Guide to IoT Analytics: 7 Key Tips

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The Internet of Things (IoT) is one of today’s most disruptive technologies. Interconnected device networks can streamline operations and make the customer experience more convenient, and their potential goes beyond these straightforward use cases.

In short, if you want to capitalize on this technology fully, you need to embrace IoT analytics.

Also see: Best IoT Platforms for Device Management

What Is IoT Analytics?

As the name implies, IoT analytics is the application of data analytics to IoT-derived data. There are more than 11.3 billion connected IoT devices in the world today, representing a vast pool of data at businesses’ disposal. IoT analytics lets enterprises turn this data into actionable insights.

Given how broad and diverse the IoT itself is, these analytics processes vary widely. Some applications are highly specific, such as using connected machine sensors to learn when to repair equipment, while others are more open, like analyzing user behavior to create personalized experiences.

Similarly, IoT analytics can vary by where you process the data. Most architectures send data from IoT endpoints to a centralized location for batch analysis, but edge analytics processes each device’s data at the source. Regardless of the specifics, these workflows help you act on the information IoT devices collect, informing more effective business practices.

Also see: Top Enterprise Networking Companies

Why Enterprises Need IoT Analytics

Implementing IoT analytics can give you more insight and control over the fine details of your operation. Predictive approaches can go a step further and help equip you for changes in the future. As industries become increasingly competitive and data richness grows, these benefits will likewise rise.

As IoT adoption soars, enterprises must do all they can to make the most of their IoT environments to stay competitive. Already, 59% of all organizations in some sectors are currently deploying IoT networks. Having connected devices alone is no longer sufficient to stay ahead. Businesses must capitalize on their IoT data, which analytics enables.

Also see: Best Cloud Networking Solutions

How to Implement IoT Analytics

Given how varied IoT analytics environments can be, there’s no one path forward for every business. However, some key considerations remain constant across use cases. Here’s how your organization can design and implement IoT analytics projects.

1) Define Your Goals and Data Scope

The first step is to determine what you want from your analytics project. Start by finding an area where IoT analytics could provide value, such as reduced operating costs or improved ROI, then outline your specific goals within that scope.

Once you know your goals, you can define your data’s scope. Determine what kind of data you need and how you can collect it through the IoT.

The sheer volume of data is the most significant barrier to effective analytics, with 62% of government CIOs naming it as their top challenge. Many organizations struggle with this because they gather and analyze more than they need. Defining the scope of your data ahead of time helps prevent this over-collection.

2) Review Regulatory Guidelines

Before you buy any hardware or start designing IoT networks, you must also consider any applicable regulations. The IoT Cybersecurity Improvement Act of 2020 and similar legislation may require IoT environments in some organizations to meet certain security standards. Even where not IoT-specific laws apply, data privacy regulations might.

If you’re only using IoT devices to collect data over your own processes, you likely don’t have to meet any specific standards. However, if your IoT analytics project requires data on customers, you should review what information you’re collecting and how you’re protecting it.

Privacy regulations may limit what types of data you can gather or what security measures you must implement. These restrictions will dictate the devices and programs you can use, so it’s important to review these considerations before going further.

3) Acquire Necessary Devices and Software

Once you have defined goals, scope, and regulatory requirements, you can acquire the hardware and software you need. The largest consideration in this step is deciding whether to build your own solution or use an existing platform.

Begin by reviewing your data scope and security requirements to determine the types of devices you need. If readily available, off-the-shelf solutions will work, it’ll likely be most cost-effective to buy rather than build. However, if you need highly specialized hardware or network setups, you may want to partner with an IoT analytics developer to create a custom solution.

Similarly, your goals can inform the software you need to analyze your IoT data and act on the results. With more than 65 key industry players, the IoT analytics market is large and diverse enough that existing platforms can meet many specific needs.

4) Standardize and Automate Where Possible

As you start to implement your IoT devices and design your analytics workflows, you should standardize the process as much as possible. There are more than 21 IoT connectivity standards, and not every device will support the same ones. While you don’t necessarily need each endpoint to use the same protocol, standardization will make it easier to manage these networks.

Similarly, standardizing data types and storage solutions where possible will help streamline analysis. When everything is consistent across the whole network, it’ll be easier to apply necessary security and updates. You’ll also gain a more complete picture of your IoT data.

Automation can help further streamline the process. Automated updates, reporting tools, network scanning, and other systems give you more time to focus on other, less repetitive and more value-adding tasks. This is particularly important for large, complex IoT networks.

5) Consolidate and Clean IoT Data

As you collect IoT data, it’s important to consolidate and clean it before analysis. While this step is easy to overlook, it’s essential for effective analytics programs.

Poor quality data costs businesses $15 million annually, but you can avoid most quality issues by cleaning data before analyzing it. Standardizing formats, removing redundant information, fixing incomplete records and similar steps ensure you don’t act on misleading data, preventing losses.

Consolidation has similar benefits. Moving your data to one place before analyzing it gives you the entire picture at once. When you or analytics programs don’t have to piece together different parts of the situation, you’ll get more reliable insights. Centralized data storage may also make it easier to secure any sensitive information.

6) Secure IoT and Analytics Environments

After implementing your IoT analytics system, you must secure it. While cybersecurity is crucial for any IT project, IoT environments are notoriously vulnerable. There were 1.5 billion attacks on IoT devices in the first half of 2021 alone, so they deserve special attention.

One of the most important steps is to segment networks to keep IoT devices separate from more sensitive systems and data. Encrypting all IoT traffic and changing default passwords is crucial, too, as many devices’ default settings aren’t conducive to strong security.

Securing IoT analytics environments is about addressing IoT security, too. You must also protect any other endpoints, databases, and cloud platforms you use to store or analyze the data. Implementing zero-trust architecture and using automated monitoring tools may be necessary.

7) Review Processes and Results

Finally, remember that IoT analytics is an ongoing process, not a one-time project. Before you start using these systems, set targets using KPIs directly tied to the goals you outlined in step one. Then, after some time using the system, record the results and compare them to these benchmarks.

IoT analytics can be complex, so you may need to adjust your system before it delivers optimal results. Given that uncertainty, it’s also important to start small before expanding these analytics workflows to larger, more mission-critical operations.

Implement an IoT Analytics Program Today

IoT analytics is the next step forward for enterprises that want to make the most of modern technology. If you follow these steps and approach these projects carefully, you could see impressive returns.

The IoT will only grow from here, creating more potential for analyzing the data it collects. Accessing and acting on these insights will be a critical part of staying competitive as more companies embrace this technology, so consider how you can benefit from this technology today.

Also see: Using Digital Twins to Push IoT

Devin Partida
Devin Partida
Devin Partida is a contributing writer for Enterprise Networking Planet who writes about business technology, cybersecurity, and innovation. Her work has been featured on Yahoo! Finance, Entrepreneur, Startups Magazine, and many other industry publications. She is also the Editor-in-Chief of ReHack.

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