To harness the data generated by IoT and keep it secure, organizations need to extend their data management practices to the edge.
IDC predicts that the amount of data generated at the edge will experience a compound annual growth rate of 34% each year through 2027. This growth rate is faster than the growth rate of data generated at the core or other endpoints. To protect and use all your data, security and data management practices must extend beyond your core enterprise systems and business applications to where the data is generated.
Strong and secure data management practices enable organizations to successfully ingest, store, organize, retain, and protect their data assets. Responsible management can lead to more data insights and better decisions, and can also power AI and advanced analytics systems.
IoT Data Management Challenges
According to experts, data leaders face many challenges with IoT data management, including identifying all data sources, integrating the data, ensuring data quality, and applying appropriate controls to meet data security needs.
IoT environments also present unique challenges, including the scale and volume of data generated by IoT, which is huge, with IoT devices continually recording and adding data points.
“We’re seeing an overwhelming influx of new IoT applications, with data volumes and levels of granularity far exceeding what IT professionals had anticipated,” said Nicholas Napp, IEEE Senior Member and co-founder of Xmark Labs.
Nicholas Knapp
Data and technology leaders are struggling to understand how their organizations can use various data points to create business benefits, and they are also struggling to create and implement the infrastructure required to store and use all that IoT data.
Another major challenge in IoT data management arises from the distributed nature of IoT, Napp said.
“Let’s go back 10 or 15 years ago when corporate IT really understood the ins and outs of their networks and their systems. Now virtually anyone can have an IoT device,” he said.
IoT Device Management
Data and IT leaders are faced with the increased task of cataloging all the IoT devices being deployed in their environments, understanding the data being generated, and devising strategies to manage the data sets.
Operations teams and non-technical employees who frequently deploy IoT devices are typically not as familiar with security and privacy requirements as data leaders. Knapp said they don’t build in the right security and privacy controls from the start. He gave the example of how easy it is to deploy smart cameras: Facilities teams typically deploy smart cameras but don’t know how to secure the data that’s being sent from the cameras.
The number of [IoT device] Protocols are one of the challenges with IoT data that make IoT data management unique and more difficult. Tancred Taylor, Senior IoT Analyst, ABI Research
“Everything has vulnerabilities, and if you’re unfamiliar with the technology, it’s a potential risk from a data management standpoint,” Knapp said. As a result of that unfamiliarity, data teams often have to go back and identify and implement necessary controls after deployment.
“The number of protocols is one of the challenges surrounding IoT data that makes IoT data management unique and more challenging,” said Tankred Taylor, senior IoT analyst at ABI Research. The variety of protocols and the need to ingest data into a single data engine “means that you need a lot of drivers and data conversion tools to handle all the protocols in the same way.” [eventually] We speak the same language.”
Taylor said IoT devices generate unstructured data that doesn’t make much sense on its own — for example, an IoT device might record and transmit data about vibration levels generated by manufacturing equipment at set times.
“That data doesn’t make sense on its own, it needs to be analysed in depth or combined with enriched data so that companies know what to do with it,” he said.
A final challenge experts cited was inventorying all the data sets being generated by IoT devices. “It’s a huge task to understand which pieces of data are important and which aren’t,” said Sarb Sembhi, CTO of Virtually Informed and a member of ISACA’s Emerging Trends Working Group.
AI, machine learning, and other cutting-edge technologies can deeply analyze IoT data to unlock its potential and create new business value.
IoT Data Management Best Practices
Experts recommend that organizations have robust data management capabilities to meet the challenges of IoT.
Develop a strategy for how data will drive organizational goals. Develop policies for naming and cataloging files. Manage metadata. Protect data according to organizational and regulatory requirements. Develop data quality policies and practices. Implement policies and practices for data integration, preservation, and retention. Design systems to deliver the right data to the right users (human workers or applications) at the right time.
Once data management capabilities are in place, data leaders will need to layer in additional policies and practices to address the specific needs of IoT data.
Establishing data security
Understand the security capabilities and limitations of various IoT devices, protocols, and connecting networks. Use that knowledge to develop additional security policies and practices to ensure that the security of data in your IoT environment is consistent with the security established for data in your core IT environment.
“Understand every part of the IoT stack: what’s being communicated, how it’s being communicated, and the vulnerabilities at each step from the IoT device to where the data is ultimately stored,” Napp says. “You need to look at every step and make sure someone really understands what’s going on at each step.”
Analyze the data
Identify what IoT data serves what purposes, what data is sufficient on its own for those purposes, and what data needs to be augmented with additional data to be useful, Taylor said.
Process Data
Identify what data requires edge processing to meet use case requirements, such as real-time monitoring and alerting, and what data can be processed and stored by centralized systems, whether in the cloud or an on-premise data center.
Continually evaluate the data
Rethink and reevaluate which IoT data stays at the edge and which gets sent to central data systems. Advances will increase the computing power of edge devices, making TinyML more mainstream and enabling more analytics closer to IoT devices, Taylor says. AI, especially generative AI, will help data leaders understand and use data.
Integrate your data
Taylor said IoT data can be combined with other organizational data so that it can all be used and distributed to the right people at the right time.
Mary K. Pratt is an award-winning freelance journalist specializing in enterprise IT and cybersecurity management.
