The Internet of Things (IoT) is generating a tsunami of data through connected sensors, devices, and systems. By 2020, Gartner predicts there will be over 26 billion connected IoT devices generating zettabytes of data every year. However, data alone provides little value – the real power lies in analyzing and extracting actionable insights from the data. This is where IoT analytics comes into the picture.
What is IoT Analytics?
IoT analytics refers to the processing and analysis of vast amounts of data streaming from networked IoT devices to gain operational insights. It combines specialized analytics techniques, IoT connectivity capabilities, and contextual understanding to convert raw machine data into meaningful conclusions that can drive better decisions.
The overarching goal of IoT analytics is to monitor performance, uncover patterns, predict outcomes, and enable organizations to optimize processes for efficiency, lower costs, enhance products/services, tap into new revenue streams and disrupt old business models.
Key Capabilities
Though approaches differ across IoT analytics solutions, some typical capabilities include:
- Connectivity: Integrating disparate device data into a unified data pipeline
- Pre-processing: Cleansing, standardizing, contextualizing diverse data streams
- Storage: Scalable databases for storing high velocity data
- Analytics: Descriptive, predictive, prescriptive algorithms tailored to IoT data
- Visualization: Interactive dashboards to track KPIs and emerging trends
- Orchestration: Streamlining and automating analytics workflows
- Deployment: Supporting on-premises, cloud, and hybrid installations
Why IoT Analytics is Critical
With more devices getting interconnected every day, IoT analytics unlocks game-changing opportunities across multiple fronts:
1. Optimize Operations
By closely monitoring machine performance and environmental conditions, IoT analytics powers predictive maintenance so issues can be proactively addressed before downtime ensues. This translates into enhanced operational efficiency and lower costs.
2. Improve Agility
Analyzing real-time data against historical patterns empowers organizations to rapidly adapt to changes in demand, supply dynamics etc. This leads to improved organizational agility.
3. Innovate Smarter
Understanding exactly how customers are using products provides an empirical basis for customer-centric innovation. IoT analytics delivers those essential usage insights to drive better product/service innovation.
4. Unlock New Revenue
Detailed insights into customer preferences and behaviors derived through IoT analytics helps to identify new monetization opportunities, customized offerings etc. It opens the doors to new revenue potential.
5. Enhance Customer Intimacy
By analyzing how, when and why products or services are consumed by customers, organizations can fundamentally improve customer experiences through personalized engagement, tailored pricing, proactive issue resolution and contextually relevant cross-selling.
6. Accelerate Defect Detection
Combining IoT monitoring data with other information like incident reports, warranty claims and structured product testing can accelerate the detection of defects, hazards and quality issues tremendously – unlocking orders of magnitude improvement in resolution times.
7. Reveal Emerging Market Transitions
Sophisticated analysis of IoT data combined with ancillary signals from adjacent sources can reveal subtle shifts in customer preferences, nascent market trends and opportunities that promise massive future upside if capitalized early. IoT analytics serves as an unmatched market radar.
8. Continuously Optimize Supply Chain Logistics
By instrumentation key assets like trucks, railcars, cargo containers and parcel boxes combined with geo-spatial analytics, organisations gain ultra visibility into inventory flows. This enables logistics improvements on a continual basis.
In essence, IoT analytics empowers organizations to transition from reactive to predictive – and ultimately to prescriptive – analytics. The result is data-driven intelligence that fuels continuous performance improvements on all fronts.
IoT analytics delivers powerful benefits across operations, innovation, and revenue
Real-World Use Cases
IoT analytics is tremendously versatile in its applications across diverse industries. Here are some real-world examples:
Manufacturing
GE employs IoT analytics across multiple factories to minimize unplanned downtime through predictive maintenance. By monitoring vibration, temperature and other machine health data for anomalies detection can enable early issue identification to prevent breakdowns. This has realized over $50 million in cost savings annually.
Transportation
Shipping giant Maersk analyzes real-time tracking data, weather forecasts, and historical shipment patterns using machine learning to dynamically optimize routes across its massive cargo ship network. This significantly cuts fuel consumption and greenhouse gas emissions.
Utilities
Smart meter analytics correlates household consumption patterns with data from customer databases, pricing structures and meters to reveal specific households that would benefit the most from efficiency upgrade incentives. This helps to lower peak demand.
Smart Cities
Authorities employ various IoT analytics techniques across connected infrastructure to analyze traffic conditions. Insights extracted enable smarter traffic signaling coordination to reduce congestion which also lowers pollution and commute times.
Retail In-Store Analytics
Analyzing checkout scanner data, shelf sensor tracking combined and video feeds helps retailers optimize planogram layouts, staff workloads, inventory replenishment and merchandizing campaigns to maximize store sales.
Logistics Monitoring
Package delivery leaders like FedEx and UPS instrument trucks, containers and parcels with IoT sensors to gain end-to-end visibility into shipment routes, environmental conditions and handling impacts to optimize delivery efficiency while preventing losses.
Auto Insurance
Leveraging IoT-enabled usage-based insurance apps and in-vehicle telematics makes it possible to monitor policyholder driving behavior. This allows insurers to set premiums based exactly on risk profiles accurately reflecting how, when and where vehicles are actually driven.
These are but a few examples of the diverse environments in which IoT analytics is driving transformational gains today. Essentially, anytime vast amounts of sensor data can be collected and acted upon intelligently, IoT analytics opens new vistas for performance improvements.
Key Challenges
However, multiple technical and organizational challenges can get in the way of IoT analytics success. Here are some top hurdles that need to be navigated:
Volume: The sheer scale of IoT data flowing across distributed environments makes it hard to efficiently ingest the firehose of data and process it fast enough for real-time applications. This demands advanced scalable architectures.
Variety: Myriad protocols, data structures, and semantic representations across fragmented IoT landscapes make it extremely hard to aggregate, combine and analyze heterogeneous data streams. Resolving this requires adaptable data integration capabilities.
Velocity: For time-critical IoT analytics use cases predictive maintenance or autonomous vehicles, analytical intelligence must be derived rapidly and deployed equally swiftly back into the field. Architecting this real-time analytical loop at scale is non-trivial.
Data Quality: With so much raw machine-based data of mixed pedigree across far-flung IoT infrastructure, validating integrity, managing errors, accounting for missing values and resolving conflicting readings consumes tremendous effort. Garbage in garbage out prevails.
Legacy Constraints: Brownfield IoT deployments have to account for decades-old legacy systems and specialized data environments like factory floor tool buses or building automation protocols. These are rigid constraints that raise integration complexity substantially.
Multidisciplinary Skillsets: Because IoT analytics merges OT/IT domains and necessitates specialized skill sets, organizations struggle to piece together, train and retain high-caliber multi-disciplinary teams that fuse engineering, statistics and software developer/architect roles. Talent scarcity abounds.
Analytics Velocity: Operationalizing analytical model prototypes developed by data scientists for real-time production deployment at scale with adequate monitoring and controls remains a huge challenge with long lead times. This inhibits realizing value.
Mastering IoT analytics requires overcoming these intertwined technical and organizational constraints through a combination of platform capabilities, data science skills, and governance protocols.
Emerging Innovation Frontiers
While IoT analytics has already delivered tremendous value in early implementations, continuing innovation across key dimensions promises even bigger breakthroughs in what IoT-driven intelligence can achieve.
AI and Machine Learning: Rather than rely solely on predefined algorithms, machine learning techniques that automatically build analytical models by learning from raw data will expand the power of IoT analytics exponentially. Deep learning networks in particular will unlock new realms of operational and business capabilities.
Digital Twins: IoT analytics combined with digital twin simulations of entire systems or processes will enable organizations to conduct sophisticated what-if analysis to evaluate the outcomes of endless permutations in a virtual world before actual deployment. This will supercharge innovation.
Edge Analytics: Pushing analytical intelligence to the network edge rather than relying exclusively on centralized cloud platforms will enable breakthroughs in latency-sensitive applications along with data connectivity/storage economies. The topology of compute will transform.
Video IoT and Computer Vision: Applying machine learning to unlock insights from camera inputs will trigger breakthroughs in areas like retail analytics, autonomous navigation, shop floor monitoring and smart spaces.
5G and LPWAN Connectivity: High bandwidth and low latency 5G networks along with long range LPWAN access will drastically expand the envelope of what is possible in real-time data capture and remote control applications across industries.
IoT-Optimized Chipsets: Specialized processors and system-on-chip devices purpose built to handle repetitive IoT analytics tasks efficiently are just starting to emerge. But they hold the promise of dramatic improvements in embedded analytics capabilities.
Graph Analysis: Mapping relationships between entities within IoT data landscapes using graph analytics techniques will uncover valuable insights into complex interdependent associations that underly many operationalenvironments and business verticals.
Ultimately, IoT analytics sits at the nucleus of multiple technology forces like pervasive connectivity, distributed computing, scalable data infrastructure, AI/ML and domain specialization that are intersecting to reshape nearly every industry over the coming decade.
Roadmap for Success
For most enterprises, getting started with IoT analytics can be daunting given the technical complexity and organizational transformations needed. Here is a phased roadmap to drive adoption and business value systematically:
Start with Clear Business Objectives
Rather than boil the ocean, zero-in on 1-2 high-value business objectives where IoT analytics can drive orders-of-magnitude improvement. Common areas include overall equipment effectiveness, customer churn reduction or new product sales lift. Let material outcomes guide technical direction.
Focus on Quick Wins
Big bang analytics platforms tend to fail. Instead, focus on 2-3 targeted use cases that lend themselves to rapid prototype iteration based on real operational data. Build momentum with tangible quick wins before expanding scope.
Iterate Based on Feedback
Leverage agile sprints to orchestrate MVP pilot deployments of IoT analytics capabilities that prove value but can be refined quickly based on business unit feedback before industrialization. This fail-fast model is key to cost-efficient innovation.
Account for Data Wrangling
Across numerous industry studies, data wrangling like collection, cleansing and preparation consumes 80% of effort in analytics initiatives before advanced analysis happens. Designing streamlined data pipelines needs equal priority as applying machine learning algorithms.
Operationalize Through Phased Roadmaps
The transition from data science prototypes to real-time analytics supporting live operations at scale is non-trivial. Architect this translation through phased roadmaps spanning technology, process and organization.
Drive Change Management
IoT analytics will disrupt workflows, decision rights and resource allocation. Successful adoption requires programmatic change management across processes, systems, policies and teams. Factor this rigorously into program designs.
Reassess Constantly
Build feedback loops to continually assess if IoT analytics deployments are delivering ROI and allow for course corrections. Given rapid tech advancements, it is critical to revalidate direction aligns to business objectives consistently.
With the above guideposts top of mind, technology leaders can adopt a systematic approach to extracting maximum value from IoT analytics while optimizing innovation investments. It requires business priorities to drive technology capabilities more than platforms driving nebulous direction. By maintaining this core alignment, IoT analytics can unlock its extraordinary potential to revolutionize nearly aspect of modern businesses through data-driven intelligence. The key is navigating adoption as a holistic transformational journey encompassing processes, teams, partners and culture – not merely installing software.
Key Takeaways
Here are the big-picture strategic guideposts to keep top of mind:
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Start Small: IoT analytics demands sophisticated technical capabilities and business acumen. Start with high-value use cases, iterate based on data insights, and expand slowly across the enterprise.
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Leverage Partners: To overcome skill constraints, leverage external specialists including systems integrators for technical build-outs and data science consultants to extract insights.
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Focus on Ecosystems: Evaluate not just software capabilities but also the ecosystem and community around IoT analytics platforms including connectors, developer support, shared best practices etc.
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Architect for Flexibility: Build upon open, standards-based architectures rather than monolithic vendor stacks to preserve flexibility as business needs evolve.
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Operationalize Rigorously: Data science prototypes must be industrialized into sustainable and governanced production environments responsibly before scale impact is realized.
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Measure Outcomes Obsessively: All technical capabilities must tie back clearly to business value delivery. Revalidate outcomes continuously and course correct around objectives.
For technology leaders embarking on IoT analytics initiatives, honing strategy with these guideposts top of mind will smooth the path to accelerated results. The journey demands as much economic insight as technical skills. But for enterprises that get it right, IoT analytics propels a future brimming with unprecedented data-driven intelligence that unlocks enormous new value and possibilities across every facet of business.
