As of 2025, there are 25 billion IoT devices — and this is just the start. By 2034, that figure is expected to reach 40.6 billion worldwide.
Everything from smart factories to healthcare devices and entire city infrastructures will generate continuous data streams, creating opportunities for better innovation, efficiency, performance, and decisions across every sector.
With so much data coming from all directions, organizations might struggle to separate what matters and analyze it quickly enough to guide decisions.
Enter Internet of Things (IoT) data analysis. Internet of Things data analysis enables organizations to interpret massive datasets and uncover the underlying meaning, driving informed predictions, decisions, and growth.
What is Internet of Things Data Analysis?
Connected devices can generate thousands of data points per day, making it all too easy for organizations to become lost in the data. Internet of Things data analysis involves collecting, processing, storing, and interpreting that data, transforming it into meaningful insights for smarter decisions, faster responses, optimized system performance, and long-term strategic planning.
Internet of Things, big data, and artificial intelligence (AI) go hand in hand. Devices collect and share information from sensors, machines, equipment, vehicles, and other sources, transmitting it through standardized IoT protocols like MQTT, Bluetooth, CoAP, or HTTP.
Big data systems then process, organize, and store it, while AI uncovers patterns, predicts outcomes, and even automates decisions in real time. Together, big data, IoT, and artificial intelligence move organizations beyond simple monitoring to strategic insights and effective decisions.
Why Does IoT Data Analysis Matter?
Between 2024 and 2030, the analytics market has a projected CAGR of 24.8%, with North America expected to hold a $1.23 billion share of the global IoT analytics market by 2030.
It’s no surprise. With more connected devices than ever, the ability to interpret data in real time has become increasingly vital. These days, IoT analytics is often a crucial component of achieving a successful digital transformation.
Thanks to IoT data analysis and artificial intelligence combined with big data, organizations can improve:
Real-Time Decision Making
By combining IoT data with AI, organizations can see what’s happening in real time and respond just as fast. Continuous monitoring gives teams a clear view of operations and performance, making it easier to spot issues early and act before they grow. At SEI, we help clients use these insights to stay nimble and make smarter decisions on the fly.
Cost Effectiveness
IoT data analytics also help uncover inefficiencies and waste across operations. While you can’t personally monitor every asset or process in real time, Internet of Things monitoring makes it possible to identify where resources are being overused or underperforming.
By tracking equipment utilization, energy consumption, production metrics, and other key data, you can spot inefficiencies early, reduce energy consumption, and streamline operations. Ultimately, IoT data analytics helps you minimize unnecessary spending and make better use of every asset and kilowatt.
Predictive Maintenance
IoT sensors track how equipment performs — things like temperature, vibration, and usage. These details reveal a lot. For example, unusual vibration can signal parts wearing down or moving out of alignment, while rising temperatures might point to friction or overload. When that data is analyzed in real time, you can catch small issues before they cause big problems. The result? Less downtime, fewer costly repairs, and equipment that lasts longer.
Scalability and Flexibility
IoT ecosystems are becoming increasingly large and unwieldy, meaning simply having a database for data isn’t cutting it anymore. You need the tools to interpret, predict, and act on that data, which means adopting a modern approach to data analytics.
Big data platforms can process enormous amounts of information with the speed, accuracy, and efficiency required to scale IoT device networks.
Product Development
IoT data analysis can also make a major difference in innovation and product development. After all, it lets organizations tap into real-world usage trends and reveals how end users actually interact with products. Organizations can then use that information to pinpoint pain points and refine features.
Additionally, continuous feedback from connected devices helps teams monitor performance and make informed choices to enhance quality, reliability, and user satisfaction.
Customer Experiences
In retail, healthcare, transportation, and smart cities, IoT analytics help organizations understand people’s behavior and preferences. These insights enable them to create services, products, experiences, or even entire cities that specifically cater to people’s needs and desires, leading to increased satisfaction.
Resource Utilization and Energy Consumption
Companies can also use IoT analytics to optimize energy and resource consumption. From reducing power usage on manufacturing floors to tracking equipment idle time and managing lighting schedules, IoT data provides organizations with visibility into how resources are utilized across their entire organization.
By closely monitoring usage patterns and making adjustments accordingly, organizations can cut waste, lower costs, and improve sustainability.
IoT sensors track how equipment performs — things like temperature, vibration, and usage. These details reveal a lot: unusual vibration can signal parts wearing down or moving out of alignment, while rising temperatures might point to friction or overload. When that data is analyzed in real time, you can catch small issues before they cause big problems. The result? Less downtime, fewer costly repairs, and equipment that lasts longer.
What Does IoT Data Analysis Look Like In Action?
Sifting through massive amounts of operational data by hand would take months — and cost a fortune. IoT analysis changes that. By translating streams of raw information into clear, usable insights, organizations can make faster, smarter decisions to use resources more efficiently, cut waste, and spot problems before they grow.
In practice, that might look like a factory using sensors to track machine health, predict maintenance needs, and adjust energy use in real time. Or a manufacturer that shifts from scheduled tune-ups to condition-based maintenance, repairing equipment only when data shows it’s necessary. For example, GE’s Brilliant Factories used IoT sensors and automation to improve safety and efficiency, cutting 3,800 high-risk labor hours and saving $153,000.
Entire cities are also reaping the benefits. They can adjust lighting, traffic signals, and waste management in real time based on IoT data insights. Thanks to data from IoT sensors, cities may cut collection runs by 15%, reduce overflowing trash cans by 20%, and increase recyclable collection rates by 15%.
What Are the Challenges in Internet of Things Data Analysis?
As powerful as IoT data analysis can be, it also brings its own set of challenges:
Data Volume and Velocity
IoT networks can generate multiple terabytes of data each day, leaving organizations with plenty of information at their fingertips. Without advanced analytics, teams often struggle to make heads or tails of what the data is telling them, missing out on critical insights that could improve performance and drive smarter decisions.
While IoT data analysis can uncover valuable insights, doing so at scale demands significant processing power, scalable infrastructure, and skilled data professionals to manage and interpret results in real time.
Data Quality and Accuracy
Inconsistent readings, faulty sensors, or data transmission errors can lead to inaccurate insights. That’s why proper data validation, calibration, and cleansing processes are vital for ensuring reliable, high-quality data that decision-makers can trust.
Data Integration and Interoperability
With so many different IoT devices, sensors, and systems in play, data often comes in incompatible formats or from disconnected platforms. Integrating all these data streams into a unified view can be a challenging undertaking.
Without standard Internet of Things protocols, data management frameworks, sensors, platforms, and analytics tools that “speak the same language,” valuable information can remain siloed, making it difficult for organizations to gain complete visibility, analyze trends, and coordinate actions across systems.
Compliance
From GDPR in Europe to HIPAA in healthcare, organizations must ensure their IoT systems comply with data protection laws and industry standards at all times. Noncompliance can result in expensive fines as well as reputational damage and a loss of customer trust.
Security
Every connected device is another opportunity for risk. Without proper safeguards, IoT devices can become entry points for cyberattacks and data breaches that compromise entire systems and ruin reputations. Just look at the Mirai Botnet.
In 2016, Paras Jha, Dalton Norman, and Josiah Whit launched a DDoS attack and released the code, allowing other cybercriminals to exploit unsecured IoT devices like cameras and routers running on the ARC processor.
Best Practices For Internet of Things Data Analysis
Making sense of IoT data takes the right strategy, tools, and practices. Here’s how you can make the most of Internet of Things data analysis:
Start with a clear strategy: Before deploying devices, think about your goals. Do you want to raise efficiency and cut costs? Would you like to predict maintenance needs or improve safety? Establishing your goals upfront ensures you collect and analyze the right data.
- Start with a clear strategy: Before deploying devices, think about your goals. Do you want to raise efficiency and cut costs? Would you like to predict maintenance needs or improve safety? Establishing your goals upfront ensures you collect and analyze the right data.
- Choose the right database: Massive amounts of IoT data needs a reliable database that can keep up. Pick one that offers scalable storage, data durability, and the ability to ingest large amounts of data at a time without sacrificing performance.
- Integrate with big data and artificial intelligence: IoT gets even smarter when it works alongside big data and artificial intelligence. Together, they help you spot patterns and problems faster, predict what might happen next, and fine-tune how your business runs. That means fewer surprises, better decisions, and operations that keep improving over time.
- Prioritize data quality and standardization: It doesn’t matter how much data you have if it isn’t accurate and consistent. Use standardized Internet of Things protocols to ensure data is interoperable across platforms and systems.
- Monitor and visualize data in real time: With the right dashboards in place, IoT systems let you see what’s happening as it happens. You can track performance, spot issues the moment they appear, and respond before small problems turn into big ones.
- Implement strong security policies: To protect data integrity and user privacy, establish security measures throughout your IoT ecosystem. That means having device authentication, encrypted communication, regular vulnerability testing, continuous monitoring, timely software updates, and strong governance policies in place.
Bridging the Gap from Devices to Decisions
Most organizations have no shortage of IoT data. What they’re missing is a way to make sense of it. With the right tools and analysis, that information becomes real-time intelligence that powers better decisions, predicts maintenance needs, and keeps operations running smoothly.
In a factory, that might mean identifying a machine about to overheat. In a hospital, it could mean tracking equipment usage to improve patient care. Across industries, the goal is the same — to turn constant signals into clear, strategic action.
We’ve seen how easy it is for teams to get buried in dashboards and reports without gaining clarity. Our consultants help cut through the noise, building strategies that make IoT data understandable, actionable, and valuable from day one.
