THE ANALYTICS HUB LEVERAGING A SHARED SERVICES MODEL TO UNLOCK BIG DATA. Thomas Roland Managing Director. David Roggen Director CONTENTS

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1 THE ANALYTICS HUB LEVERAGING A SHARED SERVICES MODEL TO UNLOCK BIG DATA David Roggen Director Thomas Roland Managing Director CONTENTS Shared Services Today 2 What Is an Analytics Hub? 3 Analytics Hub Benefits 3 Operational Flow of 4 an Analytics Hub 4 Steps for Creating 5 an Analytics Hub Conclusion 6

2 Analytics Hub: A shared services center (SSC) with the talent and infrastructure to leverage advanced data analytics to deliver solutions that not only reduce costs but also drive top-line growth. are set by Leveraging data analytics is imperative to the success of your organization. Without actionable data, you will be left behind by the competition. You have gained C-Suite support to build a data-driven organization. Now what? Figure 1: MorganFranklin Analytics Maturity Model To achieve the benefits of data analytics, the next step is to develop an Analytics Hub. Business analytics became one of the top three finance initiatives in CFOs view enhanced capabilities in this space as a way to facilitate and expedite decision making and improve productivity John E. Van Decker, Survey Analysis: CFOs Top Imperatives from the 2013 Gartner FEI CFO Technology Study, Gartner, Inc., May 2, 2013, MORGANFRANKLIN CONSULTING, LLC. VISIT MORGANFRANKLIN.COM TO LEARN MORE ABOUT OUR CAPABILITIES.

3 THE ANALYTICS HUB: LEVERAGING A SHARED SERVICES MODEL TO UNLOCK BIG DATA Shared Services Today The case for becoming a data-driven organization is strong. Companies that do so successfully gain deeper insight into their organizations, customers, suppliers, and competition. The question is less about whether an organization should become data-driven and more about how to optimize analytics capabilities. By developing an Analytics Hub, organizations can maximize investments in analytical capabilities that optimize business performance. Before creating an Analytics Hub, it is vital to spend sufficient time to understand the organization s current analytics capabilities. Tools such as MorganFranklin s Analytics Maturity Model help measure analytics capabilities. As companies progress along the maturity model, they use analytics more strategically and gain insights that create additional value across the enterprise. Over time, these organizations lean upon predictive and prescriptive analytics to make better decisions and become analytics trailblazers. Industry experience and data analytics academic and market research indicate that many companies leverage shared services centers to streamline, standardize, and reduce the cost of operations. The SSC concept is critical to understanding an Analytics Hub, and it is important to first explore the SSC model before examining the development, structure, implementation, and operations of an Analytics Hub. The shared services delivery model is designed to efficiently and effectively provide standardized functional area support to multiple business units across an organization. SSCs increase automation and standardization and deliver greater quality, accuracy, and flexibility all of which reduce costs. SSCs are employed to facilitate routine back-office processes in support of finance and accounting, human resources, marketing, and IT departments. This stands in contrast to a non-shared-services approach, which houses each support service within a separate business unit. SSCs are often run like businesses. They are built around the core elements of people, processes, and technology, and they can be created from existing centralized or decentralized models. What separates SSCs from other cost centers is a strong culture of customer service, adherence to best practice methodologies, and use of service-level agreements (SLAs) to enforce standards. Once traditional SSCs reach maturity and effectively and efficiently serve their customers, they are able to pivot from keeping the lights on to becoming proactive, customer-focused strategic enablers that are viewed as valued assets by the larger organization. To increase value, new services and activities can also be incorporated. As SSCs have evolved, businesses have looked to them to provide analytical expertise, data mining and analytics, business intelligence (BI), and talented professionals with advanced skill sets to fill strategic positions. Figure 2: Shared Services Center Structure 2

4 What Is an Analytics Hub? An Analytics Hub provides additional services and expertise that organizations seek from evolved SSCs. It serves the entire organization and delivers improvements and efficiencies across business units, functional organizations, and even existing shared services centers. Further, an Analytics Hub differentiates itself from a traditional SSC by supporting greater business insights by partnering data analysts with business leaders. In turn, this drives overall business value and competitive advantage. This competitive advantage is the result of the mix of components that make up an Analytics Hub: Leading Practices & Innovation Driven by analytics subject matter experts and data scientists who work with business units to establish an ongoing dialogue to enhance the design of analytics tools, models, and data structure. Decision Support Applications & Processes Developed and standardized to ensure the appropriate level of governance while generating analytic models that enable both predictive and prescriptive capabilities for decision makers. Core Analytics Applications & Processes Foundational applications developed to enable a more advanced level of analytic insight that is shared across the entire enterprise to encourage focus on continuous improvement. Infrastructure Supporting foundation necessary to optimize the performance of an Analytics Hub that must be robust enough to house all data from financial and operational applications as well as data from unstructured sources outside the enterprise (e.g., social media). Given this mix of components and its ability to add value across an entire enterprise, an Analytics Hub propels an organization far beyond descriptive and diagnostic analytics. The outputs and subsequent insights generated by an Analytics Hub illustrate a specialized and strategic knowledge center (versus a transactional and commoditized SSC) that helps answer the questions: What is likely to happen? and What should we do about it? For example, the ability to predict market shifts before they happen is critical for decision makers as they prepare to make prescriptive capital investments betting on the futures of their markets. An Analytics Hub increases the probability of gaining insights that allow key personnel to make the right decisions, which leads to revenue generation and increased shareholder value. Figure 3: Analytics Hub Structure & Components Analytics Hub Benefits Leading Practices & Innovation Decision Support Applications & Processes Core Analytics Applications & Processes Infrastructure An Analytics Hub s fusion of strategic and transactional resources provides the following benefits: A centralized network of analytics subject matter experts. Centralized accountability and design, as well as innovation and contextual understanding. Subject matter experts who research, develop, and implement analytics leading practices based on industry-leading practices, as well as explore and observe internal leading practices (tribal knowledge). Consistent data standards and controls that facilitate a strong control environment and audit readiness. Standardized timing, forms, policies, and naming conventions, which build formality into analytics service delivery for stronger customer service. But how exactly do the components and stakeholders involved in an Analytics Hub interact with one another? MORGANFRANKLIN CONSULTING, LLC. VISIT MORGANFRANKLIN.COM TO LEARN MORE ABOUT OUR CAPABILITIES.

5 THE ANALYTICS HUB: LEVERAGING A SHARED SERVICES MODEL TO UNLOCK BIG DATA Operational Flow of an Analytics Hub An Analytics Hub supports an organization s ability to turn information into insight without redundant and excessive investment in people, processes, and technology. It is the central knowledge base for the infrastructure, applications, processes, subject matter expertise, and data scientists necessary to harness the power of predictive and prescriptive analytics. The foundation of an Analytics Hub is the infrastructure required to stand up and maintain the data warehouse. The warehouse holds all enterprise financial and operational data pulled from business applications, as well as unstructured external data. This data serves as the basis for an Analytics Hub s insights. The analytics application 2 sits atop the data warehouse and provides data scientists and subject matter experts with a user interface that can pull selective source data from the warehouse. A critical success factor for an Analytics Hub is the relationship among business users, subject matter experts, and data scientists. An ongoing dialogue must exist to facilitate understanding of requirements and organizational capabilities. The better the communication and collaboration among stakeholders, the better chance the analytical models will generate the output management needs to inform key decisions. S C E N A R I O : HOW AN ANALYTICS HUB DRIVES REVENUE GROWTH The CEO of a multinational consumer packaged goods company has set ambitious revenue targets for the upcoming year. To meet those expectations, Meghan, a pricing strategist, wants to simulate how changes to three of the products in her division will impact total revenue. Her primary concerns are price elasticity of demand/consumer response, competitor response, and cannibalization of other products in the company portfolio. To run simulations and scenario tests so that she can make data-driven decisions, Meghan turns to her Analytics Hub. Meghan contacts the Analytics Hub and presents her business case to a marketing analytics subject matter expert and a data scientist. Together, the three clarify outstanding questions, determine feasibility, and iterate through the design, development, and completion of the simulation and testing. With the support of subject matter experts and data scientists, Meghan can better forecast the expected revenue impact resulting from price changes to the three products. Moreover, the data scientists have employed prescriptive analytics so the model can provide the optimal product prices based on the assumptions Meghan enters into the model. By teaming up with the Analytics Hub specialists, Meghan is able to optimize her product pricing and better position her company to meet next year s revenue targets. In addition, the Analytics Hub is able to leverage the pricing model to help pricing strategists in other parts of the organization refine prices and take a collective stride toward bolstering top-line growth. Figure 4: Analytics Hub Operational Flow 2 Examples of predictive analytics platforms include IBM SPSS, SAS Rapid Predictive Modeler, Revolution Analytics, KXEN InfiniteInsight, SAP Predictive Analysis, Statsoft Statistica, and Tibco Spotfire. 4

6 Moving from an analytics capability that is descriptive and diagnostic to predictive and prescriptive requires executive commitment, alignment of incentives, and a culture of sharing data across an enterprise. 4 Steps for Creating an Analytics Hub While each organization may be at a different stage in the analytics maturity model, every executive can follow a four-phase approach to build an Analytics Hub: 1 Assess The first step toward building an Analytics Hub is to perform a resource assessment to help determine where the organization ranks within the analytics maturity model (See Figure 1). Where does the organization across all business units and functional areas rank with respect to tools, technology, information and data management, and analytical talent? Baselining the organization s analytics capabilities with executives will help set realistic expectations and goals for analytics growth. 2 Design After assessing the organization s analytics capabilities, work with leadership to create an analytics roadmap. Key stakeholders across the organization must agree on and support expected analytics maturity goals over defined timelines. The analytics roadmap should bridge gaps between the organization s current capabilities and analytics goals by splitting up large, unmanageable tasks into smaller, implementable initiatives to generate momentum, maintain morale, and achieve quick wins. Initiatives within the roadmap should be prioritized by business value, return on investment (ROI), and other agreed upon key metrics that best align with the enterprise strategy. 3 Implement Many of the most advanced users of data analytics technology companies use an agile implementation approach to quickly design, build, test, and deploy products. Doing so enables organizations to gain early learnings, deliver quick wins, and lay the foundation for continuous improvement. Adopting an agile project management approach while focusing on core Analytics Hub initiatives will ensure that the organization builds active feedback loops to advance its analytics capabilities and meet key analytics goals. 4 Monitor Finally, keep a pulse on how the organization s analytics maturity keeps pace with changes in the marketplace. Within the company, solicit regular feedback to improve how the Analytics Hub functions and gauge how subject matter experts, data scientists, and analytical talent can better interact with business users. Given the fast pace of change in the area of analytics, it is critical to keep an eye on technological progress in the market to advance the organization s analytics capabilities and deliver further value to the bottom line MORGANFRANKLIN CONSULTING, LLC. VISIT MORGANFRANKLIN.COM TO LEARN MORE ABOUT OUR CAPABILITIES.

7 T H E A N A LY T I C S H U B: L E V E R A G I N G A S H A RE D S E R V I C E S M O D E L T O U N L O C K B I G D ATA Conclusion The explosion of data creation, capture, and storage has provided tremendous benefits to companies and consumers. While companies have found ways to make this data surge useful, they face increasing pressure to harness data for competitive advantage. Moving from an analytics capability that is descriptive and diagnostic to predictive and prescriptive requires executive commitment, alignment of incentives, and a culture of sharing data across an enterprise. An Analytics Hub addresses an organization s need to find insights that provide distinct advantages over competitors. It not only generates insights that inform decision makers and enable them to enhance value-creation capabilities, but also centralizes the analytics function as a shared service. For those organizations wondering how to become analytics trailblazers or feeling overwhelmed by the amount of data being collected and frustrated by gleaning insights that bolster confidence in decision making, an Analytics Hub is an optimal solution. How can you transform your business into a data-driven organization? Download Profitability in the Age of Analytics: Becoming a Data-Driven Organization morganfranklin.com/data 6

8 About MorganFranklin Consulting MorganFranklin is a strategy and execution-focused business consulting firm and professional advisor. We provide strategic thinking and hands-on support to help clients manage growth and maximize performance. Our solutions always consider the key connections between finance and accounting, operations, technology, and risk connections that are critical to success. Clients say that our responsiveness, flexible style, and fast access to proven professionals make us an invaluable business partner and advisor.

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