A recent study conducted by Oxford Economics has shed light on a critical factor that hampers the success of artificial intelligence (AI) in industrial manufacturing: data quality. The study warns that unreliable insights, operational inefficiencies, and missed opportunities are significant growth barriers for companies that underestimate the importance of data quality in AI implementation.
According to the study, midsize manufacturers are primarily focused on revenue growth, including expanding market share, product innovation, customer acquisition, and market diversification. However, the growth rate remains modest, with only 12% of respondents reporting growth over 5%. To address these challenges, AI and automation have emerged as viable solutions. Unfortunately, many manufacturers find themselves in a predicament, desiring to benefit from AI but lacking the necessary foundation to support it.
Oxford Economics reveals that approximately 80% of midsize manufacturers acknowledge the importance of integrating data for business model creation, process automation, and data accuracy and security. However, a staggering 46% of these companies have yet to adopt data management and analytics software. Consequently, they face difficulties in implementing key growth strategies such as automating low-value tasks, adjusting priorities based on new information, scaling processes and systems, and entering new markets.
To establish a solid foundation for AI, manufacturers must prioritize several fundamental strategies. Firstly, they should focus on data collection and integration by implementing internet of things (IoT) sensors and other automated methods to capture real-time data from machinery, operations, and customer interactions. This approach ensures data consistency, accuracy, and minimizes the risk of human error. Integrating data from multiple sources, including ERP and CRM software, into centralized data lakes or warehouses and regularly cleaning it to remove inaccuracies, duplicates, and inconsistencies is also crucial. This enables a holistic view of operations, facilitating the application of AI-driven insights across the business.
Secondly, manufacturers need to establish clear data governance policies, defining ownership of data and establishing rules around access, sharing, and usage. This step is critical for maintaining data quality and ensuring compliance with privacy regulations. By preventing issues arising from inconsistent data usage, such as inaccurate reporting or operational inefficiencies, comprehensive data governance policies contribute to overall data quality management.
Standardizing data formats and ensuring data consistency are essential for effective AI-driven analytics. Manufacturers should implement validation processes that consistently check data accuracy and integrity, minimizing the risk of conflicting data and maximizing the effectiveness of AI insights.
As data volumes continue to grow, manufacturers must invest in scalable infrastructure. Cloud solutions provide a flexible platform to manage large volumes of data without overwhelming internal IT resources. Leading cloud platforms also offer easy access to cutting-edge AI and machine learning algorithms. Additionally, edge computing, which processes data closer to its source, reduces latency and optimizes bandwidth usage, supporting real-time AI applications like predictive maintenance.
Data security is of paramount importance, and strong cybersecurity protocols must be in place to protect sensitive data. Role-based access controls should also be implemented to ensure that only authorized personnel can access specific data, safeguarding the business internally and externally.
Lastly, manufacturers should establish feedback loops to continuously assess and improve their data management strategies. Defining key performance indicators (KPIs) and monitoring performance help identify areas for enhancement and high-impact AI use cases, aligning them with business goals.
The findings of Oxford Economics’ study emphasize that midsize manufacturers prioritizing data management are well-positioned to unlock the full potential of AI. By investing in clean, organized data and scalable infrastructure, these manufacturers can translate insights into action, outpace competitors, and shape a sustainable future for their businesses.