Analyst working with industrial data and AI

Importance of Industrial Data in AI

Industries across sectors have always had lots of data but seldom made proper use of it to improve efficiency, streamline operations, and make educated decisions. Data from equipment and devices, especially devices connected to the internet, were not being utilized sufficiently. Traditionally, industries relied mostly on just-in-time maintenance or predictive maintenance, following specifications from equipment manufacturers, and relying on third-party consultants. With the industry 4.0 revolution, data has become an important part of business strategies and efforts to complete digital transformation. IoT devices or sensors generate a lot of valuable data, which, if not put to proper use, partially defeats the very purpose for which this technology is adopted. Here we discuss how industrial data can be used efficiently with Artificial Intelligence (AI) and machine learning (ML)

AI, ML, and Industrial Data Management

Industries are also leveraging artificial intelligence and machine learning to improve performance. AI and ML systems are built on and require a regular data flow to function effectively. AI and ML algorithms have a large appetite for data and can process huge volumes of data or big data to analyze patterns related to business processes, transactions, events, people, etc. These algorithms rely not only on historical data but also learn continuously from real-time data coming in during deployment, improving the algorithm models. The insights gained from AI models can be used to improve performance, increase production, carry out maintenance as well as make informed business decisions.  

While AI needs data, it is also true that the sheer volume of data being generated in industrial settings today can be optimally used by AI and ML algorithms. But many organizations don’t have the best data management practices in place. Data residing in silos has been a problem often encountered during digitalization. In many organizations, there is a lack of data sharing not only between departments but also between different units of the same department. This fragmentation of an organization’s data, including semi-structured and unstructured data, can be an obstacle to providing good datasets for AI models. When preparing datasets for AI, it is good practice to take various factors into account.

Complete and Comprehensive 

It is vital to ensure that the dataset is complete and there are no missing pieces in it that can result in incorrect analysis. It should also be comprehensive and include all information necessary for the AI model to perform that task effectively.

Accurate and Consistent

Data captured and prepared for AI models should be consistent and correctly reflect the values for the categories they have been assigned. Furthermore, they should be from trusted data sources, and due diligence should be taken to ensure this. Incorrect data fed into AI models will only lead to incorrect results and insights.


Data fed into AI systems should be relevant so that the learning process of the algorithm is not adversely impacted. In instances where the AI model is analyzing the latest or real-time data, providing old data can lead to low-quality output. But in instances where both historical and real-time data are required, all relevant data should be provided.


Quantity of data is important, but in some cases, the quality of data is more important if not equally important. While many AI models can efficiently analyze all kinds of structured and unstructured data, some models may not. Providing quality data to the AI model can speed up the analysis and help it provide valuable and insightful results.