What is IoT Data Analytics?

At the heart of deriving value from an IoT implementation is not just data – it is data analytics. IoT “listening posts,” such as devices and sensors, are throwing off a lot of streaming data, but to make sense of it and take meaningful downstream actions requires driving the analytics process efficiently and at scale.

What is IoT Data Analytics?

IoT data analytics is the process of discovering actionable insights from the immense volume of data generated by interconnected IoT (Internet of Things) devices. At KORE, we understand that the real power of IoT lies not just in connectivity but in the ability to harness and interpret this data intelligently and efficiently. Imagine every single sensor, device, or machine in your network as a source of invaluable information waiting to be leveraged into smart business decisions.

Simply put, IoT data analytics involves the dedicated collection, processing, and analysis of data points derived from these connected devices within your IoT. This process is critical in changing raw data into meaningful insights that drive informed decision-making and operational success.

How does IoT Data Analytics work?

IoT Analytics is pivotal to successful digital data transformation. The common components of IoT Data Analysis are data collection, storage, processing, analysis, and visualization. The data provided by the devices within our IoT network is unactionable before being cleaned and analyzed, which is where data analytics creates value. This process simplifies the complexity required of the massive amounts of data that IoT produces into easy to understand insights that create smart data-informed business decisions. The four types of analytics commonly applied in this process are descriptive analytics to address the current situation, diagnostic analytics to determine the cause of the current situation, predictive analytics to foresee possible future events, and prescriptive analytics to create actionable plans based on the insights gained.

Benefits of IoT Data Analytics

As discussed, IoT analytics opens doors to unlocking incredible value from the data captured across connected products, processes, and interactions, creating significant improvements and generating valuable insights. Here are the key benefits of implementing IoT Data Analytics:

• Increased Revenue: IoT data analytics enables businesses to identify new revenue streams, optimize pricing strategies, and offer personalized services, ultimately driving increased revenue.
• Reduced Time to Market: By leveraging real-time insights from IoT data, companies can accelerate product development cycles, iterate faster based on customer feedback, and bring new products to market more quickly.
• Reduced Costs: IoT data analytics helps organizations optimize resource utilization, predict maintenance needs to prevent downtime, and streamline operations, leading to reduced operational costs.
• Improved Quality: Through continuous monitoring and analysis of IoT data, businesses can detect defects early, ensure product and service consistency, and enhance overall quality standards.

Main Challenges of IoT Analytics

Despite the incredible benefits of IoT Data Analytics, it is important to balance and prepare for the associated challenges. Three common challenges associated with IoT Analytics are:

1. Data Security and Privacy: It can be difficult to provide adequate encryption, authentication, and access control mechanisms to protect sensitive IoT data from unauthorized access and breaches. Privacy concerns also arise due to the potential for data collection on personal activities and behaviors.
2. Data Quality: It can take a great extent of resources to manage the variety, velocity, and volume of data generated by IoT devices to ensure accuracy, consistency, and reliability. Factors such as sensor accuracy, data integration issues, and real-time processing pose significant challenges to maintaining high data quality.
3. Scalability: Forward thinking is important to handle the exponential growth of IoT devices and data streams, which requires infrastructure that can efficiently process and analyze vast amounts of data. Scaling analytics platforms and algorithms to cope with increasing data loads without compromising performance is crucial.

IoT Data Analytics with KORE

A framework to think about harnessing this data requires one to think about two dimensions: first, understanding the types of data that can be analyzed; and second, how the end-user will use this data. Data can be very different in nature. Normally one would think of the data that comes straight from the sensor that being used to perform various kinds of business enablement use cases. However, there is also metadata that can predict device behavior, anomalies and security issues. In addition, your network provider may provide to you usage data – bytes/dollars in a given period.

Looking at the second dimension of how an end-user would engage with the data, there are five essential steps an end-user should follow to build a successful data path:

• Establish goals
• Gain visibility
• Identify opportunities
• Perform actions
• Track outcomes

If you bring both of these dimensions together, the two-dimensional framework allows one to think clearly about a variety of use cases, depending on the type of data and the end user in mind. If you are in the fleet management industry, where KORE has deep expertise, here are three “free” examples to illustrate the approach:

Example One:

• Type of Data: Telematics sensor data.
• User: Fleet manager.
• Establish goals: Optimize fuel utilization across fleet.
• Gain visibility: View start, stop, and idle times across the fleet.
• Identify opportunities: Pinpoint specific fleet outliers around start/stop times and create targets around optimal engine start-stop rules.
• Perform actions: Make recommendations for driver protocols and future vehicle purchases based on data.
• Track outcomes: View start, stop, and idle time trends and fuel consumption trends. 

Example Two:

• Type of data: Metadata regarding authentication and session.
• User: Security Director.
• Establish goals: Spot security issues with devices (anomaly detection).
• Gain visibility: Observe device behavior by auditing metadata such as flow of traffic, times of connection, etc.
• Identify opportunities: View changes in device behavior against a “profiled” behavior to take appropriate action. E.g., the normal trend is to send 100KB per day, but since yesterday, the device is sending 30MB (is someone is starting to use the device SIM in their iPad?).
• Perform action: Create policies that address anomalous device usage that raises security threat levels.
• Track outcomes: View device behavior anomaly trends.

Example Three:

• Type of data: Cellular usage data
• User: Finance team member looking at total network costs in the IoT Fleet program
• Establish goals: Create optimal rate plan allocation.
• Gain visibility: View connection inventory and associated rate plans, month-to-month usage across account, and usage for individual assets.
• Identify opportunities: Identify assets consistently resulting in overages, asset outliers with respect to similar assets and opportunities to switch off, and assets that incur account access fees and no usage.
• Perform action: Initiate audit of all areas that result in higher usage costs.
• Track outcomes: View usage and billing trends along with usage outliers.

As IoT deployments scale up in organizations from a few hundred devices to hundreds of thousands of devices, putting a use case mapped against the type of data used and the five-step process outlined in the framework above, can help realize the different types of value IoT data can unlock.

Learn how a partnership with KORE will simplify the challenge of IoT data analytics to help meet your unique IoT goals.