Analytics: The Real-world Use of Big Data

©2012 IBM Corporation
Analytics:
The real-world use of big data
How innovative enterprises extract value from uncertain data
Findings from the research collaboration of
IBM Institute for Business Value and
Saïd Business School, University of Oxford

©2012 IBM Corporation|
Agenda
2
Key Findings2
Macro Findings1
Recommendations3

Macro findings

IBM Institute for Business Value and the Saïd Business
School partnered to benchmark global big data activities
4
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

Big data embodies new data characteristics created by
today’s digitized marketplace
Introduction to big data
Characteristics of big data
5

Nearly two out of three respondents reports realizing a
competitive advantage from information and analytics
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
6

Respondents define big data by the opportunities it creates
Introduction to 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
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%.
7

Three out of four organizations have big data activities
underway; and one in four are either in pilot or production
Macro findings
Total respondents n = 1061
Totals 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
8

Key findings

Five key findings highlight how organizations are moving
forward with big data
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
10

Improving the customer experience by better understanding
behaviors drives almost half of all active big data efforts
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
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
11

Customer-centric analytics is the primary functional
objective across macro industry groups, as well
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 Services
Healthcare /
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
12

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
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
13
Respondents with
active big data efforts
were asked which
platform components
were either currently
in pilot or installed
within their
organization.

Internal sources of data enable organizations to quickly
ramp up big data efforts
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.
14

Strong analytics capabilities – skills and software – are
required to create insights and action from big data
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.
15

Patterns of organizational behavior are consistent
across four stages of big data adoption
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 = 1061
Totals do not equal 100% due to rounding
16

Big data leadership shifts from IT to business as
organizations move through the adoption stages
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.
Total respondents = 1028
17

Challenges evolve as organizations move through the
stages, but the business case is a constant hurdle
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.
Total respondents = 973
18

Recommendations

An overarching set of recommendations apply to all
organizations focused on creating value from big data
Commit initial
efforts to drive
business value
Build analytical
capabilities based on
business priorities
1
Develop
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
20

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
21
Big data: Tapping into new sources of value

www.ibm.com/2012bigdatastudy
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