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

Universal Theme: Seamless, intelligent and ubiquitous interactivity is a key theme across all verticals

Seamless, Intelligent and

Ubiquitous Interactivity

Healthcare: Integrated and smart patient care systems and processes

Retail: Highly personalized customer experience across channels and devices

Banking and Finance: Seamless customer experience across all banking channels

Automotive: V2V and V2I communication

Manufacturing: Intelligent interconnectivity across the enterprise for enhanced control, speed and efficiency

Universal components of seamless, intelligent and ubiquitous interactivity

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.

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).

Back-end and Front-end Integration

Analytics EngineAnalytics 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.

Mobile, Wireless,Smart Devices

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.

IoT in Banking and Finance

IoT in Automotive

IoT in HealthcareIoT in Retail

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

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.

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.

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.

Introduction Growth Maturity Source: Frost & Sullivan

Technology Lifecycle Analysis

Internet of Things: Strategically Positioned To Drive Greater Efficiencies in Process-dominated Markets

Plant ManagementEcosystem

Designs

CAD drawings

Documents

Master Data

Asset Life Cycles

Schedules & Maintenance

Testing & Operations

Content Organization / Asset Registry

Collaboration Platform

Authentication, Access, & User Policies

Interoperabilityand Integration

Compliance Assurances

ProcessRecords

• CAE Systems• Enterprise

Content Management

• Collaboration Platform

• Enterprise Resource Planning

• Project Management

• Supply Chain Management

• Inventory Management

• HR, Accounting and Marketing management

IoT Position within the Larger Technology Ecosystem

Objects and Relationships

The Four Pillars for an Effective Big Data Strategy

Storage

User Experience

Digital intelligence and Analytics

Content Discovery and Management

Just these segments account for more than $10 billion in served, addressable markets.

The Internet of Things connects all manner of end-points, unraveling a treasure trove of data

Ubiqitous networks and device proliferation enable access to a massive and growing amount of traditionally siloed information

Analytics and business intelligence tools empower decision makers as never before by extracting and presenting meaningful information in real-time, helping us be more predictive than reactive

Building a Connected and Smart Ecosystem: A Roadmap to Business Nirvana

IoT Big Data Analytics

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

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

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

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.

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

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

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

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