Between 60% and 73% of data gathered within an enterprise gets left unused, according to a Forrester market research study. This industrial data is then left to collect metaphorical dust before it’s inevitably deleted to make room for new data, continuing an inefficient cycle that, at its heart, disregards valuable information.
At least, that’s the cycle many enterprises kept their data on before this newest global transformation towards more digital means.
Now, the digital era is ramping up even faster due to the pandemic’s influence, and enterprises look to embrace artificial intelligence (AI) as their next digitized solution. As we propel forward into this fourth industrial revolution, we want to emphasize how AI can make use of industrial data through advanced data collection, integration and other AI applications.
Zion Market Research estimates a 14.9% hike in value for the industry 4.0 market between 2018 and 2024. In 2017, these industry 4.0 digital services were valued at $66.1 billion worldwide, and it’s already projected to be at $155.3 billion by 2024. This forecast demonstrates the importance of digital transformation initiatives across all industries and spotlights just how central robust industrial data is to this next digital phase.
Data is the lifeblood of going digital. It’s been a valuable business asset for quite some time now, however. Data-driven analytics and intelligence have led to the evolution of each industry thus far. Without industrial data, we would not have seen the advances we have in technology and artificial intelligence to date.
In essence, everything starts and ends with data. Enterprises set to thrive in this new digital environment are making the transition to a more information-centric and consumer-centric modern organization.
How does this transition look? It starts with the orchestration and implementation of digital machines that allow for connected technology solutions. Organizations must fundamentally alter how they gather, access and analyze their data. With solutions through connected technology, you can create value from AI applications and better manage industrial data in the process.
Consumer and industrial sectors alike will generate enormous wealth through the integration of artificial intelligence, secure broadband, improved communications and progress in information systems. It is the convergence of digital and physical equipment through industrial IoT solutions that will drive efficiency forward and add value along the supply chain.
Smart manufacturing can only go as far as the technology within its infrastructure. Fortunately, the Industrial Internet of Things (IIoT) can pair with artificial intelligence to digitally transform an industrial enterprise. This AIoT technology can change how entire industries operate. Remember that industrial data we mentioned that’s collecting dust? Integrating AIoT into an enterprise will put that data to good use to create better user experiences, drive new insights, improve safety and sustainability, measures and much more.
Industrial IoT is also not some distant tech on the horizon – this type of virtual reality is already happening on a tangible scale. Even as new as this industrial revolution is, AIoT is already making a positive impact on businesses, safety, health and people.
Without the AI part of the Internet of Things, data collected through connected devices that continue to increase exponentially is left floundering. Enterprises aren’t able to manually sort, organize and use the data at their fingertips.
The problem is likely not that enterprises leave 60% to 73% of their data unused. It’s that data volume does not necessarily equate to data value. The appropriate industrial data solutions compound this data, filter by what’s valuable, and can truly make use of the astronomical volume of data coming at each industry.
After all, the world will have close to 37 billion connected devices generating over 70 zettabytes of data by 2025. Long-term storage strategies can finally evolve with the help of AIoT.
In just the last half-decade, enterprises have been progressively scaling and investing in AI technology. Democratizing access to these AI tools has opened up the ability to accelerate artificial intelligence adoption across a vast number of industries.
As recently as 2019, however, 76% of C-suite executives struggle to scale AI to meet their enterprise’s needs, according to an Accenture market research survey. Even with those stats, 84% of those executives understand that artificial intelligence is a necessity for their enterprise to succeed in their growth objectives. That builds into a fear that executives understand failing to scale their AI initiatives could end in enterprises going out of business entirely.
This is where AIoT tech shines. Instead of merely collecting data, an enterprise can shift its focus towards data mobility, integration and accessibility. Previously undiscovered or unoptimized sets of data can be brought to light for their hidden value.
The only solution in surviving this digital landscape as we rocket forward is to adopt enterprise-wide, industrial AI strategies to compete with the enterprises already adopted those strategies. Cloud-ready and industrial data value-intensive, the industrial sector can take the right step forward to come out on top.
How is data used in industries?
Data is used across industries to recognize patterns and make decisions. How industrial data gets collected, stored and analyzed to recognize those patterns and make those decisions evolves with each industrial revolution.
What are the two types of data processing?
There are two central types of data processing: analog and digital. Analog data processing attempts to use continuous data to identify everything being measured. Digital data processing grabs samples to encode the data being measured, then reproduces the data as identically as possible.
How does industrial data processing work?
Industrial data processing is the process of collecting, storing and analyzing data, most often on an enterprise-wide scale. With each technological and digital advancement, the way we collect, store and analyze data evolves to bring us more defined insights and better experiences across each enterprise’s value chain.
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