Thomas Inman

Findings from the research collaboration of
IBM Institute for Business Value and
Saïd Business School, University of Oxford

Analytics: The real-world use of big data
How innovative enterprises extract value from uncertain data

Tom Inman, Vice President, IBM Software Group
tinman@us.ibm.com

©2012 IBM Corporation

Agenda

1 Introduction to Big Data

2 Macro Findings

3 Key Findings

4 Recommendations

5 Call to Action

2 | ©2012 IBM Corporation

Introduction to big data

| ©2012 IBM Corporation


Big data is a business priority – inspiring new models and
processes for organizations, and even entire industries
Analytics is expanding from enterprise data to big data

Volume Velocity Variety

12of Tweets create daily
terabytes 5 million
trade events per second
100’s
from surveillance cameras
video
feeds

Analyze product sentiment Identify potential fraud Monitor events of interest

350
meter readings per annum
billion 1.5 billion
call detail records per hour
80% data
growth
are images, video, documents…

Predict power consumption Prevent customer churn Improve customer satisfaction



Big data is a business priority – inspiring new models and
processes for organizations, and even entire industries



Applications for Big Data Analytics

Smarter Healthcare
Multi-channel Finance Log Analysis
sales

Homeland Security Search Quality
Traffic Control Telecom

Manufacturing Trading Analytics Fraud and Risk Retail: Churn, NBO



Big data embodies new data characteristics created by
today’s digitized marketplace

Characteristics of big data


Macro findings


Study overview

IBM Institute for Business Value and the Saïd Business
School partnered to benchmark global big data activities

IBM
Institute for Business Value
IBM Global Business Services, through the IBM
Institute for Business Value, develops fact-based
strategies and insights for senior executives
around critical public and private sector issues.

Saïd Business School
University of Oxford
The Saïd Business School is one of the leading
business schools in the UK. The School is
establishing a new model for business education
by being deeply embedded in the University of
Oxford, a world-class university, and tackling
some of the challenges the world is encountering.

www.ibm.com/2012bigdatastudy

Macro findings

Nearly two out of three respondents reports realizing a
competitive advantage from information and analytics

Realizing a competitive advantage
Competitive advantage enabler
 A majority of respondents
reported analytics and information
(including big data) creates a 2012
competitive advantage within their 63%
market or industry
 Represents a 70% increase 2011
since 2010
58% 70%
increase
 Organizations already active in
big data activities were 15%
2010
37%
more likely to report a
competitive advantage
Respondents were asked “To what extent does the use of information (including big
 A higher-than-average percentage data) and analytics create a competitive advantage for your organization in your
of respondents in Latin America, industry or market.” Respondent percentages shown are for those who rated the
India/SE Asia and ANZ reported extent a [4 ] or [5 Significant extent]. The same question has been asked each year.
realizing a competitive advantage 2010 and 2011 datasets © Massachusetts Institute of Technology Total respondents n = 1144



Respondents define big data by the opportunities it creates

Defining big data
Greater scope of information
 Integration creates cross-enterprise view
 External data adds depth to internal data

New kinds of data and analysis
 New sources of information generated
by pervasive devices
 Complex analysis simplified through
availability of maturing tools

Real-time information streaming
 Digital feeds from sensors, social and
syndicated data
 Instant awareness and accelerated
decision making Respondents were asked to choose up to two descriptions about how their
organizations view big data from choices above. Choices have been abbreviated,
and selections have been normalized to equal 100%.


Macro findings

Three out of four organizations have big data activities
underway; and one in four are either in pilot or production

Big data activities
Early days of big data era
 Almost half of all organizations surveyed
report active discussions about big data plans
 Big data has moved out of IT and into
business discussions

Getting underway
 More than a quarter of organizations have
active big data pilots or implementations
 Tapping into big data is becoming real

Acceleration ahead
 The number of active pilots underway
suggests big data implementations will rise
exponentially in the next few years
 Once foundational technologies are installed, Respondents were asked to describe the state
use spreads quickly across the organization of big data activities within their organization.
Total respondents n = 1061
Totals do not equal 100% due to rounding


Macro findings

Organizations are gaining value from working with IBM

Grow, retain and 60%
Improvement in billed
satisfy customers revenue retention rate

Increase operational 50%
Increase in
efficiency inventory turns

Transform financial
50%
Reduction in
processes planning cycle times

Manage risk, fraud & 70%
regulatory compliance Trading decisions improved
with 70% of counterparties


Key findings


Key findings

Five key findings highlight how organizations are moving
forward with big data

1 Customer analytics are driving big data initiatives

Big data is dependent upon a scalable and extensible
2 information foundation

Initial big data efforts are focused on gaining insights
3 from existing and new sources of internal data

4 Big data requires strong analytics capabilities

The emerging pattern of big data adoption is
5 focused upon delivering measureable business value


Key Finding 1: Customer analytics are driving big data initiatives

Improving the customer experience by better understanding
behaviors drives almost half of all active big data efforts

Big data objectives
Customer-centric outcomes
 Digital connections have enabled
customers to be more vocal
about expectations and
outcomes
 Integrating data increases the
ability to create a complete
picture of today‟s „empowered
consumer‟
 Understanding behavior patterns
and preferences provides
organizations with new ways to
engage customers

Customer-centric outcomes New business model
Other functional objectives Operational optimization Employee collaboration
 The ability to connect data and Risk / financial management
expand insights for internally
Top functional objectives identified by organizations with active big data pilots
focused efforts was significantly or implementations. Responses have been weighted and aggregated.
less prevalent in current activities Total respondents n = 1061


Key Finding 1: Customer analytics are driving big data initiatives

Customer-centric analytics is the primary functional
objective across macro industry groups, as well

Healthcare /
Consumer Goods Financial Services
Life Sciences
5% 2%
4%
7% 16% 10%
10%

50% 16% Customer-
51% centric
21%
20% 59% outcomes

11% 19% Operational
optimization

Risk / financial
Manufacturing Public Sector management
Telecommunications
1% New business
6% 6%
6% model
13% 18%
32%
Employee
42%
collaboration
13% 27% 11%
62%
8%
26% 30%


Case study

Santam Insurance: Predictive analytics improve fraud
detection and speed up claims processing
South Africa‟s largest short-term insurance company uses predictive analytics to uncover a major insurance fraud
syndicate, save millions on fraudulent claims and resolve legitimate claims 70 times faster than before.

Solution

 Gained the ability to spot fraud early with an advanced
analytics solution that:
 captures data from incoming claims, assesses each
claim against identified risk factors and segments
claims to five risk categories, separating higher-risk
Business Opportunity cases from low-risk claims
 Plans to use propensity modeling to enhance and
 Like most insurers around the world, Santam was refine segmentation process as more data becomes
losing millions of dollars paying out fraudulent
claims every year
 Expenses were being passed on to the customer Results
in the form of higher premiums and longer waits
to settle legitimate claims  Identified a major fraud ring less than 30 days after
 To improve its bottom line and enhance customer implementation
satisfaction, the company needed to detect and  Saved more than $2.5M in payouts to fraudulent
stop insurance fraud early in the claims process customers, and nearly $5M in total repudiations
 Reduced claims processing time on low-risk claims by
 It also needed to find a way to isolate risky,
nearly 90%
fraudulent claims so that claims managers could
 Cut operating costs by reducing the number of mobile
more quickly process lower-risk claims
claims investigations

Key Finding 2: Big data is dependent upon a scalable and extensible information foundation

Big data efforts are based on a solid, flexible information
management foundation
Big data infrastructure
Solid information foundation
 Integrated, secure and governed
data is a foundational requirement
for big data
 Most organizations that have not
started big data efforts lack
integrated information
stores, security and governance

Scalable and extensible
 Scalable storage infrastructures
enable larger workloads; adoption
levels indicate volume is the first big
data priority
 High-capacity warehouses support
Respondents with
the variety of data, a close second active big data efforts
priority were asked which
platform components
 A significant percentage of were either currently
organizations are currently piloting in pilot or installed
Hadoop and NoSQL within their
organization.
engines, supporting the notion of
exponential growth ahead

Key Finding 2: Big data is dependent upon a scalable and extensible information foundation

Big data efforts are based on a solid, flexible information
management foundation
A holistic and integrated approach to analytics and big data

Solutions

Enterprise
Enterprise Healthcare
Healthcare Next Best
Next Best Social Media
Social Media
Fraud
Fraud Analytics
Analytics Action
Action Analytics
Analytics

An approach that enable organizations to:
Analytics and Decision Management
• Discover and integrate relevant information
Predictive
Predictive Content
Content Decision
Decision Visualization
Visualization
• Analyze patterns and predict outcomes Analytics
Analytics Analytics
Analytics Management
Management & Discovery
& Discovery

• Visualize and explore for answers
Big Data Platform
• Take action and automate processes
Content
Content Hadoop
Hadoop Data
Data Stream
Stream
Management
Management System
System Warehouse
Warehouse Computing
• Optimize analytical performance and IT costs Computing

• Manage, Govern and Secure Information Information Integration and Governance

Big Data Infrastructure: Systems, Storage and Cloud


Key Finding 3: Initial big data efforts are focused on gaining insights from existing and new
sources of internal data
Internal sources of data enable organizations to quickly
ramp up big data efforts
Big data sources
Untapped stores of internal data
 Size and scope of some internal data, such
as detailed transactions and operational log
data, have become too large and varied to
manage within traditional systems
 New infrastructure components make them
accessible for analysis
 Some data has been collected, but not
analyzed, for years

Focus on customer insights
 Customers – influenced by digital
experiences – often expect information
provided to an organization will then be
“known” during future interactions Respondents were
asked which data
 Combining disparate internal sources with sources are currently
advanced analytics creates insights into being collected and
analyzed as part of
customer behavior and preferences active big data efforts
 Transactions within their organization.
 Emails
 Call center interaction records

Case study

Vestas: Better data analysis capabilities lower costs
and improve effectiveness
Vestas Wind Systems A/S optimizes capital investments based on 2.5 petabytes of
information and big data technologies
Solution

 Vestas can now help its customers optimize turbine
placement and, as a result, turbine performance.
 Uses a big data solution on a supercomputer -- one of
the world‟s largest to date -- and a modeling solution
to harvest insights from an expanded set of factors
including both structured and unstructured data

Results
Business Opportunity  Insights lead to improved decisions for wind turbine
placement and operations, as well as more accurate
 Wind turbines are a multimillion dollar power production forecasts
investment with a typical lifespan of 20-30 years  Greater business case certainty, quicker results, and
 Placement depends upon a large number of increased predictability and reliability
location-dependent factors
 Vestas has been unable to support data analysis  Decreased cost to customers per kilowatt hour
of the very large data sets the company deemed  Reduction by approximately 97 percent – from weeks to
necessary for precision turbine placement and hours – of response time for business user requests
power forecasting due to inadequate  Greatly improves the effectiveness of turbine placement
infrastructure and reliance on external models

Key Finding 4: Big data requires strong analytics capabilities

Strong analytics capabilities – skills and software – are
required to create insights and action from big data
Analytics capabilities
Strong skills and software foundation
 Organizations start with a strong core of
analytics capabilities, such as query and
reporting and data mining, designed to
address structured data
 Big data efforts require advanced data
visualization capabilities as datasets are
often too large or complex to analyze and
interpret with only traditional tools
 Optimization models enable organizations
to find the right balance of
integration, efficiency and effectiveness in
processes

Skills gap spans big data
 Acquiring and/or developing advanced
Respondents were
technical and analytic skills required for asked which analytics
big data is a challenge for most capabilities were
organizations with active efforts underway currently available within
their organization to
 Both hardware and software skills are analyze big data.
needed for big data technologies; it‟s not
just a „data scientist‟ gap

Case study

Automercados Plaza’s: Greater revenue through
greater insight
Automercados Plaza‟s uses data analysis and optimization to gain deeper insights into its
customers and generate spectacular gains in sales and the bottom line

Solution
 Automercados Plaza‟s managers now quickly review
daily inventory levels, store sales and cost of goods
to see which products are selling and are most
profitable, and which promotions are most successful
 Enables chain limit losses by scheduling price
reductions to move perishable items prior to spoilage
 The solution aids in compliance with government
price controls on grocery staples
 Assists with store location selection
Business Opportunity
Results
 $20M in inventory and more than six terabytes
of product and customer data spread across  Increased annual revenues by 30%
multiple systems and databases  Increased annual profits by $7M
 Unable to easily assess operations at  Decreased time to compile sales tax data by 98%
individual stores using manual processes  Lowered losses on perishable goods, which comprise
 Needed a comprehensive and timely view of approximately 35% of the chain's products
operations that would support and improve  Helped executives pinpoint optimal locations for four
decisions about business operations new grocery stores


Key Finding 5: The emerging pattern of big data adoption is focused upon delivering
measureable business value
Patterns of organizational behavior are consistent
across four stages of big data adoption

Big data adoption

When segmented into four groups based on current levels of big data activity, respondents showed significant consistency in
organizational behaviors Total respondents n = 1061
Totals do not equal 100% due to rounding


Additional Findings

Big data leadership shifts from IT to business as
organizations move through the adoption stages

CIOs lead early efforts Leadership shifts
 Early stages are driven by CIOs once
leadership takes hold to drive
exploration
 CIOs drive the development of the
vision, strategy and approach to big
data within most organizations
 Groups of business executives usually
guide the transition from strategy to
proofs of concept or pilots

Business executives drive action
 Pilot and implementation stages are
driven by business executives – either
a function-specific executive such as
CMO or CFO, or by the CEO Respondents were asked which executive is most closely aligned with
 Later stages are more often centered the mandate to use big data within their organization. Box placement
reflects the degree to which each executive is dominant in a given stage.
on a single executive rather than a
group; a single driving force who can Total respondents n = 1028
make things happen is critical


Additional Findings

Executive desire for quick and precise decisions to keep
up with the pace of business drives real-time data needs

Speed to insight
Reduce the lag
 Executives are focused on
reducing the time between data
intake and its availability within
business processes
 This lower latency supports the
ability to target customer-centric
outcomes, but requires a more
resilient infrastructure

Acceleration anticipated
 40% of executives in the Execute
stage expect real-time data to be
available within processes
 The move toward real-time
availability will continue to Respondents were asked how quickly business users require data to be
increase as the use of machine- available for analysis or within processes. Box placement reflects the
to-machine processing and prevalence of that requirements within each a stage.

embedded analytics expands Total respondents n = 973


Additional Findings

Challenges evolve as organizations move through the
stages, but the business case is a constant hurdle

Obstacles to big data
State the case
 Findings suggest big data activities are
being scrutinized for return on
investment
 A solid business case connects big data
technologies to business metrics

Getting started
 The biggest hurdle for those in the early
stages is first understanding how to use
big data effectively, and then getting
management‟s attention and support
 Skills become a constraint once
organizations start pilots, suggesting the
need to focus on skills during planning
 Data quality and veracity only surface Respondents were asked to identify the top obstacles to big data efforts
as an obstacle once roll-out within their organization. Responses were weighted and aggregated. Box
placement reflects the degree to which each obstacle is dominant in a
begins, again suggesting the need for given stage.
earlier attention Total respondents n = 973


Recommendations


Recommendations

An overarching set of recommendations apply to all
organizations focused on creating value from big data

1 2 3
Commit initial Develop Start with existing
efforts to drive enterprise-wide data to achieve
business value big data blueprint near-term results

4 5
Build analytical Create a business
capabilities based on case based on
business priorities measurable outcomes


Key Recommendation 1: Commit initial efforts at customer-centric outcomes

Customer analytics creates a high-impact start to big data

Customer analytics imperative
Customer analytics imperative “I think big data will
 Focus initial big data initiatives on areas that significantly impact the
can provide the most value to the business
business delivery and
 Customer analytics enable better service to
customers as a result of being able to truly consumer landscape by
understand customer needs and anticipate helping service providers
future behaviors
and retailers better predict
Dynamic customer expectations consumer needs and
 To effectively cultivate meaningful relationships reduce overall costs
with customers, organizations must connect
with them in ways their customers perceive as
through better supply-chain
valuable management, increased
 The value may come through more speed in delivery and
timely, informed or relevant interactions
 Value may also come as organizations improve higher sales”
the underlying operations in ways that enhance
the overall experience of those interactions – Entertainment /Media executive
United States


Key Recommendation 2: Develop enterprise-wide big data blueprint

An enterprise-wide blueprint defines what organizations
want to achieve with big data

Components of a blueprint
Aligns organization around big data “Currently organizations
 Encompasses the vision, strategy and requirements have a lot of data but
for big data within an organization
 Establishes alignment between the needs of
they do not know how to
business users and the IT project plan make use of it.
 Creates a common understanding of how the Knowledge is right there
enterprise intends to use big data to improve its
business objectives in the data, but we did
not have the tools to
Defines the scope of big data explore them until now.
 Identifies the key business challenges to which big
data will be applied
Big data will help convert
 Outlines business process requirements that define data into knowledge”
how big data will be used
 Documents the future architecture
– Information Technology
 Serves as basis for developing an IT roadmap to
implement big data solutions in ways that create executive
sustainable business value ISA IOT


Key Recommendation 3: Start with existing data to achieve near-term results

Existing data offers opportunity to quickly start
learning new tools and technologies with familiar data

Focus on internal data

Pragmatic approach “Big data will allow
 The most logical and cost-effective place to organizations to analyze
start looking for new insights is within the
enterprise
and correlate data from
 Looking internally first allows organizations to: their internal processes
• Leverage existing data and their business
• Use software and skills already in place
• Deliver near-term business value environments in a way that
• Gain important experience before is not possible today, even
adding new data, skills and tools
with a lot of business
Speed to value intelligence and analytical
 Enables organizations to take advantage of tools that already exist”
the information stored in existing repositories
while infrastructure implementations are
underway – Consumer Products executive
 As new technologies become available, big Brazil
data initiatives can be expanded to include
greater volumes and variety of data


Key Recommendation 4: Build analytical capabilities based on business priorities

Analytics capabilities include both skills and tools

Key capabilities required
Addressing the skills gap “I think big data is going to
 Organizations face a growing variety of analytics
tools while also facing a critical shortage of play a very important role
analytical skills in the near future since the
 Big data effectiveness hinges on addressing this
significant gap
dynamics of IT are
 In short, organizations will have to invest in changing very quickly.
acquiring both tools and skills In coming years, the
Proactive development company with the superior
 As part of this process, it is expected that new skills will lead to the path
roles and career models will emerge for of success and making the
individuals with the requisite balance of
analytical, functional and IT skills world a better place”
 Attention to the professional development and
career progression of in-house analysts – who – IT executive
are already familiar with the organization‟s
United Kingdom
unique business processes and challenges –
should be a top priority for business executives

Harvard Business Review: “Data Scientist” is the sexy new career

Key Recommendation 5: Create a business case based on measurable outcomes

Business cases must include explicit forecasts of how
technology investments will impact the bottom line

Business case details
“I believe big data will force
Articulating the case
 Many organizations are basing their business
companies to re-think their
cases on the following benefits that can be structures and business
derived from big data: divisions to focus more on
 Smarter decisions – Leverage new sources of data
to improve the quality of decision making those areas that are most
 Faster decisions – Enable more real-time data
capture and analysis to support decision making at
relevant to the accomplishment
the “point of impact” of the strategy and corporate
 Decisions that make a difference – Focus big data
efforts toward areas that provide true differentiation goals, and not just
Secure executive support financial, but also in terms of
 An important principle underlies each of these
recommendations: business and IT professionals
customer satisfaction, product
must work together throughout the big data development, research, etc.”
journey
 Active involvement and sponsorship from one or
– Insurance industry executive
more business executives throughout this
process is needed to advocate for investments Mexico


Recommendations by stage: Educate to Explore

Moving from Educate to Explore is the first critical step
down the path towards achieving value through big data

Educate to Explore:
Create a foundation for action

 Continue to expand your knowledge by focusing on use cases where big
data is providing competitive advantage to organizations, both inside and
outside of your Industry.
 Work with different business units and functions to identify your most
critical business opportunities and challenges that can be addressed with
better and more timely information access.
 Focus on strengthening your information management environment and
infrastructure, including the development of a big data blueprint.
 Blueprints are often based on industry standards, reference architectures
and other available technical frameworks and resources.


Recommendations by stage: Explore to Engage

Moving from Explore to Engage takes organizations from
strategizing about big data to beginning to realize value

Explore to Engage:
Put plans into action

 Confirm active business leader sponsorship as you develop your big data
strategy and roadmap.
 Develop the business case for one or two key business opportunities or
challenges that you plan to address through POCs or pilot project(s).
 While beginning to plan for longer-term requirements, regularly confirm that
your information management foundation and IT infrastructure are able to
support the big data technologies and capabilities required for the POC or pilot.
 Assess your current information governance processes and their readiness to
address the new aspects of big data.
 Analyze existing skill sets of internal resources, and begin gap analysis of
where you need to grow and/or hire additional skills.


Recommendations by stage: Engage to Execute

Moving from Engage to Execute is a key step to maximizing
business value and competitive advantage from big data

Engage to Execute:
Understand the opportunities and challenges ahead
 Actively promote pilot project successes to sustain momentum while
beginning to engage other parts of the business.
 Finalize the business case with the validation and quantification of projected
returns on investment and benefits, including defined success criteria and
metrics.
 Identify the business process modifications and improvements expected
from having access to better and more timely information (for example,
marketing, sales, customer service and social media sites).
 Develop a competency plan to confirm the availability of adequate technical
and quantitative skills that are required to achieve short-term and longer-
term objectives.
 Document the detailed project plan for migrating pilot(s) into production. This
plan should include confirmation of expected business value, costs,
resources and project timelines.


Recommendations by stage: Execute

Organizations in the Execute stage must continue to expand
their capabilities to stay ahead of the competition

Execute:
Embrace the innovation of big data
 Document quantifiable outcomes of early successes to bolster future efforts.
 Initiate formal big data communications across the organization to continue
building support and momentum.
 Focus on extending technologies and skills required to address new big data
challenges across business units, functions and geographies.
 Remain vigilant about information governance (including information lifecycle
management), privacy and security.
 Continue to evaluate rapidly-evolving big data tools and technologies. Balance
existing infrastructure with newer technologies that increase scalability,
optimization and resiliency.


Create a business case based on
measurable outcomes
Key Business Benefits

Dealer incentives, Pricing, District based approach
Business Areas
Targeted marketing and increased cross-sell / up-sell
1. Sales & Marketing
Effectiveness
Reduction in subscriber churn through advanced
subscriber analytics
2. Advanced Data
Expansion
Filling capacity , Sachet approach on pricing

3. Network Optimization
Freemium approach,

4. Finance Improve network utilization

Optimize network capital investments

Optimize network operating investments

Benchmarking, Spend smart, Financial analysis ,
post mortem

Product Profitability Analysis


COMPANY CONFIDENTIAL

Getting started
j


Getting started

Big data creates the opportunity for real-world
organizations to extract value from untapped digital assets

 Focus on measurable business outcomes
 Take a pragmatic approach, beginning with existing data,
tools/technologies, and skills
 Expand your big data capabilities and efforts across the enterprise

Big data: Tapping into new sources of value

www.ibm.com/2012bigdatastudy


Thomas Inman

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