Why trusted data is crucial to AI innovation in manufacturing
In the dynamic landscape of modern manufacturing, AI has emerged as a transformative force. It’s reshaping the industry, offering unprecedented efficiency and innovation. As we navigate the fourth and fifth industrial revolution, AI technologies are sparking a shift in how products are designed, produced, and optimized.
Data is at the heart of this change. Armed with data from sensors, historical maintenance logs, and other contextual data, manufacturers can use AI to predict how their equipment will behave and when the equipment or a component will fail. AI can even prescribe the appropriate maintenance action at the right time, helping manufacturers to identify potential failures, optimize maintenance schedules, and reduce downtime. It can also be used to forecast product demand using historical data, trends, and external factors like weather and market conditions, generating huge value for manufacturers.
But, whilst AI promises to drive smart intelligent factories, optimize production processes, enable predictive maintenance, and pattern analysis, personalization, and many other use cases, without a robust data management strategy, the road to effective AI is an uphill battle.
Understanding the value of data
Data – as the foundation of trusted AI – can lead the way to transforming business processes. However, many manufacturing executives say they are challenged when utilizing innovations, including AI, for new use cases. According to Gartner, 80 percent of manufacturing CEOs are increasing investments in digital technologies – led by artificial intelligence (AI), Internet of Things (IoT), data, and analytics. Yet only eight percent of industrial organizations say their digital transformation initiatives are successful.
The lack of universal industrial data has been one of the major obstacles slowing the adoption of AI amongst manufacturers. But advanced technologies are only part of the digital transformation story, and manufacturers who want to get ahead on AI innovation must first understand data’s role and value. Due to the very low cost of sensors, manufacturers now have unprecedented capacity to collect, utilize, and manage massive amounts of data.
But if AI doesn’t have access to a complete set of high-quality data, it will provide questionable analysis and below-optimal results. It’s not uncommon for organizations to construct solutions with these faulty assumptions. Without this solid foundation, AI will be biased and untrusted, and more likely to fail. Simply put, many organizations fail to realize the value of AI because they rely on tools being applied to data which is faulty to begin with.
Laying solid foundations
To combat these data challenges – and fuel data-driven AI in manufacturing – businesses must develop a data strategy built on a robust data platform. Here, collaboration between manufacturing operations and IT can help foster a data-centric culture, enabling end-to-end data life cycle management focused on reliability and security. The key is to focus on data first, not complex AI systems.
Many manufacturing organizations still use legacy infrastructure and data sources on varied types of platforms – such as on-premises or public cloud. But by deploying a holistic data platform built around a modern data architecture, manufacturers can eliminate data siloes by centralizing data in a common data lake, offering the single source of truth that AI needs to flourish. This helps to ensure that AI is trained on or integrated with their own data, bound by their own networks and control, reducing the risk of data passing outside of their organization and ensures that the outputs of AI are contextual and accurate.
Realizing the potential of AI
It’s clear that AI can revolutionize manufacturing. But as with any new technology, there’s a risk of manufacturers focusing too intently on AI, without taking the necessary steps to ensure its success. Any AI implementation must be built on trusted data, underpinned by the solid foundations of a modern data architecture. Without this, organizations will fail to realize the true value of AI.
In an industry where even the slightest improvements can significantly enhance yields, those who harness the potential of AI will gain a substantial advantage, able to navigate the ever-changing manufacturing landscape. ■
By Vinod Ganesan
For a list of the sources used in this article, please contact the editor.
Vinod Ganesan is Regional Vice President – UKI at Cloudera. Cloudera believes data can make what is impossible today, possible tomorrow. It empowers people to transform data anywhere into trusted enterprise AI so they can reduce costs and risks, increase productivity, and accelerate business performance. Cloudera’s open data lakehouse enables secure data management and portable cloud-native data analytics, helping organizations manage and analyze data of all types, on any cloud, public or private. With as much data under management as the hyperscalers, Cloudera is the preferred data partner for the top companies in almost every industry.