Winning the Industrial AI race starts with your data
Companies aiming for AI-driven efficiency and business gains must start by assessing their data maturity. Without sufficient availability, accessibility, and quality of data, any AI projects will face difficulties and disappointing results. AI is only as strong as the data that feeds it.
A data maturity assessment measures how well digital technologies and automated workflows are embedded in operations. High-maturity organizations use advanced OT/IT systems, data platforms, and analytics supported by systems such as ERP, SCADA, MES, and EMS. Lower-maturity firms should focus on building a data foundation by improving the data infrastructure, connectivity, integrations, and upskilling employees.
Industrial AI relies heavily on data governance, e.g., the framework for how data is managed, accessed, and shared. Even large companies often struggle here. Poor governance leads to incomplete or low-quality data, making AI models unreliable or impossible to scale.
A successful path often starts with a data strategy, followed by master data harmonization and integration projects. Overlooking these steps leads to failure with AI initiatives.
Common data challenges in Industrial AI:
Availability:
AI models require data streams from various sources, including sensors, machines, systems, and more. Connectivity is often essential.
Accessibility:
Breaking down data silos and integrating across systems is crucial to enabling AI.
Quality and consistency:
Standardized collection, validation, and cleaning processes ensure reliable insights.
Security and compliance:
Organizations must align with regulations and industry standards.
A typical problem is that data is siloed and scattered across operational units, each owning its own datasets. Sharing data across units is more vital than ever. AI systems suffer if they must retrieve fragmented and incompatible data from dozens of sources.
A connectivity strategy is sometimes necessary to ensure availability. Industrial environments often include legacy systems that use multiple protocols, requiring harmonization layers.
Low data quality can make AI useless
Low data quality is one of the most critical issues. Incomplete, inconsistent, or outdated data can lead to incorrect AI outputs. Faulty sensors, low sampling rates, or missing data due to inaccessible areas can result in inaccurate predictions or even cascading errors.
Synchronization issues can make datasets unusable for AI. In industrial environments, cleaning and harmonizing data may not be enough – sometimes physical cleaning is required as well to get the data sorted out: A dirty camera lens may obstruct a computer vision solution from providing reliable results. In addition to these steps, companies must pay close attention to their data supply chain. It is a digital equivalent of the traditional manufacturing supply chain, engineered to deliver insights with precision and purpose.
For data to create real value, it must travel a defined path from the systems and assets that produce it to the users who act on it. Industrial leaders are realizing that data isn’t just a technical issue. It is a competitive differentiator. Those who master their data foundations will unlock scalable, cross-functional AI that drives real outcomes: higher efficiency, lower costs, and better decision-making.
Do you want to know more about the foundational role of data and how to make it work for AI? Learn how to make your company data and AI-driven and download Etteplan’s guidebook Create value with Industrial AI!