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The top five IIoT challenges facing industrial organizations

The physical world is being digitized. There has been an explosion of smart devices that are in constant communication with one another and churning out large volumes of data. This data is changing the way businesses are run today. The basis of the industrial internet of things, the real-time dynamic of data analytics, is creating both new opportunities and challenges for business leaders. In one report, half of the executives surveyed across industrial and healthcare sectors said they lack the talent required to consolidate and interpret the massive volume of disparate data that exists across their facilities. Yet within the next year, 72% of those companies fear they will lose market share if they are unable to implement their big data strategy. So what’s holding them back? Below are the top five challenges currently facing organizations in the age of IIoT.

1. Asset-level visibility

Improved capacity is one of the benefits of state-of-the-art information systems. To achieve production targets, operators need to be able to monitor assets in real time and ensure those assets are performing at an optimal level. Operators also need increased visibility and better insights on the health of the machine so they can detect anomalies and fix issues before they occur. Asset performance management can provide operators with answers to critical questions, including how often equipment fails so it can be prioritized, how equipment should be maintained and how unexpected failures and downtime can be avoided.

2. Technology integration

Traditionally, management of industrial technology has been split between information technology (IT) and operational technology (OT). IT works from top down, deploying and maintaining data-driven infrastructure, whereas OT is built from ground up, starting with equipment and assets, and moving up to monitoring and industrial control systems. With smarter machines and the pervasiveness of IIoT, the worlds of IT and OT have converged. IT and OT, developed separately with independent systems architectures, need to securely integrate without data loss or the introduction of vulnerabilities.

3. Aging workforce

According to the Bureau of Labor Statistics, by 2024 the median age of U.S. workers is expected to be 42.4 years old, so it comes as no surprise that this aging workforce will impact a number of industries. Retirement of experienced workers is expected to create a skills gap, and while younger generations will bring new skills, it is crucial that the knowledge accumulated by more senior employees is captured and made accessible to the new workforce before retirement. Organizations must prepare for this impending change, and can do so by using digital technologies to help ease the transition.

Advanced cloud computing and software technology is transforming the data management process and adapting to a younger, more digital-savvy generation. Data management and analytics technology with a simple, mobile-enable interface dramatically increases productivity across the organization and reduces the costs required for manual data organization and review. Further, the ability to better predict maintenance issues and eliminate unexpected equipment issues could save industries billions of dollars per year.

4. Data islands

Keeping up with a flood of information is difficult for any organization. Most companies struggle with data deluge driven by lower-cost storage, sensing and communications technologies, and few have figured out how to properly leverage data. Big data that is neither structured nor contextualized is difficult to store and analyze in its entirety through traditional computing approaches in a cost-effective way — and can lead to data islands.

Data islands are either a byproduct of operational decisions being made without the context of a larger data strategy, or by layering legacy systems with newer technologies without a data governance system in place. Data then gets siloed, and this fragmentation presents complex technical and organizational challenges. When the data is scattered throughout the plant and the enterprise, for example, integrating and analyzing it manually becomes resource-intensive and time-consuming. By the time data is actually organized, its value may have already been lost.

5. Cybersecurity

As billions of assets get smarter and are networked to store information on the cloud, they become exposed to digital privacy risks. Cyberattacks pose a range of threats — from personal devices to corporate IT systems — making both individuals and institutions vulnerable to financial and operational damage. There is growing awareness among business leaders to mitigate these risks. Vendors are now deploying solutions to prevent cyber events, but as industrial organizations continue to invest in digital technologies, security capability must be considered in the selection criteria.

IIoT challenges facing industrial companies today may seem overwhelming. These challenges, however, offer game-changing business opportunities to improve productivity and growth for those companies willing to embrace a systematic approach of applying contemporary intelligent data management and analytics systems.

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