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Challenges of data management in the internet of things

The trajectory of data growth resulting from myriad devices — macro to micro — that capture or create data is beyond astounding. There’s more data today than yesterday; there will be more — much more — data tomorrow than today. The internet of things is the newest contributor to the massive volumes of data created daily. This surge has once again prompted the industry to evaluate data management strategies from the perspectives of scale, data gravity, integration and general security. Yesterday’s approaches to data management are no longer adequate in dealing with the volume, diversity and interconnectivity that characterize IoT. Scalable infrastructure and centralized management are required.

Infrastructure needs to scale easily and globally

Volumes of data generated by IoT are the initial shock wave with which IT organizations have to contend. Increasing data volumes have been a concern for years, while decreasing storage size and cost have made these increases manageable. But the sources, locations and speed at which IoT data is generated require rethinking the entire data lifecycle. Imagine how much data is generated and used by, say, an autonomous vehicle. Or consider how much data you generate in a day if you’ve enabled location services on your mobile device. With regard to data management, we’re once again asking the following questions:

  • Can the existing network and communications infrastructure handle these volumes?
  • Where are these data best managed — in the data center, in the cloud, at the digital edge or in all of these locations?
  • Who should have access to these data — operations, maintenance, compliance, finance, external service providers?
  • What are the retention requirements, operationally and legally, for these data?
  • What is the expected data growth over the next three years?

These questions are both rhetorical and practical, and there’s a common thread in the answers to each: the need to easily scale your IT infrastructure to support data management and processing growth for years to come.

An ever-increasing number of enterprises respond to this need by moving to the cloud, which has proven financial and operational benefits in contrast to on-premises data centers. The challenge for many enterprises, however, is the number of cloud providers with which they need to work to support the variety of applications, operations, data and geographies in which they operate. Reduced costs for capital expenditures and operating expenses can lead to greater complexity when managing a diverse infrastructure.

IoT data gravity: With volume comes value

As the volume of IoT data increases in any one location, it acquires data gravity. In other words, as data volume grows, other applications or functions find value in that data. In turn, those applications usually increase that volume even further. An instrumented drill string on an offshore platform tracks depth, speed, angle, temperature, head pressure and other operational data. That’s all useful in managing that single downhole operation.

However, that data becomes even more valuable when combined with data from hundreds of other downhole operations. By analyzing that data, operators can predict and optimize the performance of drilling operations in similar environments or locations. When combined with geomorphic data, it could lead to more efficient exploratory techniques. Equipment manufacturers also benefit from operational data, especially when it indicates failure or suboptimal performance. Such insight leads to improved product design and better preventative maintenance schedules.

In this example, large data volumes provide greater insight, which will benefit operators, manufacturers and maintenance personnel. The greater the volume of data, the greater the inherent value of that data. Your IT infrastructure just needs to capture, manage and share these data securely.

IoT requires secure integration

Much of the value of IoT comes from the interconnectivity, whether wired or wireless, of devices, processors and storage at the physical level and myriad applications and services that transform bits into value. These data acquire greater value when shared securely with legitimately interested parties. A drilling equipment manufacturer gains significant benefit from analyzing operational data shared by its customers.

However, secure integration among the components and connections that comprise your IoT environment is a challenge. How do you efficiently connect, collect, exchange and manage data while maintaining security as data moves over an expanding IoT network?

The concept of interconnection — private data exchanges between businesses — addresses this challenge. It provides a secure nexus to integrate data sources and services at the digital edge to reduce latency and optimize performance. For many IoT operations, real-time processing is a baseline requirement when operational data is generated and needs to be analyzed on the factory floor, in the field or at a busy intersection.

Data security begins with encryption strategy

In addition to secure interconnections to protect data in motion, enterprises also need to focus on the security of data at rest. The threat of cyberattacks has expanded beyond personal and financial data, and now any enterprise with significant physical operations — manufacturing, utilities, transportation, city infrastructure, chemical and petroleum, pharmaceutical, telecommunications and more — must be concerned with the security of operational and intellectual data.

A successful breach of an operational data store combined with surreptitious modification by a knowledgeable hacker could disable a critical process. Organizations that depend upon intellectual property, whether it be research or proprietary processes, need to protect that information.

Data security requires a multifaceted approach, but at the most fundamental level, a sound data encryption strategy can be your strongest defense. In the context of IoT and the widely distributed nature of operations across multiple cloud environments, a centralized approach to encryption key management is needed. This will allow you to manage the entire lifecycle of encryption keys, regardless of where the keys are being used across multiple cloud providers.

Encryption key management needs to be delivered as a service. The traditional hardware security module (HSM) approach can’t readily provide scalability in multi-cloud environments. Encryption key management as a service also provides an added level of data security by managing key separately from encrypted data: Encrypted data is useless ciphertext without access to the encryption keys.

Scalable and reliable IoT

You can more efficiently address the issues of scalability, data gravity, integration and security via a global platform that enables you to deploy, directly connect and effectively scale your IoT infrastructure. Rather than attempt to build your IoT infrastructure piece by piece, opt to work with an organization that already provides a global footprint, access to IoT and related services from best-in-class providers, and access to the top clouds and networks. An HSM-as-a-service approach simplifies provisioning and control of encryption keys and provides cloud scalability, secure key storage, encryption and tokenization services at the digital edge of IoT operations.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.

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