Accenture surveyed 90 large, global consumer packaged goods companies and found three important dimensions toward building an analytics-driven organization. Read our other analytics research on accenture.com: http://www.accenture.com/CPGanalytics

1. Building an
Analytics-Driven
Organization
Organizing, Governing, Sourcing and
Growing Analytics Capabilities in CPG

2. Consumer Packaged Goods (CPG) companies realize that Analytics is a
required capability to compete effectively in today’s marketplace. Yet,
few have succeeded in capturing the business value they wanted or
expected from their Analytics investments. Our recent research with
CPG executives revealed that while companies have pockets of localized
Analytics capability, fewer than half have ingrained Analytics or believe
it to be a differentiating capability within their organization. What’s
more, companies continue to struggle with fundamental issues related
to Analytics spanning data, methods, organization and technology
(Figure 1). And, Analytics capabilities are deployed frequently to
generate hindsight—“rearview” descriptions of what happened—rather
than forward-looking insights that can be used to make operational,
managerial and strategic decisions.
Figure 2: Title Placeholder
Please rank*CPG Firms Contend with Several Analytics Challenges
Figure 1: order your organizations most significant challenges related to analytics:
69%
Data: Quality
67%
Data
Data: Timeliness for decision making
57%
Data: Integration
Data: Availability
53%
Methods
Methods: Metrics & KPis functionally siloed and do
not provide necessary insight
50%
Methods: Focus is more on gathering/manipulation
than insight generation
49%
Technology
Organization
Methods: Reactive processes and do not help with
root cuase analysis
34%
Organization: Lack the right talent or an appropriate
amount of talent
30%
Organization: Investments in analytics are
not sufficient
28%
Organication: Lack of sponsorship
22%
Technology: Lack an appropriate toolset
Technology: Lack of training or ability to effectively
leverage existing toolset
27%
14%
*Ranked within top five choices
Source: Accenture 2013 Research Study
*Ranked within top five choices
2

3. We believe these challenges can be addressed
if companies take the time to develop an
enterprise-wide Analytics strategy and
underpin it with an operating model designed
to harness the power of Analytics. Taking this
type of issue-to-outcome approach is critical
because it puts the focus where it should
be: on tying Analytics directly to making
decisions, taking action and delivering value
for improved business performance (Figure 2).
To achieve these business outcomes, an
Analytics operating model needs to meet
three core requirements.
1. Infusing Analytics into the
Decision-making Process.
3. Sourcing and Deploying
Analytics Talent.
To embed an “Analytics first” philosophy into
the business, CPG leaders are well served by
starting with the business issue first, then
defining the most relevant data and analysis
and then reengineering decisions to use the
resulting analysis and insights.
Analytics talent is hard to come by, and
analysts who have industry-specific
experience are even harder to find.
CPG companies need to revise talent
management processes to reflect this reality
in the sourcing, development and recognition
of Analytics talent.
2. Organizing and Governing
Analytics Capabilities
across the Organization.
Specifically designing the most appropriate
Analytics organization construct and
allocation of resources based on the maturity
and needs of the business, where Analytics
insight will deliver the most value and the
closest positioning to decision-making.
A critical element of this is the ability to
effectively manage supply and demand for
Analytics services across the business.
While there is no one “right” operating model
that works for every company, there are seven
components that should be addressed to
shape the appropriate operating model:
• Sponsorship & Governance
• Organization Structure & Talent
Management
• Data to Insights
• Capability Development
• Insight-driven Decisions
• Outcome Measurement
• Information & Data Management
Figure 4: Enterprise Analytics Operating Model Components
Figure 2: Accenture Analytics Operating Model Components
“Insights to Outcomes”
Data to Insights
The roles and processes required to analyze data
and uncover insights
Capability Development
The industrialization of individual Analytics capabilities to
move up the Analytics maturity scale
3
Insight-Driven Decisions
Organization Structure & Talent Mgmt.
The people, their skills and the organizational structure needed
to support Analytics transformation
Outcome Measurement
Sponsorship & Governance
The process to obtain executive sponsorship, financial support
and senior leadership commitment to the Analytics vision
The processes to assess the value of Analytics insights as
well as track the benefits realized over time
“Information to Insights”
Process to deliver insights for consumption by
the business to make smarter decisions
Information & Data Mgmt.
The rules and processes to identify and prioritize the
specific data elements from internal and external sources
to be extracted, integrated, processed and managed
“Data to Information”

4. “Taking an issue-to-outcome approach is
critical because it puts the focus where
it should be: on tying Analytics directly
to making decisions, taking action and
delivering value for improved business
performance.”
The Analytics Journey to ROI
Figure 3: The Analytics Journey to ROI
Shrinking market share
Pricing
pressures
Customer defection
Fragmentation and
complexity
Inefficient operations
Aged platforms and
systems
Employee engagement
Fraud & non-compliance
Expanding market share
What?
What?
Gaining insights
from enormous
amounts of diverse
data.
How?
We combine
technical and
business expertise
to unlock the value
from big data using
advanced Analytics
platforms, open
source technologies
and a strong
alliances network.
What?
Driving insights
discovery through
the right
combination of
quantitative
techniques,
analytical talent
and new
technologies.
How?
Our global
network of
Analytics experts
apply a broad
spectrum of
Analytics-based
assets and
market-tested
approaches to find
and create
opportunities for
significant impact.
Generating
actionable insights
to drive improved
customer
acquisition,
retention and
capital
efficiency.
How?
We have a highly
relevant portfolio
of issue-based
industry and
function solutions
to drive insight
driven business
outcomes.
Enhanced cost and
cash advantage
What?
This is the moment
of truth−making
the leap to
adoption as insights
are integrated into
our decisions and
processes.
How?
Our Analytics
experts deploy new
programs, tools and
processes, turning
insights into actions
and driving
outcomes at scale.
Customer loyalty
Speed-to-insights
Operational excellence
Leading edge platforms
and systems
Winning the war
for talent
Reduced risk and fraud
4

5. Section I:
Infusing Analytics
into the Decisionmaking Process
she has the opportunity to leverage Analytics
insights to improve assortment, price and
promotion effectiveness. Changes for improved
performance require fact-based discussion,
decisions and actions across brand marketing,
sales planning, field sales, supply chain and of
course the retailer. To ensure that differentiated
Analytics-driven insight can be acted upon
at speed, end-to-end process assessment and
reengineering is usually needed.
Many CPG companies have specialized teams
providing Analytics services or capabilities.
This approach allows companies to spread
these rare skills across the business so that
they enhance decision-making in existing
business processes. The downside is that it
doesn’t build a sustainable, enterprise-wide
Analytics capability.
Insight-driven Decisions
Companies will need to reengineer decisionmaking in business units and functions to
become more Analytics-driven. It will take
conscious effort, because even though 62
percent of companies we surveyed believe
that Analytics makes for “quicker/more
effective decision-making,” only 25 percent
habitually rely upon Analytics in that process.1
Companies will also have to take a hard
look at their ability and willingness to reengineer processes so that functions such as
marketing, sales and supply chain work more
collaboratively to use Analytics consistently.
CPG companies will also need to coordinate
Instead of bolting Analytics onto current
processes “as needed,” we advocate adapting
cross-functional processes, activities, roles and
responsibilities to infuse Analytics into daily
decision-making. This approach generates a
greater return on Analytics capabilities, as
well as institutionalize their use in everyday
decisions continuously and repeatedly, in
near real time. For example, every time a
category manager speaks to a customer he or
Figure 5: Prioritizing CPG Analytics
Figure 4: Prioritizing CPG Analytics Capabilities
High
4.2
4.2
4.6
4.6
4.4
4.4
3.1
3.1
Strategic Importance
2.4
2.4
3.4
3.4
2.3
2.3
4.3
4.3
2.1
2.1 4.1
2.2
5.4
5.4
1.6
1.6
3.5
1.5
1.5
1.10
1.1
1.7 4.5
1.7 4.5
2.6
2.6
1.1
1.1
1.2
1.2
1.9
1.9
2.5
2.5
4.7
4.7
1.4
1.4
3.6
3.6
3.8
3.8
2.7
2.7
1.8 4.8
1.8 4.8
5.15.2
5.2
5.3
Low
Low
Capability Value Potential
High
Represents analytic capabilities (e.g., assortment optimization)
5
To get started with reengineering decisions,
CPG firms should consider conducting an
Analytics diagnostic to identify what insight
is needed, when and by whom, so that the
insights delivered are relevant, actionable
and timely, and the breadth of insights is
appropriate. Changes that increase the
visibility and value of Analytics are also
needed. These include consistently and
deliberately tying strategies and tactics to
insights generated from Analytics, as well
as prioritizing outcomes-based requests for
Analytics services clearly tied to meeting
important enterprise business goals. This
typically involves prioritizing Analytics
capabilities based on strategic importance
and value potential as shown in Figure 4.
Too often we find that companies launch
Analytics or “big data” efforts without a clear
view of what exactly they want to accomplish,
which results in a solution that is not tied to a
business problem. Indeed, Accenture research
found that even among companies that selfreport having good performance management
systems, only 20 percent could point to a
causal link between what they measure and
the outcomes they hope to achieve.4
Value Realization
3.3 3.9
3.3 3.9
3.7
3.7
3.2
3.2
1.3
1.3
with retailers to ensure that the new insights
are acted upon at the shelf. P&G, for example,
focuses on a data-driven culture and uses
innovative tools like “Business Sphere”
to make sure Analytics informs business
decisions (see sidebar).
Analytics will take root faster if tied to
business outcomes and if there is a clear
business case that quantifies the benefits
of Analytics. Consequently, companies need
to experiment with developing mechanisms
that identify, track and realize the value
of Analytics efforts. The value realization
mechanism will help move organizations
from a data-based mindset to an outcomesbased mindset and minimize needless or
unproductive requests for Analytics support.
Embedding Analytics in decision-making and
tying it to overall business outcomes also
helps break down the organizational barriers
that impede information sharing. If the
expectation is that decisions must be backed
by specific and shared insights, it will be
much harder for “data/information hoarders”
to continue such practices.

6. Case Study: P&G Uses Analytics to Drive a “Cultural Revolution”
P&G’s Business Sphere Analytics-based environment allows the company to harmonize data quickly,
operationalize Analytics and reinforce an Analytics mindset so that the company reacts to insights faster and
“speeds the pace of business.” Business Sphere combines an immersive, data visualization environment with
an integrated dynamic technical architecture and facilitated discussion frameworks. According to former CEO
Bob MacDonald, it is a key component of P&G’s effort to “move business intelligence from the periphery of
operations to the center of how business gets done.”2 Business Sphere has been characterized as “the opposite
of creating standard reports” and as being centered on “creating a standard environment with the right tools
(where)…experts…use whatever data they need to make the right decisions.”3
Case Study: Infusing Predictive Customer Analytics into Decision-making Processes
As one of the world’s largest retailers, Tesco has spent decades extracting insights from customer buying trends
and incorporating those insights into upstream operations. Tesco leveraged its customer loyalty card program
to extract customer purchasing insights and applied them toward the redesign of its internal operational
processes, most notably its supply chain. Through scenario planning, Tesco can deliver exactly the right type and
amount of inventory to the right store at the right time and reduce its risk of stock-outs. For example, Tesco
can accurately predict weather-driven buying behavior at unique stores and can precisely stock them based
on a weekend weather forecast. Tesco’s supply chain Analytics division has approximately 50 employees, all
skilled in engineering and statistics, and who have also been trained in retail processes and other technology
applications, enabling the team to apply advanced Analytics insights in a meaningful operational manner.
As a result, Tesco has captured more than $100M GBP in operational cost savings, in addition to revenue and
margin gains from improving its customer segmentation capabilities. Tesco is a visionary through its innovative
application of deep-dive customer insights to its supply chain, and today can quickly pilot and test new ideas
(thanks to its warehouse of data, technology tools and Analytics experts) and make quick decisions on new
cross-functional strategies.
1 Analytics in Action: Breakthroughs and Barriers on the Journey to ROI. Feb. 2013. http://www.accenture.com/us-en/Pages/insight-analytics-action.aspx.
2 Murphy, C. P&G CEO Shares 3 Steps to Analytic-Driven Business. http://www.informationweek.com/global-cio/interviews/pg-ceo-shares-3-steps-to-analyticsdrive/240148065, Feb. 7, 2013 accessed May 22, 2013.
3 Murphy, C. Why P&G CIO Is Quadrupling Analytics Expertise. http://www.informationweek.com/byte/why-pg-cio-is-quadrupling-Analytics-expe/232601003 Feb. 16, 2012,
accessed April 26, 2013.
4 McCarthy, B., Rich, D., and Harris, J. Getting Serious about Analytics: Better Insights, Better Decisions, Better Outcomes. 2011. http://www.accenture.com/
SiteCollectionDocuments/PDF/Accenture_Getting_Serious_About_Analytics.pdf.
6

7. Section II:
Organizing and
Governing Analytics
Capabilities across
the Organization
• Governance: Where should Analytics
talent “live” in the organization—will
Analytics talent reside in a stand-alone
organization, be embedded within the
business or some of both? How should
demand and supply be managed?
• Sponsorship: Who should have
accountability for the direction, funding
and governance of Analytics?
Our research shows CPG companies are at
various stages of implementing an Analytics
operating model with these components.
On a relative basis, companies outside
North America report the most progress.
Companies span the spectrum from not
having a defined Analytics operating model
to having a model that is fully designed
and implemented, with the largest segment
(26 percent) characterizing their efforts as
partially defined/partially implemented (see
Figure 5).
• Leadership: Who is charged with realizing
the vision for Analytics?
Sponsorship
To extract the most value from Analytics,
and to do so efficiently, cost-effectively
and continuously, companies will need to
address some basic organizational issues.
These include:
• Funding: How should the development
of an Analytics capability be funded?
Put another way, who has to bear the
responsibility to fund Analytics up front
and on an ongoing basis? How does
funding impact the use of Analytics?
As with most transformational programs,
having the right level and type of
sponsorship is critical. The sponsor must be
passionate and articulate enough about the
benefits of Analytics to whip up enthusiasm,
and confident and senior enough to maintain
order and balance supply and demand. An
Figure 5: Companies Vary in Implementing Analytics Operating Model
Fully implemented
9%
Fully defined:
implemented across certain
geographies only
15%
Fully defined:
implemented across
certain functions only
Fully defined:
not implemented
Fully defined:
partially implemented
(e.g., piloting in a region)
17%
13%
26%
enterprise view is also required, as the
sponsor will need to make decisions that
benefit the organization, as opposed to
separate units. A sponsor’s ultimate purpose
is to accelerate adoption and buy-in across
the organization in order to increase value
realization. Consequently, the sponsor also
needs to be a politically astute leader who
can break down cultural barriers to extract
and disseminate data more broadly.
Leadership
Analytics leaders cascade the vision of
what Analytics can do for the business,
encourage people to realize this potential
and hold people accountable for the
results. The leader’s role is to build
alignment and reinforce the Analytics
culture, advancing the Analytics capability
of the organization and improving
decision-making. Several organizations
have a new C-level role—”Chief Analytics
Officer” or “SVP Enterprise Analytics.”
Funding
Funding can come from multiple sources and
often does, generally from functions and in
proportion to the priority the function puts on
Analytics. While not a rule, we’ve seen that
the more advanced the organization is in terms
of Analytics maturity, the more prevalent is
an enterprise-wide funding and resourcing
model. Our point of view is that funding at the
enterprise level would underscore the strategic
value Analytics can generate, while preserving
the option of migrating to a pay-to-play model
for the consumption of Analytics. Initially,
a pay-to-play model enables functional
executives to opt out of integrating Analytics
insight into everyday business decisions and
actions. Having a base-level corporate charge
against functional P&Ls provides incentive
to begin to use the function. As adoption
increases, usage-based allocation of charges
and ultimately resources becomes more
appropriate. There are many different funding
models and the optimal approach is truly
dependent upon each company’s culture and
unique set of circumstances.
Partially defined:
14%
not Action: Breakthroughs and Barriers on the Journey to ROI. Feb. 2013. http://www.accenture.com/us-en/Pages/insight-analytics-action.aspx.
1 Analytics in implemented
2 Murphy, C. P&G CEO Shares 3 Steps to Analytic-Driven Business. http://www.informationweek.com/global-cio/interviews/pg-ceo-shares-3-steps-to-analyticsdrive/240148065, Feb. 7, 2013 accessed May 22, 2013.
Undefined
3 Murphy, C. Why P&G CIO Is Quadrupling6%
Analytics Expertise. http://www.informationweek.com/byte/why-pg-cio-is-quadrupling-Analytics-expe/232601003 Feb. 16, 2012,
Effective governance includes both ownership
accessed April 26, 2013.
of Analytics as well as the ability to manage
4 McCarthy, B., Rich, D., and Harris, J. 5
Getting Serious about Analytics: Better Insights, Better Decisions, Better Outcomes. 2011. http://www.accenture.com/
0
15
20
25
30
Source: Accenture 2013 Research Study 10
SiteCollectionDocuments/PDF/Accenture_Getting_Serious_About_Analytics.pdf.
demand and supply for Analytics capabilities.
Governance
7

8. Figure 6: Options for Implementing Analytics Organization
Figure 6: Analytics Operating Model Options
Decentralized
Resources allocated only
to projects within their
silos with no view of
analytics activities or
priorities outside their
function or business unit
corporate
Business unit
Function
Analytics group
Analytics group
Analytics Project
Analytics are scattered
across the organization in
different functions and
business units
Little to no coordination
Resource allocation
driven by a functional
agenda rather than an
enterprise agenda
Analysts are located in
the functions where the
most analytical activity
takes place, but may also
provide services to rest of
the corporation
Little coordination
Resources allocated
based on availability on a
first-come first-served
basis without necessarily
aligning to enterprise
objectives
Analysts work together in
a central group but act as
internal consultants who
charge “clients” (business
units) for their services
Analytics Project
Function
Analytics Project
Analytics group
Analytics Project
Consulting
corporate
Analytics group
Business unit
Function
Analytics Analytics Project
Analytics Project group
Centralized
corporate
Analytics group
Business unit
Function
Analytics Analytics Project
Analytics Project group
Center of Excellence
corporate
COE
Business unit
Function
Analytics Group
Analytics Group
Analytics Project
Stronger ownership and
management of resource
allocation and project
prioritization within a
central pool
Analysts reside in central
group, where they serve a
variety of functions and
business units and work
on diverse projects
Better alignment of
analytics initiatives and
resource allocation to
enterprise priorities
without operational
involvement
Analysts are allocated to
units throughout the
organization and their
activities are coordinated
by a central entity
Same as “Center of
Excellence” model with
need-based operational
involvement to provide
SME support
A centralized group of
advanced analysts is
strategically deployed to
enterprise-wide
initiatives
Project Management Support (Coordination of analytic activity)
Business unit
Analytics Governance (Project pipeline, resource allocation and budget management)
corporate
Analyst Location (Where analysts reside)
Functional
No centralized
coordination
Coordination by central
analytic unit
Flexible model with right
balance of centralized
and distributed
coordination
Analytics Project
Federated
corporate
COE
Business unit
Function
Analytics Group
Analytics Group
Analytics Project
Analytics Project
8
Flexible model with right
balance of centralized
and distributed
coordination

9. Case Study: Evolving the Analytics Organization at a Large Australian Bank
CPG companies looking to organize a strong Analytics capability can learn from a large national bank’s approach.
Initially the bank introduced Analytics “pods” to provide Analytics service to the business. However, without a
standard reporting structure this led to unnecessary headcount and low or uneven utilization and business knowledge
among analysts. Over time the bank transitioned to an organization that utilized a centralized, offshore Analytics
CoE that would allow it to ramp up and down Analytics services based on the demands and readiness of the business.
The bank’s well-orchestrated Analytics evolution took place in three phases over two-and-a-half years:
• In the year-long phase one, the bank established a centralized Analytics organization to provide basic Analytics, such as
models, commentary, and basic recommendations and insights.
• In the second phase, the new CoE spent six months providing a solid foundation of business and industry knowledge to
Analytics experts so they could generate insights aligned to business strategies and objectives, better identify consumer
or industry trends and engage in forecast optimization.
• The final phase was given over to training the business to effectively use and apply these insights in day-to-day business
decisions and defining new processes that encouraged and rewarded use of Analytics across the enterprise as a whole.
The governance structure defines the distinct
roles and responsibilities that each group or
individual assumes as it relates to Analytics.
For instance, where do Analytics capabilities
“reside” in the organization? Is it better to be
managed centrally or within a function?
Analytics can be organized in several ways, as
shown by the six options in Figure 6. Options
range from wholly decentralized or centralized
groups to functional or Center of Excellence
(CoE) constructs. In choosing an organization
construct, CPG firms should consider how
each facilitates (or inhibits) governance over
multiple Analytics projects and also recognize
that organizations frequently evolve as
business needs change (see sidebar).
How do companies determine which
organizational option is right? There are three
primary considerations: company priorities,
maturity of Analytics capabilities and the
need to balance supply and demand for
Analytics skills. For instance, the Functional
model is often used when the organization
is relatively new to Analytics, doesn’t need
analysts in every area of operations and, in
fact, there are too few analysts to justify
centralizing. A Centralized model is often the
choice when there is a growing demand for
Analytics and a critical mass of analysts exists,
and allocation of these scarce resources is
a priority. An evolving model for Analytics
Competitors is the Federated model (also
referred to as a “hub and spoke” model) which
works well where there is a high demand for
Analytics across the organization, justifying
both a central Analytics “SWAT team” to
address complex cross-functional efforts as
well as resources in different areas of the
business to execute more functionally focused
Analytics. This model also enables greater
efficiencies as both basic and more advanced
Analytics become repetitive in nature (i.e.,
requiring regularly scheduled refreshes)
and can be integrated into a “factory”
environment either within a hub or a spoke
location using more cost-effective resources.
In our experience, CPG companies are
gravitating toward a CoE or Federated model
because of several advantages such as
flexibility to allocate capabilities to maximize
their effectiveness, easier governance
and increased resource engagement. The
Federated model can ensure adequate
coverage of both enterprise activities (data
virtualization or enterprise dashboard design,
for example) as well as function-specific
Analytics with predictive and prescriptive
modeling. Governance is streamlined
because duplication is reduced and KPIs are
established for the central and dispersed
teams. Finally, Analytics resources have the
benefit of both a centralized organizational
unit to provide capability development
opportunities as well as the ability to
specialize in different areas of the business to
deepen institutional knowledge and ties.
9
Another critical aspect of governance is the
ability to define an appropriate process to
manage the supply and demand for Analytics
capabilities. It usually doesn’t take long
before there is an overwhelming “pull” for
Analytics from the organization, making
the process to qualify, solution and service
demand critical.
Demand typically comes from two
sources: (1) Stakeholder initiated, where
points of contact within the business
unit identify and qualify opportunities,
and (2) Proactive identification, whereby
the Analytics organization identifies
opportunities based on diagnostics or
awareness sessions with stakeholders.
Once opportunities have defined business
cases and pass an initial set of screening
criteria, they can be consolidated and
reviewed periodically by a centralized
Analytics organization and/or Steering
Committee. This body prioritizes
opportunities based on a defined set of
criteria (strategic, financial, capacity, etc.)
and determines the appropriate approach
and team to service the opportunity.
Effective management of demand not only
helps to identify, prioritize and service the
highest value opportunities, it helps in
downstream planning for talent acquisition,
capability development and planning for
other investments.

10. In CPG, there is clearly an opportunity to use
deeper, more comprehensive Analytics to improve
performance by addressing different issues,
including:
• Getting closer to the consumer: Intense competition for consumer
loyalty means that CPG companies need the ability to draw deeper
consumer insights from “big data” and make quicker, fact-based
decisions.
• Optimizing the supply chain: Increasing pressure to reduce costs
while simultaneously increasing service levels is also driving a need for
improved decision making throughout the supply chain.
• Strengthening relationships with the retailer: Retailers have direct
access to the shopper, have a wealth of information at their disposal,
continue to mature their Analytics capabilities and are now expecting
this level of sophistication from their suppliers.
• Better managing talent: How to hire, manage and deploy the right
talent across the business to meet global marketplace needs.
10

11. Section III:
Sourcing and
Deploying
Analytics Talent
surveyed are in the market for Analytics
talent, finding that talent remains difficult. A
full 20 percent of respondents had pressing
needs for statistical modelers, econometric
experts and decision scientists that they could
not fill. Additionally, one in four companies
senses some constraints in its ability to fill
roles associated with the delivery of Analytics
insights such as business intelligence or
visualization specialists (Figure 7).
It was hardly surprising when the Harvard
Business Review named “Data Scientist” the
sexiest job of the century5—barely a decade
into the century—underscoring the dearth
of Analytics talent. Research by Accenture’s
Institute of High Performance found that
only one out of 10 qualified university
graduates accepts industry-based Analytics
positions and, out of these, most head
toward investment banking, consulting or
software firms.6
Talent Needs in CPG
Not any Analytics talent will do in CPG.
Companies need analysts that have advanced
Analytics skills and familiarity with the
complexity of CPG distribution networks and
the volume of structured and unstructured
data. While many CPG companies tend
to have talent in the area of descriptive
Analytics, companies need analysts capable
of generating predictive and prescriptive
insights as well. Our experience is that most
formal Analytics organizations require several
analysts of various tenures across roles and
skill levels as shown in Figure 8.
The shortage could crimp many CPG firms’
ability to become an Analytics-driven
company in the near future. Our current
research of CPG leaders showed that while
nearly three-quarters of CPG companies
CPG companies need
analysts that have
advanced Analytics skills
and familiarity with
the complexity of CPG
distribution networks and
the volume of structured
and unstructured data.
5 Davenport, T., and Patil, D. Data Scientist: The Sexiest
Job of the 21st Century. October 2012.
http://hbr.org/2012/10/data-scientist-the-sexiestjob-of-the-21st-century/.
6 Craig, Smith, Mulani and Thomas. Where will you find
your talent? Outlook 2012, No. 3.
Figure 8: Talent Gaps and Hiring Constraints
Please evaluate the following analytic skill areas in terms of your
Figure 7:to build and sustain capabilities without constraints.
ability Continued Constraints in Sourcing Analytics Talent
Data Management
Specialists
Statistical Modelers/
Econometicians
9%
20%
26%
 Cannot Resolve/NA
44%
 Pressing Constraints (Pressure to Resolve)
1%
 Some Constraints but not Pressing
13% 7%
19%
28%
33%
 No Constraints (Satisfactory)
 Excel in this area
Business Analyst 8% 6% 12%
Visualization Specialist
BI Specialist
Six Sigma
SAS / R Programmers
42%
32%
11% 6%
29%
23%
8% 9%
26%
27%
30%
19%
29%
29%
9% 12%
14%
31%
27%
28%
27%
23%
32%
24%
4%
Decision Scientist
17%
3%
Source: Accenture 2013 Research Study
11

12. Analytics capabilities need to Talent Acquisition and Sourcing
evolve, just as other business Fortunately, many organizations—companies,
cities and universities—are now galvanized
skills have, to remain relevant and worried enough that they are focusing
to the strategic intent of the their energies, individually or in partnership, to
close the Analytics talent gap in a variety of
business, and this includes
ways. Universities ranging from MIT to George
Mason are investing in data science degree
executive-level skills.
programs, some with industry specializations
such as health care, to train the data scientists
of tomorrow. Public-private partnerships
to develop or retain data scientists are also
leading to some interesting collaborations.
• New York City is contributing $15
million to Columbia University’s Institute
for Data Sciences and Engineering, a
certificate program to be led by a staff
of 75 professors. NYU is also launching a
graduate program. The vision is to have
New York become a mecca for Analytics,
and not just on Wall Street.
• Companies in Seattle are taking a more
direct, aggressive approach. Microsoft,
Google and Amazon are all supporting
Analytics-related programs at the University
of Washington.8 Of course, the companies
would be among the future employers and
beneficiaries of program graduates as well,
making their investment a win/win.
CPG companies can also look to alternative
arrangements to source the appropriate skills.
A third-party provider could be retained to
address a specific Analytics problem or project.
Another alternative is to secure a dedicated
“capacity” of Analytics talent from a thirdparty provider in an onshore, offshore or hybrid
model for a set period of time. This approach
has several advantages for CPG companies,
including the ability to dynamically reorient
toward value, flexibility of talent and capacity,
and lower cost compared to hiring internally or
through a project.
Capability Development and
Knowledge Management
Analytics capabilities need to evolve, just as
other business skills have, to remain relevant
to the strategic intent of the business, and
7 http://www.accenture.com/us-en/Pages/insight-counting-analytical-talent-summary.aspx.
8 The Numbers of Our Lives: Big data, big money, big skill set required. The New York Times, April 14, 2013.
Education Life section.
Figure 9: Analytic Organization Pyramid
Figure 8: Levels of Analytics Talent Needed in CPG7
1%
5-10%
Analytics Leadership
Roles with business leadership skills. May need
analytics leadership skill development.
Analytics Champions
Lead analytical initiatives
Analytics Scientists
Build analytical models and algorithms
15-20%
70-80%
Analytics Experts
Apply analytical models
to business problems
Analytics Users
Put the output of analytical
models to work
IT Specialists
Manage the data, software environment and technical infrastructure
Source: Counting on Analytics Talent Research
12
Professional Model Builders
Roles for which deep functional and technical
analytics skills are sourced from the market.
May need business-side and analytics
implementation education.
Business Analysts
Roles with business analysis skills. May need to
develop functional analytics skills through
technical training and core competency
enhancement.
Business Specialists
Roles with operational business skills. Will
required training related to interpreting
analytics output and to effective participation
in analytics initiatives.

13. Case Study: How Google Wins the War for Analytics Talent
Google is a visionary in the field of Analytics capabilities and continues to set the bar for the most advanced and
creative approaches to recruiting and cultivating leading-edge Analytics talent. For example, the company follows
a “70-20-10 rule”10—where employees spend 70 percent of their time on their standard role, one day per week on
projects that will develop their technical skills and benefit the company, and half a day per week exploring product
and business innovations and ideas. This sort of on-the-job training (vs. classroom training) is critical for the
engagement and development of employees. Google’s approach not only develops in-house Analytics talent, but it
also allows the company to attract, select and hire only “the best” Analytics talent available.
this includes executive-level skills. There is
an evolving set of managerial “literacies”
essential to competing on Analytics, including
the ability to find, manipulate, manage
and interpret all kinds of data. In addition
to these technical skills, managers at all
levels should be willing and able to apply
the principles of scientific experimentation
to business and have an appreciation for
quantitative methods. Some organizations are
conducting an annual “Analytics Academy” to
build Analytics competence and literacy. For
example, P&G created “a baseline digitalskills inventory that’s tailored to every level of
advancement in the organization.”9
Talent Management
The care and feeding of Analytics talent may
require new approaches beyond the standard
career progression and incentives. For instance,
given their love of data, many analysts don’t
aspire to typical management or organizational
leadership roles, so CPG companies need to
work harder to develop career paths so that
they can retain Analytics talent long enough
to close the skills gap. And when it comes
to Analytics talent it is a seller’s market. Our
survey indicates that two-thirds of respondents
view Analytics talent retention as their biggest
challenge (Figure 9) and that the difficulty of
attracting and retaining Analytics talent has
been felt for several years. Companies might
want to take a page from Google, a clear
innovator in forging ways to keep its talent
happy (see sidebar).
9 Harris, J. Data Is Useless Without the Skills to Analyze It. Sept. 13, 2012. http://blogs.hbr.org/cs/2012/09/data_is_useless_without_the_skills.html Accessed May 23, 2013.
10 See Harris, J., Craig, E., and Egan, H. Counting on Analytical Talent. March 2010. http://www.accenture.com/us-en/Pages/insight-counting-analytical-talent-summary.aspx,
Figure 10: ADavenport, T.,Market: Retaining Analytics Talent 2008. http://hbr.org/2008/04/reverse-engineering-googles-innovation-machine/ar/1).
Seller’s Reverse Engineering Google’s Innovation Machine. April
and Iler, B., and
is Top Challenge
Please indicate which of the following challenges that you face
from talent perspective:
Figurea9: A Seller’s Market: Retaining Analytics Talent is Top Challenge
Talent retention
66%
Talent attraction
53%
Analytics capability
development
42%
Not enough qualified
people
27%
Pool of experience
shallow
Other
15%
2%
13

14. Summary:
Journey To ROI
toward an Analytics capability that is grounded
in business value creation, and in knowing
that much of the value will depend upon realigning roles and decision-making processes.
How long does it take to become an
Analytics-driven CPG company, and does
the journey ever end? There is not a “typical”
Analytics journey that companies make but
there are common guideposts along the way
(Figure 10). Companies may use a major
business transformation or clear pain points
as the burning platform needed to jumpstart
their Analytics journey. Some CPG companies
may already have a high sense of urgency
within top management, or an executive
sponsor who is passionate about the company
moving toward a more fact-based orientation
and culture. Given that there are often
“pockets” of Analytics capability to build
on, the leap is oftentimes from “craft” to an
“industrialized” approach.
CPG companies that use an enterprise-wide
Analytics operating model have been able to
build a data- and insights-driven culture, make
better decisions faster and improve business
outcomes. Companies that have moved from
investing in Analytics to capturing Analytics
ROI have done so by establishing a strong
backbone of Analytics across the organization.
This includes innovating, piloting and
industrializing Analytics solutions with the help
of a network of skilled talent and capabilities;
scaling Analytics capabilities using various
models; and effectively supporting business
initiatives that clearly generate revenue,
optimize costs and mitigate risks.
Figure 10 illustrates a two- to three-year
migration toward becoming an Analytics
Competitor, that begins with the design of
the operating model discussed in this paper.
The exact path and duration of the journey
to Analytics ROI can also vary based on value
creation potential, cultural fit and level of
Analytics maturity. At whatever stage a CPG
firm begins its journey, the key is to progress
To implement an issues-to-outcome
approach to Analytics and achieve desired
business outcomes, CPG companies need an
Analytics operating model that meets three
core requirements:
1. Infusing Analytics into the Decisionmaking Process
2. Organizing and Governing Analytics
Capabilities across the Organization
At whatever stage a CPG
firm begins its journey, the
key is to progress toward an
Analytics capability that is
grounded in business value
creation, knowing that much
of the value will depend
upon realigning roles and
decision-making processes.
Competing on Analytics and building an
enterprise Analytics capability is no easy
task, but within the CPG industry, it is
quickly becoming a necessity. Those who
are moving beyond understanding what
happened and why, and beginning to predict
the outcomes of various decisions and
outcomes are gaining competitive advantage
by reducing costs, increasing speed to
market and driving profitability.
3. Sourcing and Deploying Analytics Talent
Figure 10: Journey to an Analytics-driven Organization
Two to three year migration to a fully implemented operating model
Operating Model
“Data to Informat Design
Capabilities
Assessment
Model
Framework
Rollout Plan
Design
Initial Rollout & Stakeholder Buy-in
Initial
Stakeholder Rollout
Buy-in
with Select
Process
Functions
Refine
Model
from
Feedback
Enterprise
Rollout
Roadmap
• Internal assessment of business needs and
• Build engagement and buy-in to the operating
Analytics capabilities
• Incorporate leading practices from other
industries
• Engage function and business until stakeholders
• Define optimal operating model
• Define prioritized rollout plan to generate early
wins and business “pull” for new model
model
• Test with several functions to prepare for the
enterprise rollout
• Incorporate learnings and feedback to refine the
operating model
• Establish governance structure and processes
• Launch the roadmap and migration path for
enterprise wise implementation
14
Implementation at Scale
Sequence
Rollout to
Drive Value
Embed Model
into Daily
Activities
Value
Realization
• Rollout the operating model across the
enterprise
• Embed the structural changes to drive value
• Build sustainable business processes, talent
management strategy, technology enablement,
governance and data management

15. About the Research
Accenture Research on Governance of Analytics in CPG
Accenture recently completed global research to understand how CPG companies are structuring Analytics-driven
organizations and “infusing” Analytics into their decision-making processes. We surveyed 90 CPG executives with
responsibility for or oversight of Analytics in organizations with revenues of more than $2 billion. The survey
addressed the following topics: (1) Analytics challenges and priorities, (2) Organizing and governing Analytics
capabilities and (3) Insight-driven decision-making.
Company Profile
The study included 90 global consumer goods companies with $1B+ in annual sales.
Global revenue in the past fiscal year
Industry Sector
Food & Grocery
 $40 Billion or more
28%
19%
 $10 Billion to $39.9 Billion
Alcohol & Beverage
 $5 Billion to $9.9 Billion
19%
24%
Health & Personal Care
 $2 Billion to $4.9 Billion
18%
49%
30%
General Merchandise
19%
7%
Foodservice
 $1 Billion to $1.9 Billion
6%
Food & Grocery
16%
Apparel
4%
3%
Other
Respondent Profile
Respondents were director and above, with a significant portion from the c-suite and with sole responsibility for
Analytics in the organization.
Title
Analytics Responsibility
 CEO/COO/CMO or other C-Suite
 Sole responsibility
 SVP / VP
37%
41%
 Partial responsibility
38%
 Director
62%
22%
Function
Duration in Analytics Responsibility
 Operations / Supply Chain
33%
23%
12%
 Sales
 2-5 years
 IT / Technology
1%
15%
13%
13%
 More than 5 years
43%
 Marketing
2%
 6 months-2 years
 Finance
45%
 Cross function
 Other
15

16. About Accenture
About Accenture Analytics
Accenture is a global management
consulting, technology services and
outsourcing company, with approximately
261,000 people serving clients in more
than 120 countries. Combining unparalleled
experience, comprehensive capabilities
across all industries and business functions,
and extensive research on the world’s
most successful companies, Accenture
collaborates with clients to help them
become high-performance businesses and
governments. The company generated net
revenues of US$27.9 billion for the fiscal
year ended Aug. 31, 2012. Its home page is
www.accenture.com.
Accenture Analytics delivers insight-driven
outcomes at scale to help organizations
improve performance. Our extensive
capabilities range from accessing and
reporting on data to advanced mathematical
modeling, forecasting and sophisticated
statistical analysis. We draw on over 12,000
professionals with deep functional, business
process and technical experience to develop
innovative consulting and outsourcing
services for our clients in the health, public
service and private sectors. For more
information about Accenture Analytics, visit
www.accenture.com/analytics.
About the Authors
Julio Hernandez
Managing Director, Accenture Analytics,
Products North America Practice Lead
+1 404 307 5363
julio.j.hernandez@accenture.com
Bob Berkey
Director, Consumer Goods & Services,
Analytics Lead for North America
+1 917 817 5923
robert.e.berkey@accenture.com
Rahul Bhattacharya
Director, Accenture Analytics, Offshore
Delivery Lead for North America CG&S
and Retail
+91 900 874 4332
rahul.r.bhattacharya@accenture.com
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are trademarks of Accenture.
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