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1 Global Headquarters: 5 Speen Street Framingham, MA USA P F Creating Big O p portunities with Big Data and Analytic s i n Consu m e r P r oducts W H I T E P A P E R Sponsored by: TIBCO Kimberly Knickle July 2013 Dan Vesset I D C M A N U F A C T U R I N G I N S I G H T S O P I N I O N "A shortage of data" is not a business challenge you'll hear about from consumer products (CP) manufacturers, but converting data to information and empowering data-driven decision making most certainly is a challenge. Data volumes and data sources continue to grow, from social media and ecommerce Web sites to instrumented supply chains and manufacturing to more point-of-sale (POS) data. CP manufacturers are trying to increase the sophistication and the speed of their information analysis for use cases such as increasing the power of their brands, enhancing demand forecasts, optimizing trade promotions and marketing campaigns, and raising performance at a store or product level. Investments in big data and analytics will be key to their success. Our research finds that: According to IDC's 2012 Global Technology and Industry Research Organization IT Survey, CP manufacturers' top 3 drivers for using big data technologies are analyzing operations-related data (manufacturing or supply chain data), analyzing transactional/pos data, and analyzing online consumer data (clickstream and social networking data). The most significant big data challenges for most manufacturers are deciding what data is relevant to their business needs, what data to keep/store, and what data to discard. Challenges related to business intelligence and analytics are most commonly about the complexities of integration. To maximize results from new IT investments in business analytics and big data, CP manufacturers will need to evaluate their readiness in terms of people, process, and technology to ensure the availability of appropriately skilled staff, reengineer business processes for specific use cases, and apply big data and analytics technologies. July 2013, IDC Manufacturing Insights #MI242299

2 I N T H I S W H I T E P A P E R In this white paper, IDC Manufacturing Insights examines how IT investments in big data and analytics can allow CP manufacturers to address their business challenges and create new opportunities more quickly at lower costs. Our definition of CP manufacturers includes food and beverage, footwear and apparel, health and beauty, household care, housewares, sporting goods, and toys. S I T U A T I O N O V E R V I E W M a n a g i n g D a t a t o D r i v e B u s i n e s s P e r f o r m a n c e CP manufacturers face challenges from many directions the increasing pace of business built on top of complex supply chains, the expectations of their customers and consumers, and their sensitivity to value as defined by input costs, consumers, and the power of their brands. For example, CP manufacturers need a means of responding more quickly to demand signals without straining the organization or adding costs. By better understanding the consumer, they can not only improve forecast accuracy and "manage" demand volatility but also manage costs by changing production schedules and lowering inventory in the supply chain. And they can also identify price sensitivities in the market that are essential to profitability and achieving revenue targets. We already see manufacturers investing in business intelligence and analytics to improve the performance of their businesses for a number of drivers (see Table 1). Although traditional business intelligence tools may improve CP manufacturers' ability to report on and analyze the state of their businesses, we also see opportunities for new tools that help CP manufacturers create actionable insights even more quickly, especially as they add new data sources. Page 2 #MI IDC Manufacturing Insights

3 T A B L E 1 T o p D r i v e r s f o r I m p l e m e n t i n g B u s i n e s s I n t e l l i g e n c e / A n a l y t i c s S o l u t i o n s Q. What were the top 3 drivers for your organization to implement business intelligence/analytics solutions? Driver % of Respondents Product or service innovation 31.8 Cost control or reduction 34.1 Risk management 34.1 New customer acquisition 36.4 Optimization of operations 36.4 Customer retention and service 38.6 n = 44 Source: IDC's Global Technology and Industry Research Organization IT Survey, 2012 Opportunities for speeding insights from a broader set of data include: Improving fulfillment execution: Analyzing carrier data or other information sources for inventory (in transit and in stock) and associating this information with customer orders and consumer demand to know if/when deliveries will impact sales and customer orders Raising performance at a store or product level: Analyzing sales data to better define SKU mix, inform category management, optimize pricing, and decrease out of stocks by understanding consumer buying patterns at a more granular level Enhancing demand forecasts: Using the analysis of broader and deeper data to develop more accurate forecasts in a timely manner, and making use of consumer sentiment and point-of-sale data Optimizing trade promotions and modeling marketing campaigns: Gaining improved insight into trade promotion performance to inform future promotions and campaigns, and more generally increasing spend effectiveness Increasing the power of the brand and/or product loyalty: Strengthening brand relationships and engagement with the consumer, and focusing on the consumer experience through segmentation and personalization 2013 IDC Manufacturing Insights #MI Page 3

4 Increasing insight from social engagement: Conducting socialytics or sentiment analysis based on behavior in ecommerce Web sites or comments on social media forums (blogs, Facebook, Twitter, etc.) Making these use cases a reality is driving investment in big data tools and technologies. F U T U R E O U T L O O K B i g D a t a a n d A n a l y t i c s : N e w T e c h n o l o g i e s f o r a N e w A p p r o a c h Despite the name, big data tools and technologies are for much more than just large volumes of data. They enable manufacturers to analyze data from a variety of sources (e.g., ecommerce sites, social networks, and point-of-sale data) and data types (structured and unstructured) and deliver analysis at a greater velocity, in some cases from weeks to minutes and from days to seconds. This technology can provide insights into the past, the present, and the future: knowing what has happened, what is happening, and what might happen. This technology can enable CP manufacturers to make decisions faster or to drive analyses that were too complex to do affordably in the past, thus changing the way they serve their customers and consumers. IDC defines big data and analytics software as: Data organization and management software to extract, cleanse, normalize, tag, and integrate data Analytics and discovery software for ad hoc discovery, and deep analytics and software that supports real-time analysis and automated, rules-based transactional decision making Decision support applications with the functionality to support collaboration, scenario evaluation, risk management, and decision capture and retention B i g D a t a a n d A n a l y t i c s I n v e s t m e n t in C P M a n u f a c t u r i n g We expect companies to spend heavily on big data technologies. In fact, IDC forecasts the big data technology and services market will grow to $16.9 billion in 2015 across the technology stack including infrastructure, software, and services. Spending those funds wisely is going to be very important to manufacturers, starting with identifying the right drivers for those investments (see Table 2). Analyzing operations-related data is at the top of the list. We can also see this in Page 4 #MI IDC Manufacturing Insights

5 the results of an earlier survey (IDC Manufacturing Insights' 2012 Supply Chain Survey), where we found that 62.2% of manufacturing supply chain executives consider big data tools to be important or very important to their supply chain. T A B L E 2 T o p D r i v e r s f o r U s i n g B i g D a t a T e c h n o l o g i e s a n d A p p r o a c h e s Q. What are your organization's drivers for using big data technologies and approaches? Driver % of Respondents Analysis of operations-related data 37.2 Analysis of transactional data from sales systems 31.0 Analysis of online customer behavior related data (clickstream analysis, Web logs, and social networking data) 28.7 Analysis of machine or device data 20.2 Service innovation 15.5 Non-analytic workload (to run OLTP systems or Web sites or applications) 9.3 n = 129 Note: Multiple responses were allowed. Source: IDC's Global Technology and Industry Research Organization IT Survey, 2012 T I B C O S p o t f i r e TIBCO Spotfire is an enterprise analytics platform that can help companies identify new opportunities and detect emerging trends quickly with analytic applications, interactive dashboards, data visualizations, and predictive analytics so executives, managers, and analysts can make better decisions faster. TIBCO Spotfire can help CP manufacturers better serve consumers, lower costs through process efficiencies, and increase profitability by uncovering brand and market trends and opportunities. The software gives users the ability to visually explore data and various business scenarios across data types and sources. TIBCO also supports the necessary integrations to ensure insight is developed from the right data. Manufacturers are using TIBCO Spotfire for applications that include brand performance by geography, social media analytics, price elasticity, and modeling spend based on shopper behavior and demographics IDC Manufacturing Insights #MI Page 5

6 C O N C L U S I O N S C h a l l e n g e s a n d C o n s i d e r a t i o n s Implementing any new IT investments requires advance planning to minimize challenges. For investments related to big data and analytics, challenges include: Creating a well-defined business case where the best way to address skepticism in big data is to identify and track measures of success, such as revenue uptake, decreased out of stocks, or positive brand mentions by customers in social media outlets Ensuring data quality and data governance are foundational requirements (master data management systems and various forms of the data management organization may be necessary) Integrating across data sources and factoring in how quickly you can react to the analysis to decide where and when you need realtime integration Obtaining the necessary analytical skills to not only use the technology but also ask the right questions and interpret the results We expect these types of projects to potentially include a number of partners and to cover the range of software, hardware, and service requirements as appropriate. Coordinating and collaborating among IT suppliers and the internal IT organization will be essential. Finding IT suppliers that have proven track records in the CP industry will be important for achieving results more quickly. For TIBCO specifically, the challenge is to ensure its offerings continue to develop as the technology advances. Although TIBCO is a well-established IT supplier with a strong track record serving IT and business users, TIBCO also faces competition from many other vendors serving CP manufacturers. R e c o m m e n d a t i o n s Today, CP manufacturers have the opportunity to unlock the value of information assets and new data sources. But they will need to evaluate their readiness in terms of people, process, and technology to ensure the availability of appropriately skilled staff, reengineer business processes for specific use cases, and apply big data and analytics technologies. IDC's Big Data and Analytics Maturity Model presents a framework of stages, critical measures, outcomes, and actions required for organizations to effectively advance toward datadriven decision making as they invest in big data and analytics. Figure 1 presents a high level view of IDC's Big Data Maturity Model. Page 6 #MI IDC Manufacturing Insights

7 F I G U R E 1 I D C 's B i g D a t a M a t u r i t y M o d e l Source: IDC, 2013 Recommendations on how to progress include: Now: Assess the business and IT big data and analytics "as is" situation. Identify opportunities to use existing data, technology, and analytics in new ways. Recognize the value of untapped data assets in supporting fact-based decisions. Recognize the implications of operating without critical information and build use cases to address the challenges. Experiment with proof-of-concept and prototype projects. In the next one to two years (the next budget cycle): Use early quantifiable wins to demonstrate potential and justify budget allocations. Evaluate existing technology and its shortcomings. Assess skill gaps and plan to hire and/or externally source professional services. Identify business sponsors and champions that will support and promote projects. Expand projects, incorporate new data sources, and begin to define architectural standards IDC Manufacturing Insights #MI Page 7

8 In the next three to five years: Ensure new and existing projects are supported with appropriate technology, staff, data, processes, and funding. Engage in business process reengineering in response to new insights from big data investments. Assess progress and adjust internal investment priorities to match evolving requirements. Maintain a closed-loop learning environment based on data-driven decision making and expert judgment. C o p y r i g h t N o t i c e Copyright 2013 IDC Manufacturing Insights. Reproduction without written permission is completely forbidden. External Publication of IDC Manufacturing Insights Information and Data: Any IDC Manufacturing Insights information that is to be used in advertising, press releases, or promotional materials requires prior written approval from the appropriate IDC Manufacturing Insights Vice President. A draft of the proposed document should accompany any such request. IDC Manufacturing Insights reserves the right to deny approval of external usage for any reason. Page 8 #MI IDC Manufacturing Insights

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