Thomas Inman

©2012 IBM Corporation

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

Tom Inman, Vice President, IBM Software Group

[email protected]

Findings from the research collaboration of

IBM Institute for Business Value andSaïd Business School, University of Oxford

©2012 IBM Corporation|

Agenda

2

Introduction to Big Data1

Recommendations4

Macro Findings2

Call to Action5

Key Findings3


Introduction to big data


Analytics is expanding from enterprise data to big data

from surveillance cameras trade events per second

meter readings per annum

Analyze product sentiment

Predict power consumption

Monitor events of interestIdentify potential fraud

Prevent customer churn

call detail records per hour are images, video, documents…

Improve customer satisfaction

Volume Velocity Variety

5 100’sof Tweets create daily

12 terabytesvideo feeds million

350 billion 1.5billion 80% data growth


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


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

5



Applications for Big Data Analytics

Homeland Security

Smarter Healthcare

Manufacturing

Finance Multi-channel sales

TelecomTraffic Control

Trading Analytics Fraud and Risk

Log Analysis

Search Quality

Retail: Churn, NBO



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

7


Characteristics of big data


Macro findings


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

9

Study overview

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

IBM Institute for Business Value

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


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

10

Macro findings

Total respondents n = 11442010 and 2011 datasets © Massachusetts Institute of Technology

Realizing a competitive advantage

Respondents were asked “To what extent does the use of information (including big data) and analytics create a competitive advantage for your organization in your industry or market.” Respondent percentages shown are for those who rated the extent a [4 ] or [5 Significant extent]. The same question has been asked each year.

Competitive advantage enabler A majority of respondents

reported analytics and information (including big data) creates a competitive advantage within their market or industry

Represents a 70% increase since 2010

Organizations already active in big data activities were 15% more likely to report a competitive advantage

A higher-than-average percentage of respondents in Latin America, India/SE Asia and ANZ reported realizing a competitive advantage

63%

58%

37%

2012

2011

2010

70% increase


Respondents define big data by the opportunities it creates

11


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

Defining big data

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%.


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

12

Macro findings

Total respondents n = 1061Totals do not equal 100% due to rounding

Big data activities

Respondents were asked to describe the state of big data activities within their organization.

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, use spreads quickly across the organization


Organizations are gaining value from working with IBM

Grow, retain and satisfy customers

Manage risk, fraud & regulatory compliance

Increase operational efficiency

Transform financial processes

Improvement in billed revenue retention rate

60%

Increase ininventory turns

50%

Trading decisions improved with 70% of counterparties

70%

50%Reduction in

planning cycle times

Macro findings


Key findings


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

15

Key findings

Big data is dependent upon a scalable and extensible information foundation2

The emerging pattern of big data adoption is focused upon delivering measureable business value5

Customer analytics are driving big data initiatives1

Big data requires strong analytics capabilities4

Initial big data efforts are focused on gaining insights from existing and new sources of internal data3


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

16

Key Finding 1: Customer analytics are driving big data initiatives

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

Other functional objectives The ability to connect data and

expand insights for internally focused efforts was significantly less prevalent in current activities

Total respondents n = 1061

Big data objectives

Top functional objectives identified by organizations with active big data pilots or implementations. Responses have been weighted and aggregated.

Customer-centric outcomes

Operational optimization

Risk / financial management

New business model

Employee collaboration


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

17

50%

11%

21%

16%

2%

42%

26%

13%

13%6%

59%20%

10%

7%5%

51%

19%

16%

10%4%

62%8%

11%

18%1%

32%

30%

27%

6%6%

Consumer Goods Financial ServicesHealthcare / Life Sciences

Manufacturing Public Sector Telecommunications

Key Finding 1: Customer analytics are driving big data initiatives

Customer-centric outcomes

Operational optimization

Risk / financial management

New business model

Employee collaboration


Santam Insurance: Predictive analytics improve fraud detection and speed up claims processing

18

Solution

Business Opportunity

Results

Case study

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.

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 cases from low-risk claims

Plans to use propensity modeling to enhance and refine segmentation process as more data becomes Like most insurers around the world, Santam was

losing millions of dollars paying out fraudulent claims every year

Expenses were being passed on to the customer in the form of higher premiums and longer waits to settle legitimate claims

To improve its bottom line and enhance customer satisfaction, the company needed to detect and stop insurance fraud early in the claims process

It also needed to find a way to isolate risky, fraudulent claims so that claims managers could more quickly process lower-risk claims

Identified a major fraud ring less than 30 days after implementation

Saved more than $2.5M in payouts to fraudulent customers, and nearly $5M in total repudiations

Reduced claims processing time on low-risk claims by nearly 90%

Cut operating costs by reducing the number of mobile claims investigations


Big data efforts are based on a solid, flexible information management foundation

19

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

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 the variety of data, a close second priority

A significant percentage of organizations are currently piloting Hadoop and NoSQL engines, supporting the notion of exponential growth ahead

Big data infrastructure

Respondents with active big data efforts

were asked which platform components were either currently

in pilot or installed within their

organization.


• Discover and integrate relevant information

• Analyze patterns and predict outcomes

• Visualize and explore for answers

• Take action and automate processes

• Optimize analytical performance and IT costs

• Manage, Govern and Secure Information

An approach that enable organizations to:

Big Data Platform

Solutions

Analytics and Decision Management

ContentAnalyticsContentAnalytics

Predictive AnalyticsPredictive Analytics

Information Integration and Governance

EnterpriseFraud

EnterpriseFraud

StreamComputing

StreamComputing

Data Warehouse

Data Warehouse

Decision Management

Decision Management

Visualization& DiscoveryVisualization& Discovery

HadoopSystemHadoopSystem

HealthcareAnalytics

HealthcareAnalytics

Next BestAction

Next BestAction

Social MediaAnalytics


Content Management

Content Management

ContentAnalyticsContentAnalytics

Predictive AnalyticsPredictive Analytics

Information Integration and Governance

EnterpriseFraud

EnterpriseFraud

StreamComputing

StreamComputing

Data Warehouse

Data Warehouse

Decision Management

Decision Management

Visualization& DiscoveryVisualization& Discovery

HadoopSystemHadoopSystem

HealthcareAnalytics

HealthcareAnalytics

Next BestAction

Next BestAction



Content Management

Content Management

Big Data Infrastructure: Systems, Storage and Cloud

A holistic and integrated approach to analytics and big data

Big data efforts are based on a solid, flexible information management foundation

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


Internal sources of data enable organizations to quickly ramp up big data efforts

21

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

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

Combining disparate internal sources with advanced analytics creates insights into customer behavior and preferences

Transactions Emails Call center interaction records

Big data sources

Respondents were asked which data

sources are currently being collected and analyzed as part of

active big data efforts within their organization.


Vestas: Better data analysis capabilities lower costsand improve effectiveness

22

Solution

Business Opportunity

Results

Case study

Vestas Wind Systems A/S optimizes capital investments based on 2.5 petabytes of information and big data technologies

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

Wind turbines are a multimillion dollar investment with a typical lifespan of 20-30 years

Placement depends upon a large number of location-dependent factors

Vestas has been unable to support data analysis of the very large data sets the company deemed necessary for precision turbine placement and power forecasting due to inadequate infrastructure and reliance on external models

Insights lead to improved decisions for wind turbine placement and operations, as well as more accurate power production forecasts

Greater business case certainty, quicker results, and increased predictability and reliability

Decreased cost to customers per kilowatt hour

Reduction by approximately 97 percent – from weeks to hours – of response time for business user requests

Greatly improves the effectiveness of turbine placement


Strong analytics capabilities – skills and software – are required to create insights and action from big data

23

Key Finding 4: Big data requires strong 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

technical and analytic skills required for big data is a challenge for most organizations with active efforts underway

Both hardware and software skills are needed for big data technologies; it’s not just a ‘data scientist’ gap

Analytics capabilities

Respondents were asked which analytics

capabilities were currently available within

their organization to analyze big data.


Automercados Plaza’s: Greater revenue throughgreater insight

24

Solution

Business OpportunityResults

Case study

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

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

$20M in inventory and more than six terabytes of product and customer data spread across multiple systems and databases

Unable to easily assess operations at individual stores using manual processes

Needed a comprehensive and timely view of operations that would support and improve decisions about business operations

Increased annual revenues by 30% Increased annual profits by $7M Decreased time to compile sales tax data by 98% Lowered losses on perishable goods, which comprise

approximately 35% of the chain's products Helped executives pinpoint optimal locations for four

new grocery stores


Patterns of organizational behavior are consistent across four stages of big data adoption

25

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

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


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

26

Additional Findings

CIOs lead early efforts 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

Later stages are more often centered on a single executive rather than a group; a single driving force who can make things happen is critical

Leadership shifts

Respondents were asked which executive is most closely aligned with the mandate to use big data within their organization. Box placement reflects the degree to which each executive is dominant in a given stage.



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

27

Additional Findings

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 increase as the use of machine-to-machine processing and embedded analytics expands

Speed to insight


Respondents were asked how quickly business users require data to be available for analysis or within processes. Box placement reflects the prevalence of that requirements within each a stage.


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

28

Additional Findings

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 as an obstacle once roll-out begins, again suggesting the need for earlier attention

Obstacles to big data

Respondents were asked to identify the top obstacles to big data efforts within their organization. Responses were weighted and aggregated. Box placement reflects the degree to which each obstacle is dominant in a given stage.



Recommendations


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

30

Commit initial efforts to drive business value

Build analytical capabilities based on

business priorities

1Develop

enterprise-wide big data blueprint

Create a business case based on

measurable outcomes

Start with existing data to achieve

near-term results

4

2 3

5

Recommendations


Customer analytics creates a high-impact start to big data

31

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

“I think big data will significantly impact the business delivery and

consumer landscape by helping service providers

and retailers better predict consumer needs and reduce overall costs

through better supply-chain management, increased

speed in delivery and higher sales”

– Entertainment /Media executiveUnited States

Customer analytics imperative

Customer analytics imperative Focus initial big data initiatives on areas that

can provide the most value to the business Customer analytics enable better service to

customers as a result of being able to truly understand customer needs and anticipate future behaviors

Dynamic customer expectations To effectively cultivate meaningful relationships

with customers, organizations must connect with them in ways their customers perceive as valuable

The value may come through more timely, informed or relevant interactions

Value may also come as organizations improve the underlying operations in ways that enhance the overall experience of those interactions


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

32

Key Recommendation 2: Develop enterprise-wide big data blueprint

“Currently organizations have a lot of data but

they do not know how to make use of it.

Knowledge is right there in the data, but we did

not have the tools to explore them until now.

Big data will help convert data into knowledge”

– Information Technology executiveISA IOT

Components of a blueprint

Aligns organization around big data Encompasses the vision, strategy and requirements

for big data within an organization Establishes alignment between the needs of

business users and the IT project plan Creates a common understanding of how the

enterprise intends to use big data to improve its business objectives

Defines the scope of big data Identifies the key business challenges to which big

data will be applied Outlines business process requirements that define

how big data will be used Documents the future architecture Serves as basis for developing an IT roadmap to

implement big data solutions in ways that create sustainable business value


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

33

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

“Big data will allow organizations to analyze and correlate data from their internal processes

and their business environments in a way that is not possible today, even

with a lot of business intelligence and analytical

tools that already exist”

– Consumer Products executiveBrazil

Focus on internal data

Pragmatic approach The most logical and cost-effective place to

start looking for new insights is within the enterprise

Looking internally first allows organizations to:• Leverage existing data• Use software and skills already in place• Deliver near-term business value • Gain important experience before

adding new data, skills and tools

Speed to value Enables organizations to take advantage of

the information stored in existing repositories while infrastructure implementations are underway

As new technologies become available, big data initiatives can be expanded to include greater volumes and variety of data


Analytics capabilities include both skills and tools

34

Key Recommendation 4: Build analytical capabilities based on business priorities

“I think big data is going to play a very important role

in the near future since the dynamics of IT are

changing very quickly. In coming years, the

company with the superior skills will lead to the path

of success and making the world a better place”

– IT executiveUnited Kingdom

Key capabilities required

Addressing the skills gap Organizations face a growing variety of analytics

tools while also facing a critical shortage of analytical skills

Big data effectiveness hinges on addressing this significant gap

In short, organizations will have to invest in acquiring both tools and skills

Proactive development As part of this process, it is expected that new

roles and career models will emerge for individuals with the requisite balance of analytical, functional and IT skills

Attention to the professional development and career progression of in-house analysts – who are already familiar with the organization’s unique business processes and challenges – should be a top priority for business executives

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


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

35

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

“I believe big data will force companies to re-think their

structures and business divisions to focus more on those areas that are most

relevant to the accomplishment of the strategy and corporate goals, and not just financial,

but also in terms of customer satisfaction, product

development, research, etc.”

– Insurance industry executiveMexico

Business case details

Articulating the case Many organizations are basing their business

cases on the following benefits that can be derived from big data:

Smarter decisions – Leverage new sources of data to improve the quality of decision making

Faster decisions – Enable more real-time data capture and analysis to support decision making at the “point of impact”

Decisions that make a difference – Focus big data efforts toward areas that provide true differentiation

Secure executive support An important principle underlies each of these

recommendations: business and IT professionals must work together throughout the big data journey

Active involvement and sponsorship from one or more business executives throughout this process is needed to advocate for investments


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

36

Recommendations by stage: Educate to Explore

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.

Educate to Explore: Create a foundation for action


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

37

Recommendations by stage: Explore to Engage

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.

Explore to Engage: Put plans into action


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

38

Recommendations by stage: Engage to Execute

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.

Engage to Execute: Understand the opportunities and challenges ahead


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

39

Recommendations by stage: Execute

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.

Execute:Embrace the innovation of big data


COMPANY CONFIDENTIAL

1. Sales & Marketing Effectiveness

2. Advanced Data Expansion

3. Network Optimization

Dealer incentives, Pricing, District based approach

Targeted marketing and increased cross-sell / up-sell

Reduction in subscriber churn through advanced subscriber analytics

Filling capacity , Sachet approach on pricing

Freemium approach,

Improve network utilization

Optimize network capital investments

Optimize network operating investments

Key Business Benefits

Business Areas

4. Finance

Benchmarking, Spend smart, Financial analysis , post mortem

Product Profitability Analysis

Create a business case based on measurable outcomes


jGetting 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

Getting started

42

Big data: Tapping into new sources of value

www.ibm.com/2012bigdatastudy


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