The Internet of Things (IoT) is, in the briefest way possible, any device with an off switch that has connection to the Internet. The term is newer, and focuses on newer pieces of technology including automation, machine learning, artificial intelligence, and more. It’s simply a method of machines communicating with each other, something we’ve had since the 1800s. We’ve seen the growth in technology over the years, with large leaps occurring in the 1950s.
Companies can use this IoT network to help with a variety of things, from predictive analytics and maintenance to digital twins of demographics or operations of supply chains. Using this technology provides a lot of benefits to companies, from allowing them to better monitor processes to also transforming their customer experience and ultimately saving the company money. When coupled with AI, IoT provides the foundation that companies need to quickly design, develop, and deploy new products and services.
IoT networks started with the telegraph and since then have expanded to applications that connect billions of devices to instantly send and stream data back and forth. As the networks have evolved, many companies have found that their networks are falling short with what they can do.
Managing this much data is a massive problem by itself, one that use of IoT data modernization coupled with strong governance that’s appropriate for both structured and unstructured data, can address.
But data itself is useless if the business can’t extract the insights it contains and make them actionable in real time. This requires speed and capacity in ever-increasing amounts.
Yet despite the clear and growing need for greater network performance, many companies cling to legacy data platforms and infrastructures that can’t provide the scale, data management, edge computing, and virtual/cloud deployment capabilities required to fully leverage these tools.
In some cases, it’s a lack of understanding with how IoT and digital twins operate. In others, it’s an internal issue with not having the proper team set up to manage this shift. Ultimately though, incorporating an IoT ecosystem will help companies scale in regards to new products and services, enable real-time updates, transform customer service, and more. All of which can help a company save money where needed and grow their customer base.
Companies can leverage continuous learning and strategic upskilling to become more competitive and sustain growth.
When data is the key to any IoT initiative, data modernization becomes a precursor for success. Data sources and volumes increase by the minute and many legacy platforms can barely keep up, and this lack of capacity can keep a business from fully realizing the potential of the insights their data contains.
Data platform modernization includes adding capabilities and accelerators that enable executives to base their tactical and strategic decisions on data and insights. Automation should play a key role, leveraging modern practices and processes across development, deployment, testing, and orchestration of data pipelines. Leaders should pay particular attention to DataOps and other processes that guide data ingestion, processing, preparation, and consumption.
More than just being important for strategic insights, modernizing your data estate and shifting to the cloud makes any business greener and more efficient. It can also help to streamline operations and save money. It can also drive corporate sustainability efforts by reducing carbon emissions and other waste, enabling the company to use less energy, optimize physical plant and cooling system design, and even reduce paper waste.
Any platform modernization initiative requires skilled technologists, not just on development teams but across the enterprise. Learning data science must become a process, and while it may seem overwhelming to have AI upskilling training for your team, it’s ultimately a great way to ensure your employees are leveraging new skills and can help your organization to run efficiently in the future.
The predictive ability of AI creates the basis for IoT monetization. Companies can leverage digital twins of almost anything to run simulations and test hypotheses. When coupled with hyperautomation, AI-enabled IoT devices can gather data in real time, enabling company leaders, marketing and commerce teams, and product development orgs to develop, test, and sell new products more quickly and easily.
Roles focused on revenue, growth, innovation, and operations benefit most clearly from broad adoption of IoT, but the data it generates has value of its own.
For instance, car makers can leverage data gathered from existing vehicles to improve the next year’s model, to increase safety and efficiency across their fleets, and to design autonomous vehicles. They can also sell that data to insurance companies to more accurately predict and assess risk. Government agencies can leverage it to plan infrastructure improvements. And logistics companies can use it to plan delivery routes and set expectations with their customers.
Across industries, the predictive ability combined with data gathered from IoT devices enables companies to alter their products and capabilities, and offer users, vendors, and employees a better experience from start to finish.
Network vendors must be able to provision and monitor IoT devices at massive scale in secure multi-tenant and multi-vendor environments for a range of applications. For this, Operational Support System (OSS) and Business Support System (BSS) platforms deliver the capabilities required to operate a network and sell services, and to enable secure, scalable, efficient IoT device management.
Network planners, service designers and engineering teams typically use OSS platforms to orchestrate and automate ‘back-office’ network management functions, while BSS platforms support commercial, revenue, and customer-relationship activities. Considerations for these platforms include:
Data itself is useless if the business can’t extract the insights it contains and make them actionable in real time.
The potential of sustainable IoT networks is expansive. From governments to city planners, IoT networks will continue to evolve and help create a future of interconnectedness.
Leaders are increasingly turn to IoT solutions, especially digital twins, for insight into how their populations are evolving, how to plan for those changes, and how to execute their plans.
Digital twins combine and analyze data from sensors embedded in complex physical structures, systems, and fleets of vehicles and other products, then leverage AI to create virtual replicas that mirror the performance of these things in real time. This enables users to understand how the originals are performing, with insights that can range from how individual products function under different stresses to how entire systems interoperate. They can even troubleshoot the original, suggest alternate courses of action and offer suggestions about how to update the original.
From building new vehicles to updating city layouts, digital twins and IoT play a key role in every step of the way.
In many industries, AI, IoT, and advanced data capabilities are no longer optional. If a company wants to stay in the game, they’ll need to update their IoT systems and embrace AI. However, a key part of this is ensuring that your employees are properly trained to do so.
Companies can leverage continuous learning and strategic upskilling to become more competitive and sustain growth. This lets all of their employees move beyond routine tasks and tap into their strategic and creative sides to streamline operations, help make smarter decisions, and unlock new potential. This is critical to bridging the skills gap.
Organizations that follow this approach become more innovative and agile, and are better positioned to navigate the digital age. When everyday tasks become chances for personal and professional development, employees see opportunities to enhance their skills, be more satisfied with their jobs, and engage more deeply with their work.
We launched Sand Academy to help you reskill and upskill your people in technologies that drive transformation. We focus on real-world practicality, with projects and learning designed to drive organizations forward. By focusing on strategic upskilling and continuous learning, particularly in AI, you position your company to uncover new opportunities for growth, innovation, and competitiveness.
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