IoT devices generate zettabytes of data, and IT teams need a way to manage all of it. Finding one data point in a massive pile without a labeling or categorization system is nearly impossible. That's where metadata comes in.
Metadata is a way to label and describe data systematically so anyone -- or anything -- can find it. Metadata enables applications, databases, systems and resources to organize and catalog data so the network can find and use it efficiently. Simply put, metadata is data about data.
What's the difference between data and metadata?
Generating and storing data is easy. It's one of the most basic functions of any device, especially any IoT device. IoT-generated data is a set of information that can be referred to, communicated and analyzed later, whether it's how many daily steps a person takes or the number of records updated in a minute by a software application.
Metadata is in-depth information about that data, such as the time it was generated, the system or device that generated it, its format and so on. Ideally, the metadata is a standard set of information about the data that makes it easier to understand or categorize by various systems, applications and resources.
Businesses that use IoT must be ready to address their IoT data in ways that meet various guidelines, regulations and data management best practices.
Metadata standardizes the data catalog
Metadata is an essential part of ensuring that companies get the most value from their IoT devices and data, as well as any system or resource that uses that data. Developing a standard metadata catalog for the business can help curate the inventory of data available from IoT devices. It also simplifies mapping the data to current infrastructures, systems and resources because it offers an overall view of what data is available and what uses it. The catalog also identifies whether the data is stored on the IoT device itself and which additional tools or software applications need to extract, transmit, store and analyze the data.
Standard metadata also simplifies organizations' ability to demonstrate compliance in heavily regulated or governed industries, which is typically hard to do with IoT-generated data because the technology evolves so quickly. Companies can generate a report of their IoT systems that identifies the relevant metadata for audit compliance.
Metadata solves the IoT interoperability problem
The metadata catalog can also help with one of IoT's biggest challenges: interoperability. IoT devices can connect to and communicate with many other devices and systems. Improving interoperability means more systems can use IoT devices and the data they generate. Metadata solves the interoperability challenge by quickly helping devices and systems that want to interact with IoT devices identify them and connect using the right communication protocol. Metadata also lets other devices know what data the IoT device can exchange. This type of information makes connecting to an IoT device more efficient, and it reduces lag time and other network-wide delays.
Metadata integrates legacy hardware and software
IoT technology and use cases evolve rapidly, and new products are introduced often. But there are also older-generation IoT devices still in use. Metadata can help connect legacy IoT devices, applications or systems that a company might still have in its fleet or network. It helps identify the legacy systems earlier in workflows and network connections so the data can be shifted to a more relevant destination. Metadata can also indicate when an organization needs an additional system or tool to use the legacy system or its data. Companies can also use metadata to identify systems or IoT devices that need to be upgraded or replaced.
The challenges of metadata in IoT
There are three main challenges with metadata that companies must deal with.
First, the evolving nature of IoT makes it difficult to keep metadata updated. Companies should empower IT administrators to start building knowledge, skills and expertise in IoT data, IoT-related standards for APIs and connectivity, and metadata management. This knowledge can help organizations choose IoT data tools and resources that align with their infrastructure, standards and fleet usage.
Second, metadata faces interoperability challenges with legacy systems as IoT technology evolves further away from old technologies. It's possible that companies reach a point where no manner of IoT metadata is backward-compatible with some of their systems. Companies will eventually need to replace the legacy technology or introduce new technologies to keep using the legacy systems. Each new app or connection requires additional metadata configuration, which creates even more work for the IT team.
Finally, the volume of data that IoT devices generates may become too much to organize, even for metadata. Logging metadata manually becomes impossible, and even adding a data management tool might not be enough. Data in IoT networks moves around, and maintaining the metadata might be elusive. Data monitoring systems should handle metadata tracking dynamically and accurately, especially for highly regulated industries with rigorous auditing and compliance requirements. Falling behind with the metadata impacts data quality and trustworthiness, which introduces risk factors for companies that depend on metadata for the accurate categorization of data for compliance purposes.