Accenture Building Analytics Driven Organization

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Building anAnalytics-DrivenOrganizationOrganizing, Governing, Sourcing andGrowing Analytics Capabilities in CPG


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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.

2

Figure 1: CPG Firms Contend with Several Analytics Challenges

Source: Accenture 2013 Research Study *Ranked within top five choices


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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.

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.

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.

3. Sourcing and Deploying

Analytics Talent.

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.

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 2: Accenture Analytics Operating Model Components

“Data to Information” “Information to Insights” “Insights to Outcomes”

I n f o r m a t i o n &

D a t a M

g m t .

T h e r u l e s a n d p r o c e s s e s t o i d e n t i f y a n

d p r i o r i t i z e t h e

s p e c i f i c d a t a e l e m e n t s f r o m i

n t e r n a l a n d e x t e r n a l s o u r c e s

t o b e e x t r a c t e d ,

i n t e g r a t e d , p r o c e s s e d a n d m a n a g e d

Sponsorship & GovernanceThe process to obtain executive sponsorship, financial support

and senior leadership commitment to the Analytics vision

Organization Structure & Talent Mgmt.The people, their skills and the organizational structure neededto support Analytics transformation

Data to InsightsThe roles and processes required to analyze data

and uncover insights

Capability DevelopmentThe industrialization of individual Analytics capabilities to

move up the Analytics maturity scale

I n s i g h t - D r i v e n D e c i s

i o n s

P r o c e s s t o d e l i v e r i n s i g h t s f o r c o n s u m p t i o n b y

t h e b u s i n e s s t o m a k e s m a r t e r d e c i s i o n s

O u t c o m e M e a s u r e m

e n t

T h e p r o c e s s e s t o a s s e s s t h e v a l u e o f A n a l y t i c s i n s i g h t s a s

w e l l a s t r a c k t h e b e n e f i t s r e a l i z

e d o v e r t i m e


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Figure 3: The Analytics Journey to ROI

“Taking an issue-to-outcome approach iscritical because it puts the focus whereit should be: on tying Analytics directly

to making decisions, taking action anddelivering value for improved businessperformance.”


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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.

Insight-driven Decisions

Companies will need to reengineer decision-

making 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 hardlook at their ability and willingness to re-

engineer 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

Section I:Infusing Analyticsinto the Decision-making Process

Many CPG companies have specialized teamsproviding 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.

Instead of bolting Analytics onto current

processes “as needed,” we advocate adapting

cross-functional processes, activities, roles and

responsibilities to infuse Analytics into dailydecision-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

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).

To get started with reengineering decisions,

CPG firms should consider conducting an

Analytics diagnostic to identify what insightis 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 self-

report having good performance management

systems, only 20 percent could point to a

causal link between what they measure andthe outcomes they hope to achieve.4

Value Realization

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 organizationsfrom a data-based mindset to an outcomes-

based 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.

5.1

2.24.1

3.9

1.1

1.2

1.3 1.4

1.6

1.7

1.8

2.1

2.32.4

2.52.6

2.7

3.1

3.2

3.3

3.4

4.2

4.3

4.4

4.5

4.6

4.7

5.2

5.4

1.5

1.9

1.10

4.8

3.6

3.7

3.8

3.73.93.3

3.53.1

3.4

3.8

3.6

1.5

3.2

1.41.7

1.1

4.5

1.1

1.3

1.6

1.2

1.9 1.8

2.4 2.3

2.1

2.6

2.7

2.5

4.24.6

4.4

4.3

4.7

4.8

5.4

5.25.3

High

HighLow

Low

S t r a t e g i c I m p o r t a n c e

Represents analytic capabilities (e.g., assortment optimization)

Capability Value Potential

Figure 4: Prioritizing CPG Analytics Capabilities


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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 andamount 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 newcross-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-analytics-

drive/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.


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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-analytics-

drive/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.

Section II:Organizing andGoverning AnalyticsCapabilities across

the OrganizationTo 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:

• Sponsorship: Who should have

accountability for the direction, funding

and governance of Analytics?

• Leadership: Who is charged with realizing

the vision for Analytics?

• 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?

• 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?

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).

Sponsorship

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

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 organizationshave 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 resourcingmodel. 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 mode

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 chargesand 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.

Governance

Effective governance includes both ownership

of Analytics as well as the ability to manage

demand and supply for Analytics capabilities.

Figure 5: Companies Vary in Implementing Analytics Operating Model

0 5 10 15 20 25 30

Undefined

Partially defined:

not implemented

Fully defined:

partially implemented(e.g., piloting in a region)

Fully defined:

not implemented

Fully defined:

implemented acrosscertain functions only

Fully defined:

implemented across certaingeographies only

Fully implemented 9%

15%

17%

13%

26%

14%

6%

Source: Accenture 2013 Research Study


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Figure 6: Options for Implementing Analytics Organization

Decentralized

Business unit

Analytics group

Analytics Project

corporate

Function

Analytics group

Analytics Project

Functional

Business unit

Analytics Project

corporate

Function

Analytics group

Analytics Project

Consultingcorporate

Analytics group

Centralized

corporate

Analytics group

Center of Excellence

corporate

Federatedcorporate

Resources allocated onlyto projects within theirsilos with no view ofanalytics activities orpriorities outside theirfunction or business unit

Analytics are scatteredacross the organization indifferent functions andbusiness units

Little to no coordination

Resource allocationdriven by a functionalagenda rather than anenterprise agenda

Analysts are located inthe functions where themost analytical activitytakes place, but may alsoprovide services to rest ofthe corporation

Little coordination

Resources allocatedbased on availability on afirst-come first-servedbasis without necessarilyaligning to enterpriseobjectives

Analysts work together ina central group but act asinternal consultants whocharge “clients” (businessunits) for their services

No centralizedcoordination

Stronger ownership andmanagement of resourceallocation and project

prioritization within acentral pool

Analysts reside in centralgroup, where they serve avariety of functions and

business units and workon diverse projects

Coordination by centralanalytic unit

Better alignment ofanalytics initiatives andresource allocation toenterprise prioritieswithout operationalinvolvement

Analysts are allocated tounits throughout theorganization and theiractivities are coordinatedby a central entity

Flexible model with rightbalance of centralizedand distributedcoordination

Same as “Center ofExcellence” model withneed-based operationalinvolvement to provideSME support

A centralized group ofadvanced analysts isstrategically deployed toenterprise-wideinitiatives

Flexible model with rightbalance of centralizedand distributedcoordination

A n a l y t i c s G o v e r n a n c e ( P r o j e c t p i p

e l i n e , r e s o u r c e a l l o c a t i o n a n d b u d g

e t m a n a g e m e n t )

A n a l y s t L o c

a t i o n ( W h e r e a n a l y s t s r e s i d e )

P r o j e c t M a n a g e m e n t S

u p p o r t ( C o o r d i n a t i o n o f a n a l y t i c a c t i v i t y )

Analytics group Business unit Function

Analytics Project Analytics Project

Analytics group Business unit Function

Analytics Project Analytics Project

COE Business unit Function

Analytics Group Analytics Group

Analytics ProjectAnalytics Project

COE Business unit Function

Analytics Group Analytics Group

Analytics ProjectAnalytics Project


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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 organizationis 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 spokelocation 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.

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.

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.


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In CPG, there is clearly an opportunity to usedeeper, more comprehensive Analytics to improveperformance by addressing different issues,including:

• Getting closer to the consumer: Intense competition for consumerloyalty means that CPG companies need the ability to draw deeperconsumer insights from “big data” and make quicker, fact-baseddecisions.

• Optimizing the supply chain: Increasing pressure to reduce costswhile 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 expectingthis level of sophistication from their suppliers.

• Better managing talent: How to hire, manage and deploy the righttalent across the business to meet global marketplace needs.

10


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CPG companies needanalysts that haveadvanced Analytics skillsand familiarity withthe complexity of CPGdistribution networks and

the volume of structuredand unstructured data.

Data ManagementSpecialists

Statistical Modelers/Econometicians

Business Analyst

Visualization Specialist

BI Specialist

Six Sigma

SAS / R Programmers

Decision Scientist

9% 20% 26% 44%

13% 7% 19% 28% 33%

8% 6% 12% 42% 32%

11% 6% 29% 23% 31%

8% 9% 26% 27% 30%

9% 12% 19% 29% 29%

14%

4%

27% 28% 27%

17%

3%

23% 32% 24%

1%

Cannot Resolve/NA

Pressing Constraints (Pressure to Resolve)

Some Constraints but not Pressing

No Constraints (Satisfactory)

Excel in this area

Figure 7: Continued Constraints in Sourcing Analytics Talent

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-sexiest-

job-of-the-21st-century/.

6 Craig, Smith, Mulani and Thomas. Where will you find

your talent? Outlook 2012, No. 3.

Source: Accenture 2013 Research Study

Section III:Sourcing andDeployingAnalytics Talent

It was hardly surprising when the HarvardBusiness 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

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

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).

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 prescriptiveinsights 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.


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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 projectAnother alternative is to secure a dedicated

“capacity” of Analytics talent from a third-

party 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.

Analytics capabilities need toevolve, just as other businessskills have, to remain relevantto the strategic intent of thebusiness, and this includesexecutive-level skills.

Figure 8: Levels of Analytics Talent Needed in CPG7

1%

5-10%

15-20%

70-80%

IT Specialists

Manage the data, software environment and technical infrastructure

Analytics Champions

Lead analytical initiatives

Analytics Scientists

Build analytical models and algorithms

Analytics Experts

Apply analytical models

to business problems

Analytics Users

Put the output of analytical

models to work

Business Specialists

Roles with operational business skills. Will

required training related to interpreting

analytics output and to effective participation

in analytics initiatives.

Analytics Leadership

Roles with business leadership skills. May need

analytics leadership skill development.

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 throughtechnical training and core competency

enhancement.

Source: Counting on Analytics Talent Research

Talent Acquisition and Sourcing

Fortunately, many organizations—companies,

cities and universities—are now galvanized

and worried enough that they are focusing

their energies, individually or in partnership, to

close the Analytics talent gap in a variety of

ways. Universities ranging from MIT to George

Mason are investing in data science degree

programs, some with industry specializationssuch 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


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Talent retention

Talent attraction

Analytics capability

development

Not enough qualified

people

Pool of experience

shallow

Other

66%

2%

15%

27%

42%

53%

Figure 9: A Seller’s Market: Retaining Analytics Talent is Top Challenge

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.

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,

and Iler, B., and Davenport, T., Reverse Engineering Google’s Innovation Machine. April 2008. http://hbr.org/2008/04/reverse-engineering-googles-innovation-machine/ar/1).

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 experimentationto 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 digital-

skills 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 respondent

view Analytics talent retention as their biggest

challenge (Figure 9) and that the difficulty of

attracting and retaining Analytics talent hasbeen 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).


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Figure 10: Journey to an Analytics-driven Organization

Two to three year migration to a fully implemented operating model

“Data to InformatOperating Model Design

CapabilitiesAssessment

ModelFramework

Rollout PlanDesign

Implementation at Scale

SequenceRollout to

Drive Value

Embed Modelinto Daily

Activities

ValueRealization

Initial Rollout & Stakeholder Buy-in

StakeholderBuy-in

Process

Initial

Rolloutwith Select

Functions

Refine

Modelfrom

Feedback

Enterprise

Rollout

Roadmap

• Internal assessment of business needs and

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

• Build engagement and buy-in to the operating

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

• 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

At whatever stage a CPGfirm begins its journey, thekey is to progress toward anAnalytics capability that isgrounded in business valuecreation, knowing that much

of the value will dependupon realigning roles anddecision-making processes.

Summary:Journey To ROIHow 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.

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

toward an Analytics capability that is grounded

in business value creation, and in knowing

that much of the value will depend upon re-

aligning roles and decision-making processes.

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 frominvesting 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.

To implement an issues-to-outcomeapproach to Analytics and achieve desired

business outcomes, CPG companies need an

Analytics operating model that meets three

core requirements:

1. Infusing Analytics into the Decision-

making Process

2. Organizing and Governing Analytics

Capabilities across the Organization

3. Sourcing and Deploying Analytics Talent

Competing on Analytics and building anenterprise 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.


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Global revenue in the past fiscal year

28%19%

18%

16%

19%

$40 Billion or more

$10 Billion to $39.9 Billion




Food & Grocery

General Merchandise

Alcohol & Beverage

Health & Personal Care

Foodservice

Food & Grocery

Apparel

Other

49%

30%

24%

19%

7%

6%

4%

3%

Industry Sector

Title

37%

41%

22%

CEO/COO/CMO or other C-Suite

SVP / VP

Director

Function

33%23%

15%

13%

13%

Operations / Supply Chain

Sales

IT / Technology

Marketing

Finance

Cross function

Other

Analytics Responsibility

62%

38%

Sole responsibility

Partial responsibility

Duration in Analytics Responsibility

12%

45%

43%

6 months-2 years

2-5 years

More than 5 years

1%

2%

Respondent Profile

Respondents were director and above, with a significant portion from the c-suite and with sole responsibility forAnalytics in the organization.

Company Profile

The study included 90 global consumer goods companies with $1B+ in annual sales.

About the ResearchAccenture 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.


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Copyright © 2013 AccentureAll rights reserved.

Accenture, its logo, andHigh Performance Deliveredare trademarks of Accenture.

About Accenture

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’smost 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.

About the Authors

Julio Hernandez

Managing Director, Accenture Analytics,

Products North America Practice Lead

+1 404 307 5363 [email protected]

Bob Berkey

Director, Consumer Goods & Services,

Analytics Lead for North America

+1 917 817 5923

[email protected]

Rahul Bhattacharya

Director, Accenture Analytics, Offshore

Delivery Lead for North America CG&S

and Retail

+91 900 874 4332

[email protected]

About Accenture Analytics

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, businessprocess 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.

Shaping the Future of High

Performance in Consumer Goods

Our Consumer Goods industry professionals

around the world work with companies in

the food, beverages, agribusiness, home and

personal care, consumer health, fashion and

luxury, and tobacco segments. With decades

of experience working with the world’s

most successful companies, we help clientsmanage scale and complexity, transform

global operating models to effectively serve

emerging and mature markets, and drive

growth through evolving market conditions.

We provide services as well as individual

consulting, technology and outsourcing

projects in the areas of Sales and Marketing,

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Integrated Business Services. To read our

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