IoT data analytics: 
benefits, use cases & main challenges

IoT data analytics: benefits, use cases & main challenges

December 27, 2023

IoT data analytics market state

The growing demand for fast and reliable decisions based on relevant operational data drives the upsurge of the IoT data analytics market, stimulating the development of more and more sophisticated analytics solutions.

the value of the global IoT analytics market in 2022

Exactitude Consultancy

the projected value of the IoT analytics market by 2029

Exactitude Consultancy

North America’s share of the global IoT analytics market

Exactitude Consultancy

The architecture of IoT data analytics

IoT devices gather enormous amounts of data daily. The data is transferred to the IoT gateway that facilitates back-and-forth data transmission between IoT devices or sensors and next to the IoT data processing layer using different communication protocols like MQTT, HTTP/HTTPS, or Zigbee. The IoT data analytics layer is where data from various IoT devices and sensors turn into valuable insights. IoT analytics consists of several components that work as an entity.

Data visualizationApplication layerIoT data analytics layerData storageData processingData analysisData transmission
MQTT protocol, Wi-Fi,
Bluetooth, ZigbeeIoT gatewayDevicesSensorsActuatorsData collection layer

Data storage

IoT data storage accumulates information collected from IoT devices for further processing and analysis. IoT databases store historical, operational, and real-time data to be easily searchable and accessible.

Data processing & analysis

At this stage, the information is processed to be clear, consistent, and ready for further analysis. Data processing and analysis techniques include data cleansing and mining, regression, factor and time-series data analysis, and machine learning techniques that help identify various patterns and relationships from the datasets.

Data visualization

At the application layer, the processed and analyzed data is presented to users in meaningful and user-friendly formats like table sheets, dashboards, graphs, and charts to quickly and effectively make informed data-driven decisions.

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Top Internet of Things analytics use cases

Inventory management

RFID tags, barcode scanners, and other sensors allow companies to gather real-time data streams of inventory levels, thus helping shop managers make informed decisions on when to reorder goods and in what quantities. IoT analytics can also help predict based on the demand which products will most likely be in deficit soon.

Store layout optimization

IoT data analytics tools help increase the sales of unpopular products or goods with a short shelf life. IoT sensors can track customer behavior and interactions across the shop floor and suggest optimal layout options for better sales.

Personalized marketing

With IoT sensors gathering terabytes of information on how people interact with particular goods or services, marketing managers can make their advertisement campaigns more targeted and personalized, presenting consumers with context-sensitive content and generating more revenue.

IoT data analytics in real life

Precision manufacturing for Audi vehicles

Audi’s factory in Neckarsulm, Germany, produces about 1,000 vehicles daily, and there are about 5,000 welds in each car. Previously, 18 Audi engineers used to check the welding spots of a single selected car with ultrasound probes manually at the end of each day, but the company still could not be fully sure about the quality of the remaining 999 cars. Together with Intel, Audi implemented an ML-powered data analytics solution that can gather the data from the welding-gun controllers, compare it to quality standards, and alert technicians of a detected faulty weld. Such automation allows Audi to increase quality control accuracy and scope.
Audi vehicle manufacturing

Image title: Audi vehicle manufacturing
Image source: intel.com — Audi Precision Manufacturing

Best platforms for IoT data analytics

  • Configure IoT analytics with the help of MQTT topic filters to receive data from IoT devices only in required formats and frequencies.
  • Detect missing data, enrich data with external sources (like weather forecasts), and transform and reprocess raw and historical data.
  • Time-series data storage, as well as storage of processed and raw data.
  • Quickly run ad hoc or scheduled data queries with the in-built SQL query engine.
  • Continuously monitor your devices and predict maintenance with time-series data analysis.
  • Run a statistical analysis and forecast events and failures with pre-built Jupyter Notebook templates that include AWS-based machine learning models and visualizations.
  • Connect AWS IoT Analytics with third-party, custom or built-in AWS IoT analytics code containers, and automate and schedule your custom analysis.
  • Capture data incrementally and customize time windows for analysis.
  • Connect to Amazon QuickSight for data visualization.
Considerations

AWS data retention policy
Your data will be stored indefinitely unless you set up a retention period, after which it will be deleted.

Complicated pricing system
The final cost of AWS IoT Analytics will depend on the amount of data ingested, processed, stored, and transferred.

IoT data analytics implementation guide

Successful integration of an IoT analytics solution into your business workflows requires clear objectives and a step-by-step implementation plan. Here are the main steps of the IoT analytics implementation roadmap.

1

Define achievable goals

Determine what you want to achieve by implementing an IoT data analytics solution. The goals will depend on your business type and industry and, therefore, can vary from better control over production processes and improving energy management to predictive equipment or car maintenance and supply chain optimization.

2

Outline data collection mechanisms

Analytics heavily relies on properly gathered data. Implement the necessary IoT devices with sensors and communication protocols and ensure the data is collected and transmitted properly.

3

Set up data storage & management

Establish ETL processes to prepare data for storing, processing, and analysis. Design and implement a big data warehouse to effectively manage high volumes of data received from IoT devices.

4

Orchestrate a data analysis system

Implement mechanisms and algorithms for future data analysis, like BI tools, machine learning, statistical analysis, and predictive modeling, to derive valuable insights from historical and real-time data.

5

Introduce data visualization

Design and develop a dashboard layout to present analyzed data in a meaningful way and allow users to observe trends and anomalies. Implement a role-based approach to present only relevant data to specific organizational roles.

6

Introduce security measures

Strong security measures can ensure data protection throughout the whole journey, from data collection endpoints to reporting. Security mechanisms can include network security protocols and network segmentation, access management mechanisms, multi-factor authentication, and real-time monitoring.

7

Integrate IoT analytics with business systems

Connect your IoT analytics solution with other enterprise software, like CRM, ERP, or HRM, to enable more context-sensitive and comprehensive data-driven insights.

8

Train end-users

Establish coherent user training mechanisms to ensure all your employees interacting with IoT analytics tools can use the solution’s features and functions.

9

Ensure continuous improvement

Continuously monitor the effectiveness of your IoT data analytics system, improve its reliability and security if needed, and make sure it evolves along with your business objectives.

IoT analytics challenges & potential solutions

IoT data analytics is a highly complex business solution, so companies are likely to face some challenges when implementing it. Let’s review the most common obstacles and what can be done to overcome them.

Challenge

Solution

Data quality

Challenge

The success of IoT data analytics significantly depends on the quality of raw data gathered by IoT sensors. The most common data quality issues include data spikes, missing values, measurement deviations due to degradation of sensing materials, and noisy data.

Challenge

Solution

First, set up regular check-ups of the sensors’ health and provide regular firmware updates. Most importantly, ensure proper data management by employing data science techniques like data cleansing and mining, regression and cluster analysis, anomaly detection, and pattern recognition.

Data volume & velocity

Challenge

Companies can struggle to store, process and analyze big data gathered by multiple IoT devices.

Challenge

Solution

Combine cloud computing with fog and edge computing. It will reduce the processing load on the cloud by shifting the analysis of some data closer to the edge. We also recommend adopting machine learning and artificial intelligence techniques to speed up data processing and further decision-making.

Real-time analytics constraints

Challenge

For many IoT solutions, like self-driving cars or equipment monitoring systems, real-time or near real-time data analysis is obligatory, since a slight delay at the wrong time can lead to disastrous consequences.

Challenge

Solution

Adopting edge computing and machine learning capabilities in addition to IoT analytics platforms, as well as implementing real-time data processing frameworks, like Apache Storm or Flink, can help speed up data processing and ensure real-time analysis.

Scalability

Challenge

IoT analytics solutions must be highly scalable to store and process massive and continuously growing amounts of data.

Challenge

Solution

To ensure IoT analytics scalability, along with sufficient computing network resources, companies should opt for cloud-based IoT solutions with edge computing capabilities, distributed databases, and parallel processing frameworks like Apache Spark and Hadoop.

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IoT data analytics benefits

Real-time data analysis

The ability of IoT data analytics to collect and process data at any given moment can lay the groundwork for a more agile business.

Improved data accuracy

Advanced analytics mechanisms, along with machine learning and artificial intelligence, increase the accuracy and reliability of data analysis results, eliminating the possibility of human error.

Increased operational efficiency

By analyzing daily operations in real-time, organizations can swiftly identify existing or potential problems and take swift action to address issues or prevent them from happening.

Reduced operational costs

IoT data analytics solutions help utilize resources better, optimize production lines and energy consumption, and improve inventory management, thus decreasing organizational expenses.

Better risk management

IoT data analytics can identify patterns implicating a potential hazard or security breach and suggest preventive measures. This considerably increases safety in such sectors as manufacturing and healthcare.

Better sustainability policy

Organizations can use IoT data analytics insights in their sustainability initiatives. Businesses can optimize their energy consumption across production floors, shops, smart homes, or offices, becoming more sustainable.

Make use of data with IoT analytics

IoT data analytics has drastically changed business operations across industries. Despite certain challenges and rather high implementation costs, investing in IoT analytics is sure to prove economically viable. Operations automation and optimization based on reliable data-driven insights enable more strategic thinking and the discovery of new business opportunities. If you are looking for a vendor to implement an IoT analytics solution and change your business once and for all, Itransition experts are ready to help.