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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-toend 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 onpremises 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
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