CPG Companies: Evolving Your Analytics-driven Organizations
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The document discusses the challenges that consumer packaged goods (CPG) companies face in developing effective analytics capabilities, revealing that while many companies possess localized analytics, few have integrated it as a differentiating factor. It stresses the need for an enterprise-wide analytics strategy that aligns capabilities with decision-making and value delivery, supported by a well-structured operating model. Key requirements for success include infusing analytics into decision-making processes, sourcing talent, and effectively organizing and governing analytics across the organization.
CPG Companies: Evolving Your Analytics-driven Organizations
- 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 [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] Copyright © 2013 Accenture All rights reserved. Accenture, its logo, and High Performance Delivered are trademarks of Accenture. 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 clients manage 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, Supply Chain, ERP Global Operations and Integrated Business Services. To read our proprietary industry research and insights, visit www.accenture.com/ConsumerGoods.