The Internet of Things (IoT), which is used to drive value in nearly all sectors, from manufacturing and logistics to retail management and resource management, has a lot of potentials. The IoT collects data from network-connected “things”, which include drones, delivery trucks and medical devices.

IoT devices and sensors can collect a lot of valuable insights but they also generate huge data streams at high speeds that are hard to store, process, analyze, secure, and store. IoT data can also be highly volatile, so organizations may miss the best opportunities to exploit time-sensitive insights.

This article discusses how IoT and real-time data analysis can be combined to create new opportunities in a variety of industries.

Real-Time Data Processing IoT Apps

Organizations from all sectors struggle to keep pace with the increasing IoT adoption. IoT devices can capture gigabytes worth of data in just a few hours. This is before you take into account the data coming out of your CRM, social media channels and financial reports.

Machine learning, Big Data analytics and AI are all evolving at an incredible pace. Organizations can quickly extract valuable information from heterogeneous IoT data sets and use AI to analyze them. This allows them to respond to changing conditions in real-time. These technologies together are driving revolutionary innovations. Big Data’s inherent properties are ideal for fast training AI and ML apps.

These intelligent applications can be used to automate tasks, detect security threats, and predict equipment failures in real-time. Fully-autonomous solutions rely on AI to drive the vehicle and rely on a connected network IoT devices as a guide.

Real-time analytics is able to support drivers with safety features such as automatic braking, parking and collision avoidance through transmitting data. There are many examples of AI, advanced analytics and IoT that can be done, but they won’t make those promises without the right tools.

Powerful Computing is the Key to Real-Time Information

The majority of IoT platforms currently in use were created to connect devices and combine and process data streams from multiple heterogeneous sources. These platforms often address many of the challenges IoT presents–like storage, security, and interoperability–and they can integrate with data analytics solutions to provide valuable business insights. Real-time data processing is not possible because many data analytics solutions use a cloud computing architecture called Platform as a Service.

A recent Dell report shows that cloud-based IoT data processing has many limitations. These include security risks, latency and missed opportunities to take advantage of powerful, real-time insight. Although IoT data streams capture the current situation, processing them means that they are sent to the cloud for analysis and processing. This can be accessed later. Also, you’re working in a system that allows you to send data to remote locations at a volume that can exceed network bandwidth. This can cause wasted storage space and unusable insights.

According to the report, only 29% of participants have integrated edge computing in their analytics strategies. However, 69% of respondents believed that prioritizing IoT computing would help them achieve their primary business goals. Edge computing is not enough to unlock real-time data analytics. Technology like 5G, WiFi6, IoT platforms such as Kaa and AWS and event-driven architectures like Kafka and Spark, Storm, Cassandra and BigTable, all designed to process continuous streams, are merging to enable real-time Big Data analysis.

Convergence of IoT/Big Data Analytics

Companies have many new opportunities to build more competitive business models due to the convergence of IoT and Big Data. According to Forrester’s 2020 Predictions enterprise strategy is becoming an essential initiative to drive digital transformation. The report acknowledges that Big Data interest has declined over the years. However, machine learning and AI innovations are driving renewed interest because they offer new ways to use data.

We are also seeing IoT adoption accelerate as we see more affordable hardware, software and sensors. There are a growing number of connected “things”, including audio, video and images, that capture continuous data streams and metrics to measure machine functions and environmental conditions.

Here are some examples of how top companies use these technologies to create value.

• Disney. Disney uses advanced analytics to large IoT data and machine-learning techniques to create customized in-park experiences. A wearable, RFID-enabled MagicBand collects customer traffic patterns and allows guests to access hotel rooms. It also allows them to charge their purchases back to the room. These insights can be applied to many use cases by Disney teams. For example, optimizing park logistics can reduce wait times and redirect guests to more crowded areas of the park. These insights could also be used by Disney to predict the guest’s favorite character or arrange surprise meet-and-greets.
• CPS Energy. San Antonio-based CPS Energy talked with SAS about how they use data analytics to solve multiple problems, from helping customers save money to detecting leaks. The utility’s strategy is to collect as much data from smart meters, consumers’ usage habits, street lights and other sources as possible. Consumers can benefit from this combination of real-time anomaly detection, usage and event stream data. The utility can use real-time analytics to detect outages or leaks and respond as soon as possible. CPS can long-term use this huge amount of data to create the psychological triggers that encourage customers to adopt energy-efficiency measures or offer products that aid consumers.
• Primex. The following example shows how a company might look at Big Data analytic strategies to solve a problem. A 2016 SQLStream case report shows that Primex, an IoT company, decided to replace its legacy architecture by a cloud-based, more efficient solution. The serverless architecture they chose included Amazon Web Services Lambda, Kinesis Streams and CloudWatch. This was initially a pragmatic choice as Primex couldn’t maintain an open-source platform such as Apache Spark. The company had 150K connected devices in the field at the time. This meant that they were processing over 67K AWS Lambda queries every five minutes. The system was not designed to handle large amounts of data. One incident that occurred after a four-hour outage caused the system to take 20+ hours to process massive sensor data backlogs. In addition to high costs (around $565 per day), instability, latency, cost and latency, the organization was also subject to high levels of instability. Primex decreased Lambda costs by switching to SQLStream Blaze, a SQL-based platform that allows real-time stream processing. Primex charges clients a fixed rate instead of AWS which charges per transaction. The company’s ability to process and ingest Big Data sets and its low latency response time highlights the importance of choosing the right architecture for your solution.
• Alibaba. Ant Financial’s financial services arm uses real-time analytics to assess potential borrowers. With its real-time credit scoring system, the Chinese tech giant allowed merchants of all sizes to apply for microloans quickly and without collateral. These online solutions enable more small business owners to take part in the economy, according to an IFC Report. This is important because 70% of female entrepreneurs had difficulty obtaining loans from traditional banks, making it difficult for them to scale up and weather economic hardship.

What role does big data analytics play in IoT?

Although the Internet of Things (IoT) and Big Data are distinct concepts, they are becoming more interconnected. An IoT is a vast network of sensors that gather unprecedented amounts of data from many sources, feeding into the larger Big Data landscape. Here’s an example of how much data one device could collect.

The Ouraring is a device worn on the finger of a user and records their sleep, temperature and activity. The device records data at 250 times per second.

This is a good example of how fast water can be poured into Madison Square Garden at a rate per cubic foot. It would take us seven hours to fill the garden. This is a lot of data! These data could include customer usage insights, sentiment analysis and sales metrics. Big Data and IoT can create contextual insights that can then be used to improve products, processes, and services and generate more revenue.

Analytics platforms that use Big Data to analyze big data can unlock this data by taking unstructured IoT data (such as foot traffic to a theme park, weather patterns or patient health) and analyzing it alongside other data sources to give a holistic view. Platforms can then organize this information into easily digestible insights that companies will use to improve their processes. This allows environmental data such as surveillance footage, log files, log files and geo-location data to be combined with social media and consumer behaviour insights to provide a deeper understanding of your audience. It brings them to life in a way marketing metrics cannot.

What is the relationship between IoT (Internet of Things) and Big Data?

Carrie MacGillivray, IDC Group vice president of IoT and Mobility, stated that the IoT is driving more value across the public and private sector by enabling information exchange between people, processes, as well as the creation of a system of connected objects. IoT applications produce raw data from devices and sensors, which is then stored in a central repository called a data lake. These data lakes contain IoT data as well as structured data like customer profiles and transaction records. They also house unstructured data, such social media data, logs, and emails.

Analytic platforms for Big Data can generate visualizations and reports from the insights sourced from all data sources that feed into this data lake. This gives a clear picture of how external factors such as market trends, market fluctuations, and environmental conditions affect what’s going on inside a company.

IoT data is also beneficial for AI-based analytics tools. You can train AI applications to effectively understand and make predictions using high-volume, diverse IoT data. This can help improve business results.

How to extract value from IoT Data

Big Data analytics and IoT are not just promising future use cases; they are essential tools to stay competitive now. These analytics allow organizations to extract value from IoT systems and sensors by analyzing IoT data using existing business tools and third party data sets to provide more context information.

This information can then be used to improve products, services, or experiences. To get the most out of their investments, companies must ensure that they have the infrastructure to enable real-time data processing on a large scale.