Cracking the Data Conundrum: How Successful Companies Make #BigData Operational

Cracking the Data Conundrum:
How Successful Companies Make Big
Data Operational

2
Successful Big Data Implementations Elude
Most Organizations
Only 13% of
organizations have
achieved full-scale
production for their Big
Data implementations.
Global organizational
spending on Big Data
exceeded $31 billion in
2013, and is predicted
to reach $114 billion in
2018.
When the economic history of 2014 is
written, there will be one omnipotent
technology trend: Big Data. As Figure 1
shows, the growth in interest in Big Data
far outranks any other major technology
trend for the year.
This is not just intellectual curiosity.
Investments by large corporations are
following this trend. Global organizational
spending on Big Data exceeded $31
billion in 2013, and is predicted to reach
$114 billion in 20181
. Given this level of
interest and action, we conducted a global
survey of leading Big Data practitioners
to understand their priorities and the
challenges they face in implementing Big
Data initiatives (our research methodology
is outlined at the end of this paper).
Our survey conﬁrmed Big Data’s
importance for large organizations. Nearly
60% of executives in our survey believe
that Big Data will disrupt their industry
within the next three years.
However, recognizing the importance
of Big Data is quite different from fully
embracing it. We found that while a large
number of organizations are currently
experimenting with their initiatives, many
have not fully embedded Big Data in
their operations. In fact, our research
shows that only 13% have achieved
full-scale production for their Big Data
implementations (see Figure 2).
Figure 1: Interest over Time for Specific Tech Trends, 2004-2014, Google Trends
Source: Google Search Trends accessed in December 2014
2005 2007 2009 2011 2013 2014
Big Data
Internet of
Things
SMAC

3
Nearly 60% of senior
executives believe that
Big Data will disrupt
their industry within
the next three years.
Only 27% of the
executives we surveyed
described their Big
Data initiatives as
“successful”.
Figure 2: Status of Big Data Implementations
Source: Capgemini Consulting, “Big Data Survey”, November 2014
5%
19%
29%
35%
13%
Not implemented yet, no budget has been allocated
Not implemented yet, but a budget has been allocated
and we have identiﬁed focus areas
Proof of Concept: we are working on Proof-of-Concepts
for selected use-cases
Partial Production: predictive insights are integrated
into some of our business operations
Full-scale Production: predictive insights are
extensively integrated into business operations
The most troubling development is that
most organizations are failing to beneﬁt
from their investments. Only 27% of
respondents described their Big Data
initiatives as “successful” and only 8%
described them as “very successful”*.
In fact, organizations were found to
be struggling even with their Proof-
of-Concepts (PoCs), with an average
success rate of only 38%.
This raises a fundamental question. If
organizations recognize the importance of
Big Data, and are investing in it, then what
is standing in the way of success? Our
research revealed that the top challenges
that organizations face include: dealing
with scattered silos of data, ineffective
coordination of analytics initiatives, the
lack of a clear business case for Big Data
Lack of strong
data management
and governance
mechanisms, and the
dependence on legacy
systems, are among
the top challenges that
organizations face.
funding, and the dependence on legacy
systems to process and analyze Big Data
(see Figure 3).
*An initiative was considered to be “successful” only if it met most or all of its objectives, and “very successful” if it exceeded its objectives

4
Figure 3: Key Challenges for Big Data Implementation
46%
39%
35%
31%
27%
27%
25%
22%
18%
15%
12%
Scattered data lying in silos across various teams
Absence of a clear business case for funding and implementation
Ineffective coordination of Big Data and analytics teams across
the organization
Dependency on legacy systems for data processing and
management
Ineffective governance models for Big Data and analytics
Lack of sponsorship from top management
Lack of Big Data and analytics skills
Lack of clarity on Big Data tools and technology
Cost of specific tools and infrastructure for Big Data and analytics
Data security and privacy concerns
Resistance to change within the organization
Figure 4 highlights these four challenges
and some of the underlying causes, and
below we take a closer look at two of the
most significant:
 Scattered data: Seventy-nine
percent of organizations have not
fully integrated their data sources
across the organization. This means
decision-makers lack a unified view
of data, which prevents them from
taking accurate and timely decisions.
Filippo Passerini, CIO of US-based
consumer products leader P&G,
highlights the importance of data
veracity: “To move the business to
a forward-looking view, we realized
we needed one version of the truth.
In the past, decision-makers spent
time determining sources of the data
or who had the most accurate data.
This led to a lot of debate before real
decisions could be made2
.” Unlike
P&G, which has transformed its data-
driven decision-making (see Exhibit
1, “P&G: Lessons in Creating a Data-
Driven Culture”), most organizations
are far from being able to use data
effectively.
 Ineffective coordination: A major
stumbling block is a lack of adequate
coordination among analytics teams.
A significant number of organizations
operate with scattered pockets
of analytics resources or with
decentralized teams that function
without any central planning and
oversight. As a result, best practices
from successful implementations are
not shared across the organization,
initiatives are not prioritized, and
resources are not deployed in the
most effective ways. Eric Spiegel,
CEO of Siemens USA, highlights the
organizational challenges of Big Data
implementations: “Leveraging Big
Data often means working across
functions like IT, engineering, finance
and procurement, and the ownership
of data is fragmented across the
organization. To address these
organizational challenges means
finding new ways of collaborating
across functions and businesses3
.”

5
Figure 4: Underlying Causes of Big Data Challenges
79% 35%
67%
54% 47% 53%
36% 31%
Scattered data lying in silos across the organization
Absence of a clear business case for funding
and implementation
Dependence on legacy systems for data
processing and management
Ineffective coordination of Big Data and
analytics teams across the organization
79% of organizations have not
completely integrated their data
sources across the organization
67% do not have
well-deﬁned criteria
to measure the
success of their Big
Data initiatives
53% do not follow a
top-down approach
for Big Data strategy
development
54% do not have joint
project teams where
business and IT executives
work together on Big Data
initiatives
47% either have scattered
pockets of resources or
follow a decentralized model
for analytics initiatives
Only 31% use
open source
Big Data and
analytics tools
Only 36% use
Cloud-based Big
Data and analytics
platforms
Only 35% have robust processes for
data capture, curation, validation
and retention

6
US-based retail chain
Nordstrom has set up
the Nordstrom Data Lab
to develop new offerings
backed by data-driven
insights.
Figure 5: Comparison of Success Rates for Planned and Ad-hoc Approaches
What Separates Successful Big Data
Implementations?
There are many factors that go into
the making of a successful Big Data
implementation. However, the single
biggest factor that we observed was
that organizations that have a strong
operating model stood apart. This
operating model has multiple distinct
elements, which include, among others,
a well-defined organizational structure,
systematic implementation plan, and
strong leadership support.
Successful Organizations
Establish a Well-Defined
Organizational Structure
for their Big Data and
Analytics Initiatives
Big Data initiatives are rarely, if ever,
division-centric. They often cut across
various departments in an organization
and consequently, coordination and
governance are usually significant
implementation challenges. Organizations
that have clear organizational structures
for managing rollout can minimize the
problems of having to engage multiple
stakeholders. Our research showed that
the success rates of Big Data initiatives
are a direct function of the structural
cohesion of the lead unit (see Figure 5).
Organizations that have
adopted a centralized
structure for their Big
Data and analytics
units report higher
levels of success than
their peers who have
ad-hoc or decentralized
teams.
Scattered
Pockets
Ad-hoc, isolated
analytics teams
43%
27%
20%
53%
Decentralized
Separate analytics
teams for separate
departments
Centralized
Central team acting
as a competence
center for Big Data,
and coordinating
initiatives for all
business units
Business Unit
Analytics team as a
distinct profit-making
division

7
As Figure 5 shows, success rates for
organizations with an analytics business
unit are nearly 2.5 times those that
have ad-hoc, isolated teams. There
are significant merits to a centralized
set-up. The centralized approach can
bring together technology and business
executives to conceptualize new use-
cases and define best practices that
other teams can leverage. US-based
retail chain Nordstrom, for instance, has
set up the Nordstrom Data Lab to develop
new offerings backed by data-driven
insights. The lab is a multi-disciplinary
team of data scientists, mathematicians,
statisticians, programmers, and business
professionals. It follows a continuous
deployment model to build and test
prototypes, and take new products to
market rapidly4
.
A leading global automotive major has
followed a similar approach and set up
a central data analytics unit that acts as
a service provider to all teams worldwide
for Big Data activities. The head of the
unit describes the role of the team in
these words: “We act as a core team
that provides expertise on data and
analytics to our global business teams.
We define the methodology for Big Data
analytics programs and establish global
standards for data quality that business
teams are required to follow. We also
evaluate hardware and software tools for
Big Data analytics to determine the most
appropriate solutions for our organization,
and we make these available to business
teams to help them manage and use
data5
.”
Adopt a Systematic
Implementation Approach
to Focus Investments
Wisely
One key factor that separates the winners
from the also-rans is how they approach
implementation. Intuitively, it would seem
that a systematic and structured approach
should be the way to go in large-scale
implementations. However, our survey
shows that this philosophy and approach
are rare. Seventy-four percent of
organizations did not have well-defined
criteria to identify, qualify and select Big
Data use-cases. Sixty-seven percent of
companies did not have clearly defined
KPIs to assess initiatives. The lack of a
systematic approach affects success
rates (see Figure 6).
Figure 6: Comparison of Success Rates for Planned and Ad-hoc Approaches
51%
28%
Well-Defined Criteria
for Use-Case
Selection
Clear Roadmap with
Timelines and
Milestones
Well-Defined KPIs to
Measure Success
of Initiatives
51%
22%
53%
29%
% of successful initiatives
45%55% 26%74% 33%67%% of companies
No Yes No Yes No Yes

8
Have a Strong Leader at the
Top Driving the Big Data
Initiatives
Previous Capgemini Consulting research
into digital transformation, with the MIT
Center for Digital Business, established
the importance of top-down leadership
in driving implementation6
. Big Data, a
central pillar of digital transformation,
requires the same approach. Our
research showed that organizations
that have successfully implemented
Big Data initiatives usually have clearly
defined leadership roles for Big Data and
analytics. For instance, US-based Bank
of America, a pioneer in the use of data
in the banking industry, appointed a Chief
Data Officer (CDO) to champion data
management policies and standards, set
up the bank’s data platform, and simplify
tools and infrastructure7
. On the other
hand, Norway-based publishing major
Schibsted Group, a leader in the media
industry in the use of data analytics,
has followed a different approach.
Schibsted’s analytics initiatives are
being led by its VP of Strategy and Data
Analytics8
. Organizations can choose
from multiple approaches, but the key
lies in ensuring that Big Data initiatives
receive the necessary stewardship. A
senior leadership position serves to
achieve that. Further, organizations must
also ensure that the Big Data leader that
they appoint is evaluated based on their
ability to embed insight into the front-
line business and have direct impact on
business KPIs.
Leadership is also crucial to foster a
culture of data-driven decision-making
within the organization (see Exhibit 1 on
P&G). The head of analytics at a leading
logistics company describes his efforts
at driving a data-driven culture: “Change
management is one of the biggest
challenges of Big Data implementation.
Analytics needs to be integrated with
processes. We had to educate and train
our field force over and over again in
order to make analytics a part of their
daily routine9
.”
US-based Bank of
America appointed
a Chief Data Officer
(CDO) to champion
data management
policies and standards,
set up the bank’s data
platform, and simplify
tools and infrastructure.
However, while the results of such
leadership-driven initiatives are quite
visible, not many organizations have
taken steps to put it in action. Our
research showed that only 34% of
companies have a Chief Data Officer, or
an equivalent role.
Leverage Multiple Channels
to Build their Big Data
Capabilities
The Big Data talent gap is something
that organizations are increasingly
coming face-to-face with. In the UK,
for example, 4 out of 5 data-intensive
businesses say they are struggling to
find the skills they need to address
growing demand10
. Closing this gap is
a larger societal challenge. However,
smart organizations realize that they
need to adopt a multi-pronged strategy.
They not only invest more on hiring and
training, but also explore unconventional
channels to source talent. Consider,
for instance, how P&G has partnered
with Google to enhance its employees’
analytics skills. The two companies
have engaged in employee exchange
programs for the past five years. While
employees from Google gain from P&G’s
expertise in advertising, those from P&G
get to learn from Google’s expertise in
data analytics11
.
Other mechanisms to acquire Big Data
talent include partnering or acquiring Big
Data startups, and setting up innovation
labs in high-tech hubs such as Silicon
Valley. For instance, UK-based retailer
Tesco’s success with Big Data analytics
can be attributed to its acquisition of
consumer data science firm Dunnhumby
in 200612
. Walmart, on the other hand,
has set up “@WalmartLabs”, an
innovation center based in Silicon Valley,
which is helping the retailer enhance
customer experience through innovative
uses of Big Data. @WalmartLabs in turn
acquired Inkiru – a startup specializing
in predictive analytics – to strengthen
its analytics capabilities. Through the
acquisition, @WalmartLabs not only
gained access to Inkiru’s suite of
technologies but also to its team of data
scientists13
.
Startups are increasingly at the forefront
of data analytics and large organizations
are realizing that they need to engage
with startups extensively. The head of
analytics at a leading gaming company
that uses Big Data extensively, and
who has a team of more than 70
data scientists, highlights the need to
leverage startups: “We believe that small
firms are more innovative than large
ones, especially when you look at very
advanced types of analytics. We are
ready to acquire skills and tools that
can help us strengthen our capabilities
further and we are keeping a close
watch on innovative startups14
.”
@WalmartLabs
acquired Inkiru – a
startup specializing in
predictive analytics – to
strengthen its analytics
capabilities.

9
Exhibit 1 - P&G:Lessons in Creating a Data-Driven Culture
P&G is among the foremost companies in the world in the use of data and analytics. It is also a striking example
of the impact of strong leadership on establishing a data-driven culture in an organization. When Filippo Passerini
took over as CIO of P&G in 2004, he renamed the IT department to “Information and Decision Solutions (IDS)”. The
renaming was based on Passerini’s belief that data and analytics needed to play a more central role in decision-
making at P&G. Since then, the IDS unit has spearheaded several initiatives that have transformed the way in which
decisions are taken at P&G.
Some of the key innovations launched by Passerini’s team include:
Supporting Real-Time Decision-Making through “Decision Cockpits”: Passerini’s team developed
“Decision Cockpits” – an initiative to provide a single source of truth for data to all decision-makers across
geographies and business units. Decision Cockpits are dashboards that provide executives with visual displays
of data on business performance and market trends. The dashboards can be customized according to individual
needs. They allow executives to drill-down to granular views of data at a country, brand or product-level and also
provide real-time automated information alerts. Decision Cockpits have been widely adopted at P&G with more than
58,000 executives using them every week. This in turn has helped P&G speed up decision making and reduce time
to market.
Creating Immersive Environments for Decision-Making with “Business Spheres”: In addition to
providing decision-makers with real-time, consistent and relevant information, Passerini’s team also enables them
to collaboratively review data and take actionable decisions. Passerini’s team has set up visually immersive data
environments called “Business Spheres”. Within a Business Sphere facility, executives are physically surrounded
by screens that display complex data from a variety of sources. The visualization techniques employed in Business
Sphere facilities help executives uncover opportunities and exceptions from the data and ask focused business
questions. P&G has more than 50 such facilities across the world.
Source: P&G website
Source: WSJ Blogs, P&G Finds a ‘Goldmine’ in Analytics”, February 2013; Harvard Business Review, “How P&G Presents Data to Decision-Makers”,
April 2013; InformationWeek, “P&G’s CIO Details Business-Savvy Predictive Decision Cockpit”, September 2012; CIOInsight.com, “Data Wrangling:
How Procter and Gamble Maximizes Business Analytics”, January 2012; CIO.com, “P&G’s Filippo Passerini Stands Out as Stellar Example of a
Strategic CIO”, December 2014; PG.com, “Business Sphere GBS”

10
Putting the Pieces Together – Undertaking the
Implementation Journey
Organizations should
consider setting up
a “data lab” – an
incubation structure
offering a complete
technical and human
environment for
developing PoCs.
Get Your Operating
Model Right
Getting Big Data operational hinges on a
number of factors. These include setting
up a strong governance framework,
building the right data management
capabilities, developing a clear strategy
to build analytics skill-sets, and creating
the right technological foundation.
Organizations need to take concrete
measures in each of these areas in order
to maximize the benefits that they can
derive from Big Data (see Figure 7).
Figure 7: Building Blocks of a Big Data Operating Model
Establish a
Robust
Governance
Framework
Define Policies
and Procedures
for Management
of Data Assets
Set up the
Technological Base
for Big Data
Initiatives
Develop
Big Data
Competencies
Invest in tools for data governance,
master data management and
metadata management
Adopt a utility pricing model for the
provisioning of Big Data infrastructure and
tools
Set up an environment that supports
SQL-based as well as data science based
consumption models
Minimize risk exposure by testing multiple
solutions for relevance and feasibility
Establish a well-defined organizational unit
for Big Data initiatives that is closely
integrated with business teams, to deliver
a local business view of insights
Create a senior leadership role for Big
Data and analytics to signal the shift to a
data-driven culture
Establish clear criteria and metrics to
select use-cases and measure the
success of initiatives
Automate the collection of metrics and
KPIs as well as the governance of data
(ex: lineage of data, risks associated
with data)
Define rules for prioritization, storing and
sharing of internal data
Clarify ownership of external and partner
data
Create an integrated set of master data
and metadata spanning internal, external,
structured and unstructured data sources
Establish procedures for data quality,
security and privacy
(opt-in/opt-out, anonymization,
authentication)
Up-skill existing analytics resources but
recognize the differing value delivered by
statisticians and data scientists
Organize hackathons and partner with
academic institutions to identify and
recruit analytics talent
Recruit analytics resources with a mix of
technical and business skills
Develop alternate career paths for
strategic and complex hires such as data
scientists
Source: Capgemini Consulting Analysis
Take an Iterative Approach
Towards Implementation
Organizations face the challenging
task of prioritizing amongst a variety
of use-cases of Big Data. This means
working with a “fail-fast” approach to
filter out the unfeasible use-cases and
narrow down the optimal ones. An agile
methodology will also help In the face
of increasing competition. The key idea
is to implement basic versions quickly,
and then iterate to plug defects and
incorporate changes. Proof-of-Concepts
(PoCs) give companies this flexibility, and
help shorten overall development times.

11
Figure 8: Best-practice – AT&T’s Rapid Implementation Approach
Source: Cnet.com, “Meet the group trying to make AT&T very un-AT&T like”, June 2012; Globes.co.il, “Why Cisco paid $475 for Intucell”, January 2013
Organizations need to
work with a “fail-fast”
approach to filter out
the unfeasible use-cases
and narrow down the
optimal ones.
Organizations should also consider
setting up a “data lab” – an incubation
structure offering a complete technical
and human environment for developing
PoCs. It is particularly helpful in attracting
and uniting internal and external talent,
and promoting cross-fertilization and
collaboration.
AT&T’s “Foundry”, an innovation center
that offers a fast paced and collaborative
environment, is a great example of the
application of these concepts. Ideas
AT&T claims total time to launch is 3x faster, in weeks as opposed to years
BU executives
submit problem
queries
Ecosystemis
leveraged to ﬁnd
matching ideas
Executive review -
fastpitches
and idea selection
Ideas go through
a fail-fast
development
cycle
Solutions go to
market
400 fast
pitches
each year
40 PoCs
launched
Minimum of 10
commercialized
Beta
12 weeks
Commercialization
12 weeks
PoC
6 – 12 weeks
Partners
Innovation
Pipeline
Ecosystem
?
are generated by leveraging the entire
eco-system of the company, including
partners. The best ideas are selected
through an executive review and put
through a fail-fast development cycle.
The company claims its total time to
launch has become three times faster
than before, in weeks as opposed to
years (see Figure 8).

12
Ensure Stakeholder Buy-
in to Secure Funding and
Approval for Your Initiatives
The returns from investments in emerging
digital technologies such as Big Data are
often highly speculative, given the lack
of historical benchmarks. Consequently,
in many organizations, Big Data
initiatives get stuck due to the lack of a
clear and attributable business case. To
address this challenge, Big Data leaders
should manage investments by using a
similar approach to venture capitalists.
This involves making multiple small
investments in a variety of PoCs, allowing
rapid iteration, and then identifying PoCs
that have potential and discarding those
that do not. Pilots should be conducted
for successful PoCs and the results from
the pilots should be used to build the
business case.
Additionally, in order to secure funding for
Big Data initiatives, Big Data leaders will
need to convince multiple stakeholders,
across diverse functions, about the
value of the initiatives. Big Data needs
to be pitched as a value creation lever
for both Business and IT (see Exhibit 2,
“Maximizing the Chances of Funding for
your Big Data Initiative”).
Removing Personally
Identifiable Information
(PII) from data reduces
the risk of potential
security issues.
Manage your Risk
by Setting up Strong
Safeguards for Security and
Privacy
The growing risk of data loss, either
due to hacking, or security loopholes,
is something that is top-of-mind for
organizations and their customers. For
organizations implementing Big Data
initiatives, having explicit opt-in/opt-
out mechanisms are one way to allay
customer concerns. “Anonymizing” data
before use is another – the risk involved
is significantly reduced if Personally
Identifiable Information (PII) is removed
from data. Kim Walker, a partner at law
firm Thomas Eggar LLP, confirms the risk
factor of identifiable information – “Use of
big data which has not been anonymized
is clearly an area of risk15
”.
The temptation for gaining first-mover
advantage can drive companies to
launch their initiatives at the cost of
ignoring security issues. But the risks
involved can make this a costly mistake.
Therefore, companies need to establish
strict risk management and clearance
procedures to ensure that initiatives are
launched only after all security loopholes
have been plugged.
* * *
Big Data is business intelligence –
enterprise brainpower that offers
significant rewards. Leaders like GE
and Amazon are rewriting the rules of
business through their concerted use of
Big Data. While these organizations serve
as powerful reminders of the disruptive
potential of Big Data, the majority of their
peers fall far short of securing its value.
Familiar organizational challenges are
getting in the way, from the dead weight
of legacy systems to teams’ inability – or
unwillingness – to coordinate effectively.
Solving these problems means tackling
the basics of the operating model. You
need the right structure, a disciplined
approach to implementation, and truly
determined leadership. Big Data will only
realize its potential when the operational
building blocks have been carved
out, put in place, and accepted by the
organization. Can organizations do all
this, and harness Big Data as a source of
true competitive advantage? The answer
to this question will unfold over the next
few years.

13
Exhibit 2 - Maximizing the Chances of Funding for your Big Data Initiative
To maximize your chances of funding, you need to ensure that you have taken a holistic, organization-wide view and
paid attention to softer points for converting a naysayer to an advocate.
Highlight the disruptive impact of Big Data
As a first step, ensure that senior stakeholders across Business and IT are aware of the disruptive potential of Big Data.
Highlight real-world instances of data-driven decision making that are altering traditional business models and customer
relationships. For instance, the use of Big Data has allowed GE to generate $1 billion annually in service revenues.
GE offers predictive maintenance, remote monitoring and asset tracking services based on the data that it collects
from sensor-equipped machines. It expects revenues from such services to grow to $5 billion by 2017. Traditional
manufacturing firms risk losing out on these new sources of growth and competitive advantage if they do not strengthen
their Big Data capabilities.
Traditional retailers, on the other hand, have been left behind by competitors like Amazon that are using Big Data to
dramatically improve customer service. Amazon’s recommendations engine, which has been credited with generating
as much as 35% of its sales, allows it to offer a highly personalized browsing experience based on analysis of customers’
past purchase behavior.
These real-world examples of the impact of Big Data serve to create a sense of urgency among senior stakeholders on
the need to adopt Big Data rapidly.
Look at cross-organizational areas of impact
A Big Data initiative is bound to impact on various parts of the organization. For instance, it can reduce the importance
of certain business functions and cause political friction. On the other hand, it can benefit multiple business units. Also,
it can augment the role and importance of the Analytics unit within the larger organization. Such softer factors should
also be considered when building the business case in terms of risks, costs and benefits.
Identify champions within the organization
Any Big Data initiative requires co-ordination between multiple teams – Business, IT and others – in order to be
successful. You need to recruit champions to support and further your cause, without which the business case will
collapse. Identify stakeholders that would be affected by your initiative and determine and address their concerns. For
instance, in order to launch a Big Data initiative focused on increasing customer acquisition and retention, the Marketing
team could identify champions from the Sales, IT and Finance teams.
Tailor the business case for the audience
The decision maker for the funding may be the CEO, CIO, CFO, CMO, etc. Ensure that the business case addresses
concerns and provides data for the audience at hand. For instance, the CFO may be more interested in detailed RoI
calculations whereas the CMO may be more concerned about the impact of the initiative on other marketing programs.
Source: Bloomberg, “GE Sees Fourfold Rise in Sales From Industrial Internet”, October 2014; NY Times, “G.E. Opens Its Big Data Platform”, October
2014; 360i.com, “The CMO’s Guide to Big Data”, November 2012; Fortune.com, “Amazon’s recommendation secret”, July 2012

14
Do you have the right operating model for your Big Data initiatives?
For each question, select the degree of applicability that is most appropriate for your organization. Mark your answer on a scale of 1
to 5, where 1 indicates the lowest degree and 5 indicates the highest.
How effective is your governance model?
Do you have a Big Data governing body that takes decisions on funding, policy formulation, selection of tools and other issues?
1 2 3 4 5
We do not have any such
governing body
We have a dedicated Big
Data governing body for
all decision making around
Big Data and Analytics
What is the extent of interaction between your business and IT teams?
1 2 3 4 5
Both teams operate
separately, with business
determining the use-cases
and requirements, and IT
implementing them
We have joint project
teams for Big Data and
Analytics initiatives, where
members from business
and IT work together as
one team
Do you have well-defined criteria to evaluate use-cases for selection?
1 2 3 4 5
No, we have not
established any evaluation
criteria
We have clearly defined,
quantitative evaluation
criteria to identify, qualify
and select use-cases
Do you have well-defined and quantitative criteria to measure the success of your Big Data initiatives?
1 2 3 4 5
No, we have not
established any success
criteria
We have clearly defined,
quantitative criteria in the
form of Key Performance
Indicators (KPIs) for
measuring success
How well do you manage your data?
Have you defined policies and procedures to ensure high data quality?
1 2 3 4 5
There are no defined
policies/processes in
place for managing data
quality
There are robust policies/
processes across various
stages (capture, curation,
storage, transfer and use)
that ensure only quality
data is used

15
How well-integrated are your datasets?
1 2 3 4 5
Isolated (data is scattered
across departmental silos,
nobody has a consistent
view on our portfolio of
data assets)
Completely integrated
(data across the entire
organization is integrated,
we are able to get a
360-degree view of our
data assets)
How robust is your security and privacy?
Do you follow any standard guidelines for data privacy and security?
1 2 3 4 5
We do not follow any such
guidelines
We follow clear,
comprehensive and well-
defined guidelines, that
address all data privacy
and security aspects
How important is security as a factor in the design and implementation of your Big Data initiatives?
1 2 3 4 5
It is not an important
factor, we just focus on
launching our initiatives
with the required
functionality
It is a critical aspect. We
have a strict risk clearance
process, and do not
launch our initiatives until
all security loopholes have
been plugged
Which tools and technology do you use?
Have you invested in specific tools for Big Data and Analytics?
1 2 3 4 5
We have not invested in
Big Data and Analytics
tools, we continue to work
with basic tools
We have invested in a full
portfolio of advanced and
integrated Big Data and
Analytics tools
How do you sharpen your analytics competencies? (please select all that apply, the score for this question is equal to the number
of choices selected)
What is your strategy for developing analytics skill sets in your organization?
We conduct training to
develop the required skills
in-house
We hire skilled resources
from the market
We partner with other
organizations to leverage
their skill sets
We acquire other
organizations to absorb
their skill sets
We partner with academic
institutions for skill
development, internships,
campus recruitment etc
Overall Score =
9 - 22 – Undeveloped: Your organization is lagging behind on Big Data and Analytics, with improvement required across all areas.
23 - 36 – Developing: Your organization is developing its Big Data and Analytics competencies, but can improve in certain areas.
37 - 50 – Developed: Your organization has a well-developed Big Data and Analytics competency, with a high maturity across all areas.

16
Survey Methodology
About the Big Data Survey
Capgemini Consulting conducted a global survey of senior Big Data executives in November 2014. The survey
covered 226 respondents across Europe, North America and APAC, and spanned multiple industries including retail,
manufacturing, ﬁnancial services, energy and utilities, and pharmaceuticals. The survey targeted senior executives across
the Analytics, Business and IT functions, who are responsible for overseeing Big Data initiatives in their organization.
Respondents were asked questions around their organization’s approach to Big Data governance, data management,
skill development, and technology infrastructure.
The results from this exercise, supplemented by in-depth interviews with industry executives, serve as the basis for the
ﬁndings and recommendations in this report.
Survey Demographics
Worldwide Distribution of Respondents
Europe
North America
APAC
50%
39%
11%
Function-wise Distribution of Respondents
Analytics
Business
IT
38%
36%
26%

17
1 ABI Research, “Unlocking the Value of Big Data in Enterprises”, September 2013
2 CIOInsight.com, “Data Analytics Allows P&G to Turn on a Dime”, May 2013
3 The Wall Street Journal, “Six Challenges of Big Data”, March 2014
4 Github.IO, Presentation on Nordstrom Data Lab for the Strata Conference in 2013
5 Capgemini Consulting Interview
6 Capgemini Consulting and MIT Center for Digital Business, “Digital transformation: a roadmap for billion-dollar organizations”,
November 2011
7 FinancialInformationSummit.com, “John Bottega, Former CDO, Bank of America”, 2014
8 Techcrunch, “Publisher Schibsted Nabs Twitter Analytics Manager To Be Its Head Of Data Science”, November 2014
10 Nesta, “How leading companies are recruiting and managing their data talent”, July 2014
11 Journal of Organization Design, “Big Data and Organization Design”, 2014
12 ZDNet, “Tesco’s big data arm Dunnhumby buys ad tech firm Sociomantic Labs”, April 2014
13 Datanami, “Walmart Acquires Predictive Analytics Startup, Inkiru”, June 2013
15 ComputerWeekly.com, “Big Data, big legal trouble?”, December 2013
References

Rightshore®
is a trademark belonging to Capgemini
CapgeminiConsultingistheglobalstrategyandtransformation
consulting organization of the Capgemini Group, specializing
in advising and supporting enterprises in significant
transformation,frominnovativestrategytoexecutionandwith
an unstinting focus on results. With the new digital economy
creating significant disruptions and opportunities, our global
team of over 3,600 talented individuals work with leading
companiesandgovernmentstomasterDigitalTransformation,
drawing on our understanding of the digital economy and
our leadership in business transformation and organizational
change.
Find out more at: www.capgemini-consulting.com
Capgemini Consulting is the strategy and transformation consulting brand of Capgemini Group. The information contained in this document is proprietary.
© 2014 Capgemini. All rights reserved.
Jerome Buvat
Head of Digital Transformation
Research Institute
jerome.buvat@capgemini.com
Roopa Nambiar
Manager, Digital Transformation
Research Institute
roopa.nambiar@capgemini.com
Rishi Raj Singh
Senior Consultant,
Digital Transformation Research Institute
rishi.b.singh@capgemini.com
Mathieu Colas
Vice President, Big Data and
Digital Transformation
mathieu.colas@capgemini.com
Ingo Finck
Vice President, Data Science & Analytics
and Performance Management
ingo.ﬁnck@capgemini.com
Authors
For more information contact
Digital Transformation
Research Institute
dtri.in@capgemini.com
The authors would like to thank Tripti Sethi from Capgemini Consulting Global, Laurence Chretien from Capgemini Consulting France,
and Steve Jones from Capgemini Global.
With more than 130,000 people in over 40 countries, Capgemini
is one of the world’s foremost providers of consulting,
technology and outsourcing services. The Group reported 2013
global revenues of EUR 10.1 billion. Together with its clients,
Capgemini creates and delivers business and technology
solutions that fit their needs and drive the results they want. A
deeply multicultural organization, Capgemini has developed its
own way of working, the Collaborative Business ExperienceTM
,
and draws on Rightshore®
, its worldwide delivery model.
Learn more about us at www.capgemini.com
About Capgemini and the
Collaborative Business Experience
Jeffrey T Hunter
jeffrey.hunter@capgemini.com
Steven Mornelli
steven.mornelli@capgemini.com
Jeffrey T Hunter
jeffrey.hunter@capgemini.com
Dr. Jerry Smith
jerry.smith@capgemini.com
Ingo Finck
Germany
Ingo Finck
France
Laurence Chretien
laurence.chretien@capgemini.com
United Kingdom
Nigel Lewis
nigel.b.lewis@capgemini.com
Norway
Erlend Selmer
erlend.selmer@capgemini.com
Netherlands (Benelux)
Liesbeth Bout
liesbeth.bout@capgemini.com
Sweden
Karl Bjurstrom
karl.bjurstrom@capgemini.com
Sandeep Kothari
sandeep.kothari@capgemini.com
Global:
North America:
Europe:
India:

Cracking the Data Conundrum: How Successful Companies Make #BigData Operational

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Cracking the Data Conundrum: How Successful Companies Make #BigData Operational

Similar to Cracking the Data Conundrum: How Successful Companies Make #BigData Operational (20)

More from Capgemini

More from Capgemini (20)

Recently uploaded

Recently uploaded (20)

Cracking the Data Conundrum: How Successful Companies Make #BigData Operational