Mind Commerce. Commerce Publishing v3122/ Publisher Sample

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1 Mind Commerce Commerce Publishing v3122/ Publisher Sample Phone: (US) or or (Int'l) Hours: Monday - Thursday: 5:30am - 6:30pm EST Fridays: 5:30am - 5:30pm EST customerservice@marketresearch.com MarketResearch.com

2 Sample/Excerpts ONLY Not Full Report Capturing Big Data in Social and Detection Systems: Market Opportunities and Challenges October 2013

3 Table of Content 1.0 EXECUTIVE SUMMARY INTRODUCTION WHAT IS BIG DATA? THE IMPORTANT V S OF BIG DATA BIG DATA GROWTH DRIVERS DETECTION: SENSORS, PRESENCE, LOCATION, AND MORE SENSORS IN CONSUMER SECTOR SENSORS IN INDUSTRY BIG DATA SYSTEMS, PROCESSES, AND PROCEDURES DATA MANAGEMENT AND BIG DATA MANAGEMENT SYSTEMS BIG DATA FRAMEWORK & TECHNOLOGIES DATA SOURCES FOR BIG DATA BIG DATA ANALYSIS CHALLENGES AND SOLUTIONS MAJOR BIG DATA COMPANIES IBM ABOUT THE COMPANY BIG DATA STRATEGY BIG DATA SOLUTIONS- PRODUCTS AND SERVICES RELATED PRODUCTS OUR ANALYSIS DATAMEER ABOUT THE COMPANY BIG DATA STRATEGY BIG DATA PRODUCTS AND SOLUTIONS OUR ANALYSIS AMAZON WEB SERVICES ABOUT THE COMPANY BIG DATA STRATEGY PRODUCTS AND SOLUTIONS OUR ANALYSIS HP- BIG DATA ABOUT THE COMPANY BIG DATA STRATEGY PRODUCTS AND SOLUTIONS RELATED SOLUTIONS OUR ANALYSIS SPOTFIRE 43 All Rights Reserved Page 2 of 13

4 4.5.1 ABOUT THE COMPANY BIG DATA STRATEGY PRODUCT AND SOLUTIONS RELATED PRODUCTS OUR ANALYSIS INTEL ABOUT THE COMPANY BIG DATA STRATEGY PRODUCTS AND SOLUTIONS RELATED SOLUTIONS OUR ANALYSIS EMC ABOUT THE COMPANY BIG DATA STRATEGY PRODUCTS AND SOLUTIONS RELATED PRODUCTS OUR ANALYSIS BIG DATA SECTORS BIG DATA AND HEALTHCARE KNOWN ISSUE AND CHALLENGES BIG DATA AND TRACKING INDUSTRIES KNOWN ISSUES AND CHALLENGES BIG DATA IN THE PUBLIC SECTOR KNOWN ISSUES BIG DATA IN RETAIL BUSINESS KNOWN ISSUES AND CHALLENGES BIG DATA IN FINANCE KNOWN ISSUES AND CHALLENGES RFID AND SOCIAL INTEGRATION RFID, BIG DATA INTEGRATION& SOCIAL INTEGRATION NFC TECHNOLOGY OVERVIEW CURRENT MARKET DYNAMICS& SOCIAL MEDIA NFC IN MOBILE COMMERCE BIG DATA REPRESENTATION BIG DATA GROWTH BIG DATA STATE BIG DATA TRENDS IN SOCIAL APPS BIG DATA MARKET BIG DATA APPLICATIONS BIG DATA AND RFID 98 All Rights Reserved Page 3 of 13

5 8.3 BIG DATA ECOSYSTEM THE BIG DATA AND RFID ECOSYSTEM BIG DATA - PRIVACY AND PUBLIC POLICY ISSUES BIG DATA AND RFID IN PAYMENT SYSTEMS CURRENT APPLICATIONS BIG DATA AND PAYMENT SYSTEMS MARKET ANALYSIS THE FUTURE OF BIG DATA AND PAYMENT SYSTEMS BIG DATA AND RFID CONSUMER APPLICATIONS BIG DATA AND RFID IN SECURITY SYSTEMS BIG DATA AND RFID IN HEALTHCARE BIG DATA IN RFID AMR APPLICATIONS BIG DATA & RFID TRACKING SYSTEMS RFID TRACKING SYSTEM RFID IMPLEMENTATION DOMAIN AND BIG DATA GROWTH ANALYSIS OF RFID TRACKING SYSTEMS PREDICTING THE MOST SUCCESSFUL RFID APPLICATIONS MOBILE PAYMENTS AND TRACKING SYSTEM KNOWN ISSUES AND CHALLENGES BIG DATA IN RFID CHALLENGES REAL-TIME EVALUATION DATA VALIDATION DATA REPRESENTATION CONCLUSIONS BIG DATA CLOUD APPLICATIONS SAAS AND BIG DATA BIG DATA AND CONNECTED DEVICES BIG DATA APPLICATIONS FUTURE APPLICATIONS THE POTENTIAL FOR GOOGLE IN BIG DATA, RFID, AND M2M BIG DATA ANALYTICS AND FUTURE APPLICATIONS FUTURE APPLICATIONS USING BIG DATA AND SOCIAL MEDIA THE NEW ECOSYSTEM ADVERTISEMENT USER TRACKING BUSINESS MODELS BIG DATA FORECASTS OVERALL GLOBAL BIG DATA REVENUE GLOBAL BIG DATA REVENUE BY FUNCTIONAL AREA All Rights Reserved Page 4 of 13

6 15.3 REGIONAL BIG DATA REVENUE SUMMARY AND RECOMMENDATIONS 153 Figures Figure 1 Structured and Unstructured Big Data 8 Figure 2 Determining Relevancy in Big Data 9 Figure 3 Top Big Data Business Challenges 25 Figure 4 The Rich Data Social Science Future 28 Figure 5 Data Volume of the Future 29 Figure 6 Enterprise Control of Big Data 30 Figure 7 API Growth Figure 8 Big Data Social and Detection Landscape 53 Figure 9 Big Data Growth 90 Figure 10 Overview across Verticals 95 Figure 11 Big Data Ecosystem 100 Figure 12 Big Data in Everyday Life 101 Figure 13 Big Data Domains 108 Figure 14 Big Data & RFID 119 Figure 15 Big Data RFID Lifecycle 120 Figure 16 Bid Data Verticals 127 Figure 17 Big Data Components 144 Figure 18 Big Data Stages 148 Figure 19 Big Data as a Service (BDaaS) 149 Figure 20: Global Big Data Revenue: ($M) 150 Figure 21: Functional Big Data Revenue: ($M) 151 Figure 22: Regional Big Data Revenue: ($M) 151 Figure 23 Functional View of Big Data Relationships 153 Figure 24 SDM Systems and Players 154 Figure 25 Big Data Relationships among Players 155 Tables Table 1 - Big Data Market 110 All Rights Reserved Page 5 of 13

7 1.0 EXECUTIVE SUMMARY Every large corporation collects and maintains a huge amount of data associated with its customers including their preferences, purchases, habits, travels, and other personal information. In addition to the large volume, much of this data is unstructured, making it hard to manage. This so called Big Data is a challenge for industry verticals yet also an opportunity. The enormous growth potential of Big Data caught the attention of various large players such as IBM and Oracle, to name only a couple, who have developed a key strategic focus in this area and various vertical-specific offerings. By now many people across various industry verticals understand the basic issues or at least have heard that Big Data is a big deal. However, most of the focus to date has been on the importance of optimizing data management and analytics. There has been very little analysis about challenges and opportunities for capturing Big Data. This report provides critical evaluation of the front-end of Big Data: capturing data from various sources. This report focuses specifically on obtaining data through sensing/detection with an emphasis on RFID. The report also evaluates social systems such as Facebook as source of data. All Rights Reserved Page 6 of 13

8 2.0 INTRODUCTION 2.1 The Important V s of Big Data It is imperative that organizations and IT leaders focus on the everincreasing volume, variety veracity and velocity of information that forms Big Data. Figure 1 Determining Relevancy in Big Data (Copyright: SAS) Volume: 90% of the world s data has been generated in the past two years alone. Organizations today, are overwhelmed with volumes of data, some of which needs to be organized, secured and analyzed. As the amount of data available to the organization rises, the percent of data that can be processed, evaluated and understood is declining. Planning for this data explosion is fundamental to Big Data management for any organization. Variety: 80% of the world s data is semi-unstructured. Sensors, smart devices and social media are generating this data through Web pages, weblog files, social-media forums, s, documents, sensor systems and All Rights Reserved Page 7 of 13

9 so on. To capitalize on Big Data, organizations must be able to analyze and store variety of data. This kind of data being generated cannot be analyzed by using traditional technologies that enable only structured storage and retrieval. Variety in data represents a fundamental shift in the way analysis needs to be done to support the present decision-making and insight process. Velocity: The continuous flow of data and the speed at which it moves makes it impossible for traditional databases to handle. To gain the competitive edge in the business, organizations need to identify a trend or opportunity in minutes or even seconds before their competitor does. Additionally, this data has a very short shelf-life which forces organizations to analyze this data in near real-time, while the data is in motion, not after it is at rest. Veracity: The reliability and quality of data is the most important frontier to stimulate new insights and ideas. In the past, focus on quality was higher for traditional data warehouses because they serve as systems of record for bookings and revenue data. The same focus needs to be applied to Big Data to increase the signal-to-noise ratio and the ability to make the right decisions. 2.2 Detection: Sensors, Presence, Location, and More What is detection? Within the context of this report, detection is defined as systems solutions and applications whose purpose is to gather data that is of an unstructured nature. In other words, detection systems in this context are those systems and solutions facilitate aggregation of Big Data. Detection systems include sensors, presence and location systems. Sensors exist within consumer sector as well as industry. Presence falls within the realm of telecommunications and general ICT systems. Presence is really not an application by itself but is rather an enabler of applications, adding value to them. We shall define presence information within a telecommunications context including the state of an object or device, status of attachment or engagement, device type, usage or activity, and coarse location information. All of these attributes can be used to distinguish the presence of an entity or object. In telecommunications or computing an object or device can be many things including a PC or laptop, All Rights Reserved Page 8 of 13

10 circuit-switched or IP-based phone, mobile/cellular phone, or other wireless data, voice or signaling device. [ See Full Report for More Information ] Structured and Unstructured Solutions: APIs API or application programming interface acts as an interface, enabler and interconnector between systems by providing a uniform and portable interface (whether it is to the big data or the platform that enables big data). An API can help companies conform large volumes of data into a usable format to control structure and access which APIs can help bring out new uses and possibilities with regard to big data. Some examples include companies like Aetna that collect billions of pieces of medical data and use APIs to share this information with developers in a controlled manner or companies like PadMapper, which relies on big data from Google Maps and Craigslist for their real estate application. An open API is a data system that possesses an interface that lets thirdparty software interact with a company s data and enables those thirdparties to remix that data to potentially build useful new tools on top of or increase its functionality or augment content. Figure 2 Enterprise Control of Big Data All Rights Reserved Page 9 of 13

11 2.3 Business Models For companies, some of the early target applications for Big Data analytics involve analyzing customer churn, facilitating upselling and cross-selling opportunities, and testing the reaction and response to advertising campaigns. However, the biggest challenge is collating the data, which is often easier said than done. Telecoms are like many large enterprises in that they have seen their various systems grow unchecked over the years to a point where they store overlapping and often conflicting information. Implementing any effective BI and analytics solution must always start with a solid data management foundation. Unfortunately, poor and inefficient data management practices in the past have resulted in stove-piped systems that are aligned with core business units or product segments. For example, telecoms have often procured multiple integrated best-ofbreed applications and databases for different lines of business within the organization (such as fixed, mobile, broadband, and cable) that perform similar tasks. Therefore, each business unit maintains its own set of customer, product, and asset data across a range of systems, including: enterprise resource planning and CRM; order and billing; network management; asset and inventory management; provisioning and fulfillment; service delivery; supply chain and planning; and BI and data mart/data warehousing systems. The end result is that most of a telecom's data is stored and managed in separate systems (as shown in Figure below), which has created silos of disparate, and sometimes conflicting, information. All Rights Reserved Page 10 of 13

12 CRM Fullfillment ERP Billing Inventory Planning Figure 3 Big Data Stages These different storage locations and multiple centers are generally inflexible and monolithic, and have no suitable data transfer or interface mechanisms for interaction. While IT departments spend a lot of manual effort to consolidate data from these silos, it is still far from unified. Data fragmentation not only causes problems for many companies in terms of integration complexity, it also results in a splintered view of customer interactions, profiles, identities, and preferences. This makes it difficult for companies to present a standardized view of the customer and results in less consistent services and customer experience across its various operating units. It also limits information and data sharing as customer segmentations cannot be applied across business lines or analysis within one business unit cannot be applied across the group. As data volumes increase, silo type structures will become untenable due to the escalating cost of acquiring and managing data. More importantly, they will also inhibit the use of data as a business asset. We see a variety of new business models emerging over time, and some of these models are difficult to imagine right now, based on existing privacy and security issues. However, with the advent of new identity management systems, and third-party entities that can broker relationships between different companies, we see these challenges ultimately overcome. Furthermore, we believe that there is more value to gain here then the risks in terms of potentially losing customer trust. All Rights Reserved Page 11 of 13

13 We see big Data as a Service (BDaaS) emerging in which trust is the key element fostered by Identity Management, the use of proxies, and very judicious processing of data. Figure 4 Big Data as a Service (BDaaS) We see the opportunity for Big Data as a Service (BDaaS) representing an ecosystem that consists of a variety of different sources of data. Those sources of data include telecom systems, consumer systems, computing systems, storage systems, and real-time systems. All Rights Reserved Page 12 of 13