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Point of View
1 7 23 27
Our point of view
How we can help from strategy through execution
Our relevant approach
Current trends about data
20
Competitive intelligence
AppendicesCredentials Contacts
38
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
1
Point of view • Data service management: leverage Big Data as a strategic advantage
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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Point of view • Data service management: leverage Big Data as a strategic advantage
Section 1 – Current trends about data
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
Section 1 – Current trends about data
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.
Point of view • Data service management: leverage Big Data as a strategic advantage
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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Point of view • Data service management: leverage Big Data as a strategic advantage
Section 1 – Current trends about data
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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Point of view • Data service management: leverage Big Data as a strategic advantage
Section 1 – Current trends about data
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
PwC
November 2015
Our point of view
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Point of view • Data service management: leverage Big Data as a strategic advantage
PwC
November 2015
Our clients say …
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Point of view • Data service management: leverage Big Data as a strategic advantage
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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Point of view • Data service management: leverage Big Data as a strategic advantage
Section 2 – Our point of view
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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Point of view • Data service management: leverage Big Data as a strategic advantage
Section 2 – Our point of view
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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Point of view • Data service management: leverage Big Data as a strategic advantage
Section 2 – Our point of view
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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Point of view • Data service management: leverage Big Data as a strategic advantage
Section 2 – Our point of view
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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Point of view • Data service management: leverage Big Data as a strategic advantage
Section 2 – Our point of view
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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Point of view • Data service management: leverage Big Data as a strategic advantage
Section 2 – Our point of view
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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Point of view • Data service management: leverage Big Data as a strategic advantage
Section 2 – Our point of view
• 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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Point of view • Data service management: leverage Big Data as a strategic advantage
Section 2 – Our point of view
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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Point of view • Data service management: leverage Big Data as a strategic advantage
Section 2 – Our point of view
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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Point of view • Data service management: leverage Big Data as a strategic advantage
Section 2 – Our point of view
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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Point of view • Data service management: leverage Big Data as a strategic advantage
Leveraging your data as a strategic advantage
Section 2 – Our point of view
“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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Point of view • Data service management: leverage Big Data as a strategic advantage
PwC
November 2015
Following are examples of Big Data practices we have observed in various industries
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Point of view • Data service management: leverage Big Data as a strategic advantage
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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Point of view • Data service management: leverage Big Data as a strategic advantage
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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Point of view • Data service management: leverage Big Data as a strategic advantage
PwC
November 2015
When you start such a strategic project, our conviction is …
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Point of view • Data service management: leverage Big Data as a strategic advantage
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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Point of view • Data service management: leverage Big Data as a strategic advantage
Section 4 – Our relevant approach
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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Point of view • Data service management: leverage Big Data as a strategic advantage
Section 4 – Our relevant approach
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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Point of view • Data service management: leverage Big Data as a strategic advantage
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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Point of view • Data service management: leverage Big Data as a strategic advantage
Section 5 – How we can help from strategy through execution
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?
29
Point of view • Data service management: leverage Big Data as a strategic advantage
Section 5 – How we can help from strategy through execution
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
Section 5 – How we can help from strategy through execution
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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Point of view • Data service management: leverage Big Data as a strategic advantage
Section 5 – How we can help from strategy through execution
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
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Point of view • Data service management: leverage Big Data as a strategic advantage
Section 5 – How we can help from strategy through execution
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
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Point of view • Data service management: leverage Big Data as a strategic advantage
Section 5 – How we can help from strategy through execution
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
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Point of view • Data service management: leverage Big Data as a strategic advantage
Section 5 – How we can help from strategy through execution
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
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Point of view • Data service management: leverage Big Data as a strategic advantage
Section 5 – How we can help from strategy through execution
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
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Point of view • Data service management: leverage Big Data as a strategic advantage
Section 5 – How we can help from strategy through execution
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
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Point of view • Data service management: leverage Big Data as a strategic advantage
PwC
November 2015
Some of our accomplishments
38
Point of view • Data service management: leverage Big Data as a strategic advantage
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
Point of view • Data service management: leverage Big Data as a strategic advantage
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
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Point of view • Data service management: leverage Big Data as a strategic advantage
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 74patrick.akiki@fr.pwc.com
Loïc MesnagePartnerIndustries & ServicesTechnology leader
Office: +33 (0) 1 56 57 54 20Mobile: +33 (0) 6 68 24 21 84loic.mesnage@fr.pwc.com
Flavio PalaciPartnerCEE Analytics
Mobile: +420 730 595 648flavio.palaci@cz.pwc.com
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
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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