lake.Yet it can be used as an ETL environment as it is able to store and process data at low cost

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While data warehouses follow a “schema-on-write” approach, data lakes are considered being a “schema-on-read”: data can be called for analysis as needed, ad hoc contexts are created on the fly, addressing

business needs from different parts of the organization. This requires a new mindset to firstly make sense of the data stored: in other words, it is about making it fundable, understandable and possible to be

combine with other data.

PwC

November 2015

The key success factors for building a data lake

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Because enterprises do not have the same priorities, the data lake has to be adjusted according to the specific industry. The tools used should also be adapted to enable independence from IT department, so that business users can obtain and analyze the data they want when they need it.

Domain specifics

Being added to existing enterprise data management systems, data lakes have to meld into it, accept and support its tools and methods. It is the only way to create the synergy of promised capabilities.

Use of multiple tools and products

As a data lake incorporates different types of data, the technology stack used has to natively support structured, semi-structured and unstructured data types.

Interface with the existing

environment

PwC

November 2015

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Leveraging your data as a strategic advantage


“Data is at the heart of business strategy”

Customer experience is improved (360° view)

Organizations focus more and more on core business and delegate support functions

There is a lot of non harnessed data within organizations

Data enables real-time processing

There is more and more Apps development actors

Data help organizations in identifying new market potential

Data is already monetizedCustomer relationship is now individualized

A strong information management strategy is mandatory

PwC

November 2015

Competitive intelligence

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PwC

November 2015

Following are examples of Big Data practices we have observed in various industries

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Section 3 – Competitive intelligence

Worldwide leader in retail industry US Leader in energy industry Belgian telecommunication operator

Big DataMultiple data sources are integrated in Big Data platform: WMS, Invoicing, CPM, manual files…The company launched a project to design and implement a Datamart to serve performance management report based on data in the platform.

The company have changed its organization‘s approach to big decision making as a result of data and analytics initiative.

The company installed a Big Data platform in order to gain visibility and control over fixed activities in the B2B market.Ensure consistency between technical, commercial and financial information, crossing market performance external data.

Data Analytics The company defined some insights (shortlist of 12 KPI), but wants more insights into the experiences that long-time and would-be consumers value. Yet it might not be getting enough new insight from the information it already receive, or it may be unfamiliar with advanced analytics and what it can do with it.

The company is putting analytics teams to work on strategic challenges:-Analyze data from a large volume of meteorological stations worldwide,-Optimize the placement of wind turbines,-Optimize electricity production.

The company made:-Data strategic analysis,-Insights identification relative to business, information management, systems, organization, processes.

Information Management The company automated the data cleaning

and machine learning in order to ensure data consistency and quality.

Using new, richer sets of data, and ensuring its quality which that allow to enhance the governance around their data.

The company structured and unified its data:Commercial, technical and financial data reconciliation,Repositories definition to ensure data consistency.

Analytic Apps

The company implemented a QlikView app to gain more insight on warehouses performanceIt designed KPI & visual representations and implemented mockups.

The company is using more specialized analytical tools and allow it to deliver immediate value.

The company developed a Performance Management Model based on an Analytical App delivered by PwC.The company has now a clear view on B2B fixed activities revenue by customer and by services to support an efficient decision-making process.

Frequent Occasional Rare

Data maturity level

PwC

November 2015

Following are examples of Big Data practices we have observed in various industries

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Section 3 – Competitive intelligence

Leader in aerospace industry Private bank Worldwide leader in insurance industry

Big Data

The company enabled a real-time Big Data collection generated by systems such as Aircraft (internet of things scope).

The company is investing significant amounts in data gathering and integration of external sources.The company is installing Hadoop platform.The aim of this initiative is to ensure the efficiency of KYC and AML process.

The company enabled a real-time .Big Data collection generated by systems such as Claims, subscriptions.

Data Analytics

The company ran analytics on its data to generate a meaningful insight.The aviation entity needed to collect a huge amount of information, on thousands flights, anticipating that the number of passengers would exceed millions.

The company has not yet considered business insights.Analysis can be both difficult andtime-consuming because of the huge amount of gathered data.

The company needs new quantitative skills that build upon its traditional actuarial and statistical perspectives. These new skills also require different ways of thinking about data and how it can be used.It’s challenging to interpret this data and incorporate new insight into traditional products, underwriting, operations and claims decision making.

Information Management

The capture of metadata allowed a more robust and diverse data framework, compared to the traditional data storage.

The company is improving data quality by creating coherence rules and establishing functional rules to ensure the accuracy of data.

The company is improving data quality by establishing functional rules to ensure the accuracy of data.

Analytic Apps

The company has implemented an analytical app and can now process large volumes of data from different sources.

No analytical app is planned for 2015 but have tothink the way to Accelerate insights via applications of data and analytics.

The company has implemented an analytical app and can now process large volumes of data from different sources.

Frequent Occasional Rare

Data maturity level

PwC

November 2015

Our relevant approach

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PwC

November 2015

When you start such a strategic project, our conviction is …

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Section 4 – Our relevant approach

Start by reshaping your business strategy around data

…then define your vision and your “data core values” …

Define a vision and your data program’s safeguards around BI, Data quality, governance (including regulatory aspects); master data management, product, customer, working mode, relevant analysis, relevant apps, enrichments needs, use cases, etc.

Clarify the data structures:

An architecture to structure data capabilities, discover the insight hidden in your data and think about where you want to be in five years.

Test and learn to lay the foundation stone of your program.

Pilot, assess, and operate: run research and development experiment on Big Data to build a realistic and operational roadmap to reach efficient quick wins to present to your board.

What is the right answer for your

business? How can your organization get to the level where it truly can take advantage of in-memory analytics in combination with Big Data?

Track and report:

Measure integration efforts and business performance to constantly adjust efforts efficiently.

… and streamlining your data structure …

… think big & start small …

… and then set up and monitor efficiently your data program.

1 2 3 4 5

PwC

November 2015

Reshape your business strategy around data and streamline your data structure

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02 Streamline your data structure

In parallel of the business need you need to integrate and clarify the data structures as much as possible.

It’s essential to have trust and confidence in your data and the systems that collect and hold it, to allow you to actively manage data-related risks in your business. In the digital era, businesses must be digitally trusted – by customers, suppliers and all business stakeholders.

Furthermore streamlining your data structure will help you to discover insight hidden in your data to understand what happened and why. Combining data sets that have often ever been linked before, to reveal trends, patterns, triggers and causal relationships to begin to explain the important ‘why’ questions.

Indeed data structure is important, and its effect on the performance of an algorithm is critical.You need to identify weaknesses, develop a restructuring and reorganization concept to redirect your data structure.

01 Define your business needs

First off all you need to answer to this crucial question:how can my organization get to the level where it truly can take advantage of in-memory analytics in combination with Big Data?

Of course Big Data will allow you to innovate and make decisions quickly while transforming the way you do business but you need to clearly define information and data sampling needs you need to improve, drive and sustain business change.An understanding of business objectives, and the underlying processes which drive profit and business growth are essential.

However it will not provide you with a complete roadmap nor will it deliver a formal framework for determining where your company should focus its investments. It will provide you with a basic guide to get started and help you to figure out the scope of both problem to be resolved and the solution. It can also help you to get an idea of the potential return of your investment.

This reflection must of course be coupled with deep study of current data structure.

What value exists in your data?

What is the right answer for yourbusiness?

PwC

November 2015

Define your vision, core values, set-up and monitor efficiently your data program

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03 Define your “data core values”

Big Data poses a variety of risk issues. Risks often include those associated with storage and retention of large volumes of data, data ownership and quality, information security, reputational risks and various regulatory requirements including privacy issues.

Effectively managing these risks will require companies to revisit governance structures and frameworks in order to allow for the effective and timely identification and assessment of risks in order to make informed risk / reward decisions. That’s why you need to define your “Data core Values”.

05 Set-up and monitor efficiently your data program

Measure success and continuously optimize your trigger program.Track and report on the current status of integration efforts, including risks, issues, successes, opportunities, and deviations from plan and from ongoing daily operations.Monitor and report on a continuing basis the impact on business performance from the integration of acquired technology with existing platforms.

Highlight realized synergies and areas for continued improvement. Modify previous key performance indicators and develop new ones to measure the business performance .With the good indicators you can deploy and iterate safely.

How do you embed data analyticsinto your organization?

Think big, start small, fail quickly, scale fast.

04 Think big & start small

Once you have clearly identified your weakness and your core value in skills, products, structure, capabilities, or depth of team. Pick a number of small, experiential orientated projects to begin to fill in your weak points, and learn about what it is you don’t know.

This will give you better depth of insight into what you need to do in order to deal with big an unclear Big Data project. you do need to have planned well, a list of efficient steps (potentials quick wins) to present to your board.

Is the project delivered in a timely and

cost-effective manner?

PwC

November 2015

How we can help from strategy through execution?

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PwC

November 2015

Assistance at C-Level, especially to CDO

PwC supports enterprises in developing and executing a solid strategic plan that applies the right technologies and methods considering the context of the client. We assist the Chief Data Officers to establish their governance’s approach, to structure, manage and control data.

Simple start with relevant use-case

PwC proposes to its clients to start their Big Data implementation by initiating a Proof Of Concept, on a small, well-known perimeter. This POC intends to demonstrate the viability, capabilities and the high added value of Big Data & analytics. We discuss topics about adequate priority depending on the complexity of your data and according to the applicable mathematical models.

Change management around data,

emergence of new professions

The release of the data brings a phenomenon of "Job Protection“. PwC provides experts around these new jobs and helps its clients to leverage on these skills and disseminate them within their company by proposing appropriate training (Data Scientist; Statistician, mathematician; CDO; Big Data Architect; No SQL database designer, …).

Technologies & innovation

PwC ensures the global transition from the actual storage method to the Big Data framework. We help our clients to implement their Big Data Architecture (e.g HADOOP, ..).We help our clients to design a “Digital Collaboration Room” in flexibility with the business which allow them to have a better way to reshape their Business.

27Point of view • Data service management: leverage Big Data as a strategic advantage

Section 5 – How we can help from strategy through execution

PwC helps clients on all Big Data areasFrom strategy through execution

“We see customers creating Big Data graveyards, dumping everything into the data lake (Big Data structure) and hoping to do something with it down the road. But then they just lose track of what’s there”

Sean MartinCambridge Semantics

PwC

November 2015

Facing today’s challenges, what is PwC Big Data Analytics ?

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A provider of complex, tailored and generic appsthat process small and Big Data to deliver simple insights on a specific topic

An architect of analytical data marts or data lakes on Hadoop or SQL Server

A qualified business understanding partner for all Advisory and Cross Business Lines data-driven assignments

A developer of analytical solutions to any operational problems

A hub for strategic insight discovery (by means of statistical modeling and optimization algorithms with advanced visualizations and real-time processing capabilities)

PwC

November 2015

So, how can we help you?

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re-defying your position vs competitors

… and much more!

Combination of ourdata science skills and your data can provide new insight into your enterprise, including…

identifying new market potential

increasing profitability through new strategy implementation

your interaction with customers

PwC

November 2015PwC can support you to deliver efficiently your Big Data project !

…with the framework of competencies below that will allow you to deliver a Big Data project.

30Point of view • Data service management: leverage Big Data as a strategic advantage


With four necessary

dimensions such

governance, hackathon,

installation of the Big Data

platform and a deep business expertize with

relevant use-case from our experience to

activate.

Relevant and specific skills requiredaround Big Data

DataScientist statistician,

mathematicianDataviz

Designer

APIDesigner

Big Dataarchitect

NoSQLdatabaseDesignerChief

Data Officer

Master Data

Manager

Legal expert

User experience

expert

Cyber security expert

Big Data skills

requirements

Web technologies

expert

PwC

November 2015

We share our experience feedback

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Experience

We have significant, relevant, and recent experience in providing actionable recommendations to various industry on improving business competitivity through technology.

Integrated global network

With 40,700 industry-dedicated professionals worldwide, PwC has a network that enables the assembly of both cross-border and regional teams. PwC’s large, integrated global network of industry-dedicated resources means that PwC Advisory Services deploys the right personnel with the right background on our clients’ behalf whenever and wherever they need it.

Extensive industry experience

Our industry expertise provides clients with real added value, making PwC a renowned supplier of industry-specific services for the market. This expertise helps our experts to understand companies’ business in wider contexts. Accordingly, they are able to recommend relevant use cases like business forecast, customer scoring, or churn prediction.

Among the key distinguishing characteristics of PwC Advisory is the depth and reach of the firm’s global network of professionals

Expertise

Multi-disciplinary eam

PwC Advisory service teams include specialists in strategy, risk management, finance, regulation, and technology. Multidisciplinary teams to provide value-added ideas impacting the client’s operations.

We feel equally comfortable helping the heads of business and the heads of risk, finance, operations, and technology; we have helped clients solve problems that cross all of these areas.

Continuous expertise improvement

We leverage our experienced professionals and PwC’s proprietary tools and frameworks, which draw upon the practices of leading peers, industry leading practices, and our lessons learned from past programs .

PwC Advisory offers flexible, strategic support to clients, based on our in-depth knowledge of Big

Data practices across various industries

PwC

November 2015

We share our expertise with you

32



Value

Our experienced teams help clients to:• Gain a deeper understanding of the total performance of the target business

Industry expertiseBy using data lakes to perform the combination of various data repositories, including external data, we offer our clients best in class methodology to tackle complex business issues.

Business as usualThe purpose of enterprise data warehouses is to facilitate regular, operational reporting. Because we know how critical this reporting is for conducting day-to-day business and operating the company strategy, we put BI continuity at the core of our efforts during the data lake set up.As a large transformation project operator, PwC is used to and has the skill necessary to maintain ongoing business services while delivering the enterprise transformation.

Organization and Skills

Processes&

Standards

Semantics

Services

Architectureand solutions

Security Policies

Governance Framework

You could benefit the opportunity toleverage on our several expertiseand in-depth knowledge of Big Datapractices across various industries

GovernanceExperience has shown that data lake projects managed without a proper governance strategy fail to deliver functional platform.PwC’s expertise in governance standards takes form in a Governance Framework as illustrated here.Regarding data lake projects, we tailor this framework around following cornerstones: Master Data Management and Data Quality Organization (e.g. setting up the Chief Data Officer role) Interface with the legacy system Technological choices and set up regarding

o Analyticso Data miningo Machine learningo Data visualization (Rapid BI)

PwC

November 2015

What makes PwC distinctive: we can help you to make your data project a success…… with our four main offers around Data Services

33



02Data & Analytics

PwC data scientist team is highly skilled in science & computing (PhD.) for rapid business insight delivery.

04Information managementManage content, data governance, strategy, and business intelligence with the best tools of the market.

PwC

Data services

Big Data

PwC enables outstanding data processing performance by providing

efficient and quality infrastructure recommendations for all data

processing purposes.

03Analytic Apps

PwC has developed 18 apps to deliver immediate value on strategic job. The

developed methodology enables to enhance internal IP.

01

PwC

November 2015

We deliver apps and analytics by leveraging benchmark and innovation

34



Innovation

Partnering with world experts in data & analytics

to deliver market-tested research and innovation

PwC Big DecisionsTM Survey

Large-scale visualizationsInnovative analytics

Apps

Accelerating client

insights via the delivery of

productized applications of data and

analytics

Data Analytics

Jumpstarting data

exploration and analytics

with an expansive

store of integrated datasets

Benchmarking

Driving deeper understanding of

industry performance and

levers can help companies grow

Finance mattersFinance function ofThe future

PwC

November 2015

A catalog of apps built to address our clients’ challenges across industries and business functions

35



IndustriesBusiness

Functions

P o w e r & U t i l i t i e s

M e d i a & T e l e c o m

H e a l t h c a r e

A e r o s p a c e & D e f e n s e

S t r a t e g i c P l a n n i n g

M a r k e t i n g

D i s t r i b u t i o n

S e r v i c e

R e g u l a t i o n

R i s k / C o m p l i a n c e

O t h e r

D e m a n d E s t i m a t o r

S T A R

S u p p l y C h a i nA n a l y t i c s

G e n e s i s

U n i f i e dS u r v e i l l a n c e

H u m a n R e s o u r c e sI n s u r a n c e

B a n k i n g

PwC

November 2015

To accelerate execution, PwC offers self-developed solutions to address key business decisions

36



DemandEstimatorSM

Optimize the customer multi-channel journey to reduce leakage and drive incremental revenue

Consumer Behavior ModelSM

Create more targeted cross-sell offers, increasing “share of wallet” as well as customer retention

Experience RadarSM

Link the attributes of customer experience to hard metrics like churn / loyalty to better inform service actions

SocialMindSM

Tie social metrics such as sentiment & reach to enterprise metrics (NPS & CSAT) to better understand customer value drivers

CLV-RSM

Quantify ‘customer level’ risk adjusted lifetime value to drive better account acquisition and customer portfolio performance

Experience NavigatorSM

Evaluate different customer experience strategies in a virtual world to see the potential impacts on revenue, brand equity, etc.

Unified SurveillanceSM

Create a holistic view of customer behaviors to infer the customer need in the moment for better cross-sell and service strategies

Disruption RadarSM

Discover early-stage emerging technologies to quickly test and imbibe into the enterprise to foster continuous innovation

Human Capital AnalyticsSM

Identify key risk areas where adoption is least likely to proactively address change readiness

PwC’s Analytic Apps – Value to our clients

PwC

November 2015

Credentials

37


PwC

November 2015

Some of our accomplishments

38


ClientBig

DataData &

Analytics

Analytic

AppInformation Management

Solution Summary

Private bank Know-Your-Customer platform

Build a platform to manage KYC regulatory processes

Private bank Anti-Money Laundering platform

Build a platform to manage AML regulatory processes

Retail bank

Point of sale optimization

Branch booster : Advanced geo-modelling approach enables to predict potential of a branch/point of sale location. Methodology tested x-industry – in retail and top 3 Polish Bank

Retail bank

Predictive and reactive churn management

Applying a 90 KPI Maturity Model, building a 360 degree Customer Data Mart and a comprehensive advanced predictive model to reduce churn by as much as 30% (Pilot Completed)

Retail

HR predictive modeling

Anticipate flight risk and turnover of resources to anticipate temporary employee

Accountant Automated benchmark hotel accountantBenchmark hotel to detect abnormal behavior and anticipate

Investmentbank

Third party referential dataAssist client to set up a third party referential data

Investment bank

Defining MDM governance models and related TOMDeveloped an Information Management Strategy, including several program initiatives that were sequenced in a 3-5 years roadmap

Section 6 – Credentials

PwC

November 2015

Some of our accomplishments

39


ClientBig

DataData &

Analytics

Analytic

AppInformation Management

Solution Summary

Telecom operator

Single view of customers

Built a single omni-channel view of the customer that impact the bottom line.

Medical center

Maintain data provenance and loyalty

Different analyses in different contexts and making possible several data analysis projects

Retail

Unifying customer data

Definition of the strategy of streamlining of data and support to the implementation

Telecom operator

Unified view of the households

Developed an information management and data analytics strategy that allowed the client to built a strong loyalty program

Retail

Supply chain monitoring

Definition of the strategy around Big Data and related apps to improve the monitoring of the supply chain

Professional services

CRM analyticsDesigned digital operating and organizational model to enable an enterprise approach to customer experience that identified short-term digital channel improvement to drive customer satisfaction and established a strategy to manage digital marketing

Professional services

Implementation of a scorecards toolsImplementation of analytics tools to provide scorecards

Retail bank Customer lifetime valueUnderstand X-Sell and Up-Sell

Section 6 – Credentials

PwC

November 2015

Contacts

40


To have a deeper conversation, please ask your contacts:

Patrick AkikiPartnerFinancial Services Technology leader

Office: +33 (0) 1 56 57 81 61Mobile: +33 (0) 6 48 00 87 [email protected]

Loïc MesnagePartnerIndustries & ServicesTechnology leader

Office: +33 (0) 1 56 57 54 20Mobile: +33 (0) 6 68 24 21 [email protected]

Flavio PalaciPartnerCEE Analytics

Mobile: +420 730 595 [email protected]

We would like to thanks all the contributors to their effort on this publication including: BhilalMougammadou, Cyril Jacquet, Jean Barrère, Marc Damez-Fontaine, Sebastien Ponte & Sofiene Korbi

mailto:[email protected]



This publication has been prepared for general guidance on matters of interest only, and does notconstitute professional advice. You should not act upon the information contained in this publicationwithout obtaining specific professional advice. No representation or warranty (express or implied) isgiven as to the accuracy or completeness of the information contained in this publication, and, to theextent permitted by law, PwC France, its members, employees and agents do not accept or assume anyliability, responsibility or duty of care for any consequences of you or anyone else acting, or refraining toact, in reliance on the information contained in this publication or for any decision based on it.

© 2015 PwC France. All rights reserved. In this document, “PwC” refers to PwC France which is amember firm of PricewaterhouseCoopers International Limited, each member firm of which is a separatelegal entity.

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