Internet of Things (IoT) applications could have a $11 trillion impact according to research for the McKinsey Global Institute, by 2025.

Along with IoT’s prosperity and influence, the world has changed significantly. Now, countless of devices, regardless size or type, are interoperable and interconnected, promising to bring  seamless and synchronized data to businesses. 

However, having IoT data and knowing how to manage, thus turn it into valuable insights efficiently is an entirely different story. 

This article will help you navigate through the complexity of IoT data as it will provide approaches and key points to remember when dealing with high-volume data in IoT.

The power of data in IoT 

Knowing that IoT data and IoT analytics hold the key to business success, efficiently managing that will lead your organization to outplay your competitors. But, how can we do it correctly?

In a research from IoT Now, 58% of the respondents considered their IoT project to be unsuccessful due to various factors, while 79% of the sample agreed that data deems to be a very important success criterion that can act as a game-changer.

Table of factors that lead to failure of IoT data project

(Source: IoT Now)

However, only a few businesses can truly realize the potential of data in IoT and manage it efficiently. In a finding from McKinsey, less than 1% of the IoT data generated and collected are currently used for decision-making. 

While connecting interoperable devices is vital in IoT projects, companies should also integrate and customize the analytical tool that can derive business insights from real-time data streams from these diverse devices. 

Turning raw data into analytics, such as prediction and optimization, is critical. For example, companies can predict maintenance issues based on the analysis and combination of data from multiple components in the system, which can help businesses leverage and optimize their operations..

Knowing how to handle the high-volume data and IoT analytics generated by multiple sources is extremely important to those businesses who want to stay ahead of the game.

Understanding your IoT data

Before embarking on a journey of making sense of your IoT data, the first thing that businesses need to bear in mind is the nature of the data itself: Data in IoT is heterogeneous. 

The data captured by devices is complex and comprises multiple formats. From structured, unstructured to semi-structured data, your IoT devices are quite creative in the data that they produce for you. 

Your organization might receive a huge volume of data, including analog signals, discrete sensor readings, device health data, or large files for images or video every day. And since IoT data is not uniform, there will be no one-size-fits-all approach for managing your data and IoT analytics.

However, businesses might consider and apply universal approaches and standard processes as a starting point.

Handling high-volume IoT data: the approaches and key points

Here are some approaches for dealing with your IoT data, from storing to analyzing.

1. Data storage

Transforming and securing data

Data transformation usually occurs to perform normalization before storing the data in IoT from different devices. For instance, we may consider re-arranging out-of-order events and  eliminating stale information if data is time-sensitive. By transforming and selecting which data is to keep, this can help organizations save bandwidth and storage costs.

Data security is crucial as it must be transmitted and stored securely using protocols and encryption. Companies need to make sure that there will be no external access to the data and sensitive information.

Storage strategies

Your IoT projects are only successful with a clear strategy envisioned and carved out To store your data efficiently, considerations contributing to the decision in the data-storage strategy are: 

  • Data volume
  • Network connectivity
  • Power availability

For example, a device that is prone to power outages will need on-premise non-volatile storage to retrieve the critical data when power is restored or the device is manually recovered while devices that have continuous connectivity and power may store all of its data to the cloud so that on-premise storage is completely unnecessary.

2. Analyzing data

It must be analyzed to turn your data into valuable insights. However, manually processing the flood of data is not practical. So, most companies will seek support from automated IoT analytics where various open-source frameworks or platforms can be used to analyze data. 

Companies need to determine if their analytics will be performed in real-time or through batch processing of historical data. This can be determined based on the data’s nature and the purpose for that data’s use. 

The analytics approaches that may be used include: 

Distributed analytics

This approach is necessary to analyze data at scale, especially when dealing with vast historical data that is nearly impossible to be stored or processed by a single node. When time sensitivity is not relevant, distributed analytics is ideal for data batch-processing.

Real-time analytics

Companies tend to use real-time analytics when time-sensitive data is included in the data stream. In this case, batch processing will not be applicable as the generated results would be too late to be useful. 

This approach is also ideal for time series data. Instead of calculating a single average over the entire dataset like batch processing, most real-time analytics tools offer control of the time analysis window and calculate rolling metrics.

Machine learning

We can apply machine learning to either historic or real-time data. Companies can use this technology to automatically identify patterns and key variables and relationships between them to establish and refine analytics models.

Then, these models can be used for simulations, to make predictions or to produce decisions. 

Machine learning is also advantageous compared to static statistical analytics models; the models can “learn” and improve over time when new data are in.

3. Key points to bear in mind

  • Establish a detailed action plan: Managing IoT data is no cakewalk for any business. It is surely not a secondary task that can be done alongside business as usual (BAU) activities. As such, a detailed action plan will help you achieve goals and success.
  • Use case is critical: Businesses must specify concrete use cases where data can be utilized rather than going mindlessly. Companies must decide which kind of data and IoT analytics might help them understand their customers, answer their pain points and where they can be sourced. This will spare you the effort of gathering irrelevant data that is optional for analysis nor generating any new knowledge.

Final thoughts

As data plays an important role in today’s dynamic world, understanding how to handle and manage the high-volume of IoT data efficiently is critical to businesses wishing to stay competitive and lead the technology game. 

If you need a trusted partner to help you navigate the complexity of data and help your organization unveil the untapped potential of data, meet Sunbytes! Working with us, you can achieve your IoT development goals while ensuring that we have your back in handling high-volume IoT data. 






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