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Internet of things, Big Data and Analytics 101

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A quick summary on the Internet of Things (IoT), Big Data and Analytics and how that is shaping our world

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Internet of things, Big Data and Analytics 101

  1. 1. Internet of Things, Big Data and Analytics 101 Frost & Sullivan’s Global Digital Media Research Mukul Krishna, Senior Global Director, Digital Media Practice Frost & Sullivan
  2. 2. Universal Theme: Seamless, intelligent and ubiquitous interactivity is a key theme across all verticals Manufacturing: Intelligent interconnectivity across the enterprise for enhanced control, speed and efficiency Retail: Highly personalized customer experience across channels and devices Seamless, Inte lligent and Ubiquitous Interactivity Healthcare: Integrated and smart patient care systems and processes Banking and Finance: Seamless customer experience across all banking channels Automotive: V2V and V2I communication
  3. 3. Universal components of seamless, intelligent and ubiquitous interactivity Back-end and Front-end Integration Analytics Engine Mobile, Wireless, Smart Devices Across verticals, the need for integration or interconnectivity between various systems, databases, and devices, both in the back-end and the front-end, is recognized as requisite for delivering a seamless experience. Analytics to process both internal and external data provide the intelligence to guide or trigger alerts, or automated adjustments to processes, offerings to customers, treatments for patients, or automotive driving controls. BYOD, tablets, and other mobile devices, sensors, smart systems, and robotics, are part of the overall vision and are a source of excitement across verticals. These enable ubiquitous and real-time interactivity, both in the back-end (e.g., among hospital staff), and the front-end (e.g., shopper with the retailer).
  4. 4. Technology Lifecycle Analysis Manufacturing: The sector is the most advanced, relatively, in terms of utilizing intelligent systems to optimize production processes. Predictive maintenance and condition-based monitoring has historically been implemented by most manufacturers with varying degrees of sophistication. IoT in Manufacturing IoT in Automotive IoT in Retail IoT in Healthcare Automotive: The segment made tremendous strides in achieving its long-term vision of truly connected vehicles that are context-aware at all times. The convergence of in-car technologies, wireless communication and mobile devices has provided the concept of IoT with greater traction in this vertical. IoT in Banking and Finance Introduction Banking and Finance: Despite significant progress made in the direction of multi-channel and mobile banking, protecting sensitive customer information and deriving actionable business intelligence from the sheer volume of data that banks collect is a restraint for this vertical. Growth Maturity Healthcare: Despite the compelling value proposition that IoT offers in terms of integrating siloed domains of operation like EMR and advanced equipments, persistent concerns around data security breaches (and associated financial liabilities) continue to slow uptake. Source: Frost & Sullivan Retail: Retail has been lagging behind in embracing the idea of IoT. Challenges associated with data security, top management buy-in, OS fragmentation and overall weak macro-economic conditions will negatively impact investments in intelligent systems in the short and medium terms.
  5. 5. Internet of Things: Strategically Positioned To Drive Greater Efficiencies in Process-dominated Markets Process Records IoT Position within the Larger Technology Ecosystem Designs CAD drawings Documents Master Data Asset Life Cycles Schedules & Maintenance Testing & Operations Plant Management Ecosystem • CAE Systems Content Organization / Asset Registry Objects and Relationships Authentication, Access, & User Policies Collaboration Platform Interoperability and Integration Compliance Assurances • Enterprise Content Management • Collaboration Platform • Enterprise Resource Planning • Project Management • Supply Chain Management • Inventory Management • HR, Accounting and Marketing management
  6. 6. The Four Pillars for an Effective Big Data Strategy Content Discovery and Management Digital intelligence and Analytics Storage User Experience Just these segments account for more than $10 billion in served, addressable markets.
  7. 7. Building a Connected and Smart Ecosystem: A Roadmap to Business Nirvana The Internet of Things connects all manner of endpoints, unraveling a treasure trove of data IoT Ubiqitous networks and device proliferation enable access to a massive and growing amount of traditionally siloed information Big Data Analytics and business intelligence tools empower decision makers as never before by extracting and presenting meaningful information in realtime, helping us be more predictive than reactive Analytics
  8. 8. Motivation for Specialized Big Data Systems • Cost of data storage is dropping, but rate of data capture is soaring • Sources: online/digital, communications, messaging, usage, transactions… • Furthermore, need for real-time data-driven insights is also more urgent • Traditional data warehouses and RDBMS systems cannot keep up • They are unable to capture, manage and optimize the volume and diversity of data marketers are seeking to harness today • Structured, unstructured, and semi-structured data are all essential ingredients in today‟s marketing mix; traditional systems cannot handle this • Big Data systems: cluster-based, commodity priced, distributed computing database management system • Most often based on Hadoop, but usable without MapReduce programming skills • Key features: linear scalability, parallel computing, node redundancy, and centralized access to data • Server clusters behave like a massive single mainframe: What traditional databases do in months, a Big Data management system can do in hours
  9. 9. Data Alone Has No Direct Utility • Data on its own is just bits and bytes of zeros and ones • Understanding correlations and making predictions is key • Understand the consumer decision process and leverage that in real-time to find and monetize opportunities • Analytics makes data come to life and unlocks its potential • Helps marketers overcome the complexity of their data and find winning opportunities • It‟s the “secret sauce” that, done well, makes marketing a hero and wins you a seat at the revenue table
  10. 10. Customer-Centric Analytics are a Business Imperative • The challenge in providing better service to connected customers is to “know” them better. • The majority of retailers are making customer service strategies their primary strategic focus. • Economist Intelligence Unit (EIU) survey shows analytics skill relevance is growing rapidly: • 37% of executives reported "using data analysis to extract predictive findings from „Big Data'“ was the marketing skill that mattered most (up >2X from 17% five years ago) • 85% of respondents agreed Big Data can help businesses make "more informed," data-driven decisions
  11. 11. Analytics is Transforming Marketing Automation • Marketing automation solutions optimize the execution of three key tasks: lead capture and retention, lead scoring, and follow-up. • Big Data adds tools such as clickstream web data to the arsenal • Analytics can then enhance marketing automation functions • Lead scoring is an art, not a science. Analytics + Big Data = • Generate and fully leverage detailed understanding of consumer behavior • Leverage historical data and benchmarks to score more effectively • Account for patterns in visitor‟s online behavior – now and earlier, at your site and others • Follow up also becomes more powerful • Successfully (and quickly!) predict which follow-up actions generate the greatest return for each situation • Optimize marketing spend by focusing it more effectively on a micro-segment basis • There is vast potential for social media engagement combined with analytics to transform customer relationships.
  12. 12. Challenges In Achieving Utopia • Big Data is daunting • Clickstreams, weblogs, social media, smart phone analytics, call transcriptions and medical records yield complex data sets that are difficult to capture, manage and process • Unstructured data, non-normalized data, need to use data across various silos, errors in data, incomplete data – all further complicate the scenario • Analyzing data is easier said than done • Nearly half of marketing executives consider limited competency in data analysis a major obstacle to implementing more effective strategies, and less than half of organizations that evaluate marketing analytics tools actually use them • That said, Big Data is also the next frontier for innovation, competitive advantage and productivity • “Analysis Paralysis” is a real risk • Data is over-analyzed without being able to take meaningful decisions or actions • Unless you can quickly draw accurate conclusions, analytics serves no purpose • More on that in the next slide
  13. 13. Conquering Analysis Paralysis • Come to terms with the data • Leverage the cloud and Big Data technologies • Break up data into manageable sets, and don‟t feel like you have to use all of it at one time – or ever • Be tolerant of imperfect data • Seek to leverage real-time streams as much as archives • Focus on gathering specific actionable insight • • • • Start with simple questions, and refine them over time Seek correlation, not cause Pay as much attention to exceptions and outliers as you do to trends Embrace convergence of data intelligence tools with marketing automation systems • Automation is key, but humans are irreplaceable • Automation is a productivity tool, not a replacement, for humans • Automation tools are only effective if leveraged intelligently – by humans
  14. 14. Bottom Line • Promise of Big Data analytics is real • Implement behavioral targeting to increase customer loyalty and grow sales • More effectively nurture prospects into warm leads, and warm leads into customers • Make a bigger impact by discovering unknown unknowns • Need balance between Big Data capabilities and analytics • Too much data, too little analytics – you‟ll drown in information and lose customers • Too little data, too much analytics – you‟ll draw misleading conclusions • Balance = ability to react quickly and accurately to raise revenue and profits • It may be daunting to tackle the ocean of Big Data – but knowledge workers have only two options: sink or swim
  15. 15. Frost & Sullivan’s 360º Research Perspective Integration of 7 Research Methodologies Provides Visionary Perspective 15
  16. 16. Global Perspective 40+ Offices Monitoring for Opportunities and Challenges 16
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