The adoption of Internet of Things (IoT) devices continues rapidly. When you look at what businesses gain from their IoT implementations, it's easy to see why.

IoT enhances an organization's operational efficiency by monitoring or automating key business processes. Maintenance, manufacturing, and supply chain have all been revolutionized by IoT. The wealth of data generated by IoT devices bolster business intelligence and data analytics. And that leads to a better understanding of the customer experience.

For these reasons and others, businesses investing in IoT are gaining a competitive advantage. However, this only comes from properly managing IoT devices’ data. For that, you need robust data integration practices. But since IoT is an emerging field, there are some roadblocks along the way.

Five key things to know about IoT data integration:

  • The Internet of Things (IoT) is changing how business is done, bringing new efficiencies and a lot more data.
  • Key insights into business operations and customer behavior can be found within the data generated by IoT devices.
  • However, collecting all that IoT data can be fraught with roadblocks and challenges.
  • A robust data integration strategy is needed to collect and analyze IoT data.
  • As you build up your strategy, there are ways to overcome IoT data integration challenges. This includes using a modern integration platform.

In this article, we'll take a look at the challenges companies face with IoT data integration, along with some practical solutions.

Table of Contents

What is IoT?

The Internet of Things is a system of connected devices that capture data and then transfer it over a network, all without human intervention.

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"Things" refers to the purpose-built nature of IoT devices. IoT data may come from sensors or cameras attached to equipment, embedded systems, gauges, scales, or any other kind of smart object. 

Businesses use IoT to collect more data about their operations. This data collection provides greater insight into efficiency, customer behaviors and expectations, and the decision-making process. IoT devices are also increasingly used to automate business functions. 

Why is Data Integration Important for IoT?

Data integration is required for companies to get the most value from their IoT projects. There is a wealth of data available today from IoT solutions that would have been impractical (or impossible) to collect before. That data is only helpful if businesses can centralize and analyze it.

IoT data integration also helps improve the overall quality of an organization's data. Without integration, companies lose the ability to identify trends and potential issues. Integration is necessary to establish a "single source of truth" in a big data environment. Without integration, data risks being lost in silos and only available to those who know where to find it.

When all of an organization's information is centralized and subject to rich data analysis, company leadership gets a complete picture of operational health and efficiency. As this information becomes more transparent, leadership can identify bottlenecks and inefficiencies.

IoT data integration platforms help organizations improve corporate processes and foster a data-centric environment. Armed with solid data analytics, company leadership can better see where they are and where they're headed.

Use Cases for IoT Data Integration

Companies can implement IoT devices to modernize a wide range of business processes. Some of the more common uses cases include:

Predictive Maintenance

With the use of IoT sensors and cameras, maintenance staff gain insight into the state of critical business equipment. IoT systems can detect equipment's operational capacity. Combined with data like last date of repair and preventative maintenance timelines, IoT performs a predictive maintenance analysis.

Proper alert messaging relies on data integration here; it's how alert messages will be delivered to maintenance staff.

Supply Chain Tracking

IoT systems play a big part in the modern supply chain. They can be used to track the location of assets in shipment, track the temperatures of shipping containers, and provide tracking updates. 

IoT can also be used for smart inventory management. Asset checkouts and check-ins can potentially be automated with the right IoT systems.

This data generated by smart supply chain devices is most useful when it's integrated into an ERP (enterprise resource planning) or inventory management system.

Remote Healthcare Monitoring

Patients with medical conditions that require consistent monitoring can benefit from IoT devices. Smartwatches and other wearable devices can track heart rate, blood pressure, and other vitals and report back to physicians and other providers. 

In these cases, a data integration strategy is needed to get the remote data into electronic medical records systems and other healthcare apps.

Security Alerts

IoT cameras and sensors work together to monitor physical security around the clock. Motion detection, combined with artificial intelligence machine learning, can alert staff to potential security breaches in real time. But that's only in cases where a good data integration strategy is in place, and alerts are delivered to appropriate staff members. 

General IoT Challenges

Regardless of the type of business, most organizations will face a general set of challenges when implementing an IoT system.

The main challenge is primarily the massive amount of data generated by a large number of IoT devices. Most IoT projects feature a wide range of devices, each with its own APIs (application programming interfaces) for sharing data. It can be challenging to collect the constant stream of information running across the network and make sense of it all.

Imagine a manufacturing operation. There could be dozens of IoT sensors on a shop floor, capturing data at every step of the production process. Each may produce data sets that don't match the others. Or they may produce similar data but label it differently. Without a data unification and integration strategy, all of this information may live in its silo.

Companies will also run into general scalability issues in their IoT implementations. With the large amounts of data comes an increase in network traffic and database utilization. More bandwidth and storage space may be needed to capture the wealth of new information that's making its way into the flow of business data.

Take healthcare organizations for example. Today's advanced medical equipment can produce more valuable data than ever before. If a provider isn't prepared for this influx of data, it may never be integrated into electronic patient records. 

As IoT technologies come from multiple vendors, you can expect compatibility issues. Differing standards and communication protocols complicate data centralization. The information’s quality and value could be questioned without a data cleansing process.

Conventional data integration tends to revolve around batch processing. IoT devices like intelligent sensors send out data in constant streams. A company's pre-existing integration strategy may not be compatible with stream messaging. A low latency tolerance is often seen in legacy integration systems. As such, experts agree that traditional batch processing is quickly becoming a roadblock in modern data integration.

A rising number of endpoints complicates things further. Traditional integration platforms may only support a fixed number of endpoints. At the same time, many newer IoT devices use communication protocols that are unfamiliar to legacy integration platforms. 

Solutions for Overcoming IoT Data Integration Challenges

There are numerous benefits to adopting an IoT platform now. The hurdles associated with data integration do exist, but that shouldn't be a deal-breaker. There's too much to be gained from the operational efficiency and robust data analytics of the IoT revolution.

Businesses have several ways to overcome IoT data integration challenges, such as:

Plan a Modern Data Integration Strategy

Successful data integration starts with having a robust strategy. Begin by laying out the integration requirements in your company's ecosystem. 

What are the data sources you currently have? Which systems and equipment do you want to collect data from? Map out all the touchpoints in your company's data pipeline and start to plan how they can all be integrated into a centralized location. Once you have a solid understanding of what you need, then you can plan for the resources you need to make the strategy a reality.

Set Data Quality Standards

An important component of data management is defining quality standards. Put together a team that can deliver documented data quality requirements. This includes data formats, structures, and delivery methods that easily integrate into your overall strategy.

Replace Legacy Systems

Outdated systems are probably the biggest hindrance to an IoT implementation. Legacy integration platforms and methods, such as batch processing, won't hold up to IoT's data integrity and security requirements. 

The overall goal should be for your company to undergo a digital transformation. Become a data-centric company where vital information can be collected from every important business process. 

While an investment is required for replacement systems, hanging on to legacy hardware and software is also costly. A lot of valuable time and resources go into keeping legacy systems alive. These are resources better spent on modernizing your company.

Research Communication Protocols

There are several ways IoT devices share their data and communicate with other IoT applications on the network. You'll either need to choose one standard or plan to accommodate multiple protocols.

Whatever you end up choosing should align with your data integration strategy and quality standards. You also need to be aware of any network requirements. This could mean upgrading your corporate LAN, WAN, or even VPN for remote connectivity. 

A smaller consideration is the data formats that IoT devices use to share data. Some will be able to send email or SMS alerts directly, some will be able to talk to a SQL database, while others simply write to a log server. Again, make sure that the devices you choose—and the output they produce—are in line with your strategy and data quality requirements.

Learn About APIs

The most common way for getting data in and out of IoT systems is via a device's APIs. While some technical knowledge and coding skill helps, working with modern APIs is often very easy. A simple request is often all that's needed to send or receive data with an API. 

When your data quality standards are in place, it's often very easy to receive data via API and insert it into a relational database or the data warehouse of an integration platform. Other IoT devices may be able to access each other's APIs, too. Just do some research ahead of time to ensure you know what you'll need to work with API data.

Use a Modern Data Integration Platform

A modern data integration platform can solve many IoT integration challenges. Modern IPaaS (Internet Platform as a Service) solutions allow you to get up and running immediately without investing in hardware or a lengthy implementation cycle.

Look for solutions that support modern data integration practices, such as data virtualization and edge computing. These trends emerged from large enterprises needing to handle massive amounts of data. Today, even smaller and medium-sized organizations could have enterprise-level quantities of data if they've implemented IoT.

Many of the components of a large-scale IoT implementation natively work with modern data integration solutions. This takes a lot of the work off your IT team and other internal resources. You'll be able to specify your quality requirements and centralize the collection of IoT data. Can Help With IoT Data Integration Challenges

It seems clear that IoT is not the future—it's here now. Organizations of all sizes gain operational efficiency and better data insights when they embrace IoT. However, a robust data integration strategy needs to be in place to ensure your company reaps the benefits. No matter which of the many IoT implementation challenges you're facing, a strong data integration platform will be the first step to success.

Now is the time to look at an IoT implementation if you haven't already. Your business competitors likely have solutions already in place. They may be reaping the benefits of business process automation and rich data analytics.

To get started, you need a modern data integration platform. is a user-friendly and feature-rich platform for real-time data integration. Our IPaaS product has been designed from the ground up to handle the challenges of modern data integration.

If you're ready to see for yourself how can help with all your data integration needs, sign up today for your free 14-day ETL Trial. Or alternatively, schedule your demo and one of our specialists will walk you through how to get the most from your trial.