Analytics: A Blueprint for Value - Executive Overview

©2013 IBM Corporation

Analytics: A blueprint for value Converting big data and analytics insights into results

IBM Institute for Business Value

October 29, 2013

©2013 IBM Corporation |

Table of contents

Table of contents

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About the research

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3

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4

Key findings: Nine levers to value creation

Recommendations: Creating an analytic blueprint

Getting started: Accelerating value from analytic investments


Table of contents

Table of contents

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About the research




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3

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Understanding how to create value from data has

been the focus of IBM’s analytics studies for 5 years

4

Analytics:

The new path to value

Operationalizing

analytics in

sophisticated

organizations

Analytics:

The widening

divide

Mastering analytic

competencies

Analytics:

The real world use

of big data

Fundamentals

of big data

Analytics:

A blueprint for value

Extracting value

from data and

analytics

2010 2011 2012 2013

About the research

The intelligent enterprise and

Breaking away with BAO

2009

Defining analytics

as a strategic

asset


To identify best practices, we examined the actions

taken by those creating the most value from analytics

Leaders create value as they:

Act strategically and tactically on data

Improves business performance with analytics

Create a competitive advantage from insights

Realize a return on investment within a year

Demographics of Leaders Represent 19 percent of total valid respondents

Evenly divided between North America, Asia Pacific,

Latin America and Europe/Middle East/ Africa

Most have more than 10,000 employees

Majority are mature companies that have been in

business more than 25 years

Key findings: Leaders

5

Leaders were defined based on their self-reported

performance within their industry and market. n=830

Identifying Leaders

Source: Analytics: A blueprint for value – Converting big data and analytics into results, IBM Institute for Business Value © 2013 IBM


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About the research

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We identified three levels of influence on value creation

among the most differentiating analytic capabilities

Key findings: Value creation levers

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Type of influence each lever has on value creation.

Enable

Drive

Amplify

Basis for big data and analytics

Boosts value creation

Establishes the foundation for

using big data and analytics to

differentiate performance

The absence of these levers

inhibit the organization’s ability to

create value

Focus on these levers will boost

an organization’s ability to create

value from data analytics

Needed to realize value

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2

3



Understanding how levers influence value is key to

realizing value from analytics investments

Key findings: Value creation levers

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Sponsorship Funding

Data

Expertise

Trust

Executive support and involvement

Development and access to skills and capabilities

Financial rigor in analytics funding process

Organizational confidence Data management practices

Source of Value Platform

Culture

Measurement

Actions and decisions that generate value

Evaluating impact on business outcomes

Integrated capabilities delivered by

hardware and software

Availability and use of data and analytics

Drive

Amplify

Enable

Basis for big

data and

analytics

Needed

to realize

value

Boosts

value

creation


Analytic levers – or sets of capabilities – defined and aligned by the influence they exert on value creation.


Big data and analytics is enabled by a clear

understanding of value creation and a strong platform

Key findings: Enable levers

9

Measure impact

and model the

future

Integrate hardware

and software to

manage big data

Key

findings

Ensure alignment between analytic focus and value

creation

Source of Value Platform Measurement Enable Basis for big

data and

analytics

Sponsorship Funding

Data

Expertise

Trust Culture Drive

Amplify


Leaders …

Align investments to

value outcomes

Focus on revenue

opportunities

Accelerate the speed

of analytics

Generate innovative ideas

Leaders …

Define success metrics

in advance

Model future

outcomes

Anticipate future

events and outcomes

Measure actual results

Leaders …

Use predictive analytics

Analyze a variety of data

Implement hardware

capable of handling big

data

Analyze streaming data


Leaders remove doubt and create an environment

where data is a priority

Key findings: Drive levers

10

Source of Value Measurement Platform Enable

Data Culture Trust

Create trustworthy

relationships

Make decisions

based on facts

Key

findings

Create confidence

with governance and

security

Needed to

realize value

Drive

Sponsorship Funding Expertise Amplify


Leaders …

Ensure data and analytics

needed to make decisions

are available

Distribute data and

analytics to those who

need it

Make majority of

decisions based on facts

Leaders …

Trust in the data and

analytics available

Adopt rigid privacy

and security

standards

Implement a strong

governance

infrastructure

Leaders …

Believe others will

do a competent job

Establish a high

degree of trust

Rely on business

and data analysts


A common vision, pooled funding and expertise amplify

the ability to generate value from data and analytics

Key findings: Amplify levers

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Sponsorship Expertise Funding

Create value with

rigor and

collaboration

Increase

knowledge-sharing

opportunities

Establish a common

vision to guide actions

and deliver value

Key

findings

Data Trust Culture Drive


Boosts

value

creation

Amplify


Leaders …

Advocate for the use

of data and analytics

Collaborate to define a

business-driven

enterprise strategy

Implement enterprise

standards, metrics and

methodologies

Leaders …

Implement a metrics-

based funding process

Require quantifiable

business case

Pool funding for

roadmap and

resources

Leaders …

Share analytics subject

matter experts

Co-locate business

analytics talent

Increase knowledge

sharing

Formalize a career

path for analytics talent


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About the research

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These levers create an analytics blueprint that defines the

business-driven requirements within a familiar framework

Recommendations

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Funding

Source of Value

Sponsorship

Data

Expertise

Platform Trust

Culture

Measurement

Strategy Organization Technology

Sponsorship Funding Expertise

Culture Drive

Amplify


Data Trust



Organizations can develop a business-driven blueprint

by following three key sets of recommendations

Recommendations

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Strategy

Technology

Organization

Accelerate analytics with a results based program

Drive change with analytics as a core competency

Instill a sense of purpose. Establish a business-driven agenda for analytics

that enables executive ownership, aligns to enterprise strategy and business

goals, and defines new business capabilities.

Architect for the future. Enable the agenda with shared analytical

expertise, new technologies, and a simplified and flexible platform.

Enable the organization to act. Create a data driven culture built on

relationships to generate business value.

Enrich the core analytics platform and capabilities



Analytics need executive ownership, business goals

alignment and prioritized funding

Recommendations

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• Establish the strategic intent of data and analytics investments

• Use measureable business outcomes to transition from executive strategy

to line-of-business actions

• Convey an enterprise-wide sense of ownership through communication

and endorsement

Funding

Source of Value

Sponsorship

• Invest the time to develop value-based business cases

• Allocate funding to maximize growth and efficiency

• Prioritize funding based on alignment to business outcomes

• Explore the growth opportunities that abound in the still-emerging

digital marketplace

• Focus on opportunities for operational innovation

• Determine the business capabilities needed to create value

Strategy Accelerate analytics with a results based program

Instill a sense of purpose



Recommendations

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Data

Technology Enrich the core analytics platform and capabilities

Architect for the future

• Govern data with rigor

• Improve data quality, accessibility and availability

• Capture a variety of big data sources

Platform

• Simplify and modernize existing platform

• Increase reusability in application development and maintenance

• Pilot new technologies that enable business capabilities

Expertise • Supplement business knowledge with analytics knowledge

• Establish a formal career path for analytics professionals

• Use partners to supplement skills gaps

Analytics need shared analytical expertise, new

technologies, and a simplified and flexible platform



Analytics needs a data-driven culture built on

relationships and evaluated on outcomes

Recommendations

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Trust

Culture

Organization Drive change with analytics as a core competency

Enable the organization to act

• Streamline the data cycle to deliver more timely and relevant insights

• Automate key portions of the analytics cycle

• Actively endorse the power and benefits of data

• Recognize trust is a key ingredients to value creation through analytics

• Invest time to create trustworthy relationships

• Transform roles to share responsibilities and outcomes

Measurement • Create a feedback loop based on the outcomes

• Identify and define the specific key performance indicators (KPIs)

• Evaluate the investments' impact to value creation



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About the research

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Developing an analytics blueprint is the first step to

converting data and analytic insights into results

Call to action

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Strategy Instill a sense of purpose

Is there a common agenda for analytics among leaders?

Are your investments aligned to value delivery?

Does the funding structure support cross-silo initiatives?

Technology Architect for the future

Are data and analytics skills nurtured within your organization?

Are data management practices strong enough to instill confidence?

Is the analytic infrastructure architected for today’s challenges?

Organization Enable the organization to act

Are data and analytics part of your decision making processes?

Can your define the impact from analytics investments?

Is the level of trust sufficient to rely on others for data and analytics?


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