FEATURE


Automation & AI


IS YOUR DATA AI READY?


IS YOUR DATA AI READY?


Raw data isn’t enough – quality, context and structure are key to unlocking AI’s potential, says Nicholas Lea- Trengrous, Head of Business Intelligence at Columbus UK. He says lack of quality is a barrier to adoption


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oday’s manufacturing industry is being revolutionised by Industry 4.0. Essential to its success is the use 


 are only 36% of UK manufacturers currently utilising AI in their operations?


AI is not a plug-and-play add-on. It needs    hygiene to the end goal of actionable AI- enabled business insights. AI-ready data is crucial to successful AI implementation. Gartner predicts that by        structured and processed.  causing missed signals or false alarms. To   control. Building this foundation is essential  a roadmap to help manufacturers make it happen.


Step 1: Data cleaning – making data simple for AI to interpret The multiple sources of data in the manufacturing industry can cause  and understand the data. Ensuring data is   


18 June 2025 | Automation


and batch numbers can help manufacturers   anomalies before data is fed into AI platforms. Step 2: Management & Security – ensuring your data stays your data  has been the most cyberattacked industry and  threat of cyberattacks is vast. Manufacturers need to ensure sensitive data is securely managed by utilising role-based access control and encryption.


“Manufacturers are data rich, but those that will benefit


greatly from it and the powers of Industry 4.0 and AI, will be those that look aſter it”


     Step 3: Break down data silos – unify workers and systems


 data use in manufacturing is the siloing      


   enabling consistency across the business.


    analytics and machine learning. Step 4: Time for a systems upgrade –  actionable insights


  data can be stored and processed. Many  legacy databases and nightly batch processes   organisations state legacy technology as a key    handling large datasets and enable high-stake manufacturing environments such as supply   Step 5: Don’t get caught in a data swamp   centralised system that can store large  These are great for encapsulating sensor readings or machine logs for deeper analysis    data lakehouse. A data architect that combines the  governance and schema management of a   This means everyone from data scientists to


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