The way we manufacture anything today is drastically different than how our grandfathers did. Machines get connected to the web and are ripe for data analytics. The SMART machines are data-driven, enabling newer endless possibilities for manufacturing. In fact, realized intelligent factories depend on data science methodologies and collaborative manufacturing systems to make operations more flexible, adaptable and optimized.
Data Science methodology is a set of techniques and processes used when solving manufacturing problems by utilizing data. This methodology has a significant impact on the practical application of data-driven products. Critical business data is analyzed with the objective of aiding enterprises better understand their business. The focus gets expanded from ‘what happened?’, ‘how often?’ and ‘where?’ to answer ‘why?’, ‘what if this trend continues?’, ‘what will happen in the future?’, and ‘what is the ideal scenario?’. Using subject-specific jargon, these questions correspond to analytical tasks widely known as statistical analysis, forecasting, predictive modeling, and optimization. 
In other words, Data Analytics is about making the most of all data using a set of tools and technologies to deliver business value. The process begins with acquiring data from multiple sources (preferably clean and possibly in large amounts) in layman’s terms, the information you can leverage. Even the smallest of data can get integrated and analyzed to reveal whether or not a manufacturer is making profits, saving money, or over-spending to improve performance. Let experienced professionals or the right data analytics tools handle the data to get real value from your production processes.
Aside from data analysts and statisticians, you can make the most out of available data by harnessing advanced AI apps, leading you to customer-centric goals and process efficiency. The AI apps work with the collected business data, bringing the numbers in the statistics to life, diagnosing, and correcting the manufacturing through informed decisions that drastically improve the production processes.
“The goal is to turn data into information, and information into insight.”
– Carly Fiorina, former executive, president, and chair of Hewlett-Packard Co.
A smart factory monitors the entire production process by linking physical and digital worlds. From building tools to individual operators in the workshop are rich data sources. It is an end-to-end connected and flexible production system, which uses a constant stream of data to provide learning from the machines. Based on the integrated data analytics, AI Apps provide real-time insights to adjust the process workflows, adapt to new needs, and optimize production. Intelligent factories thus achieve machine maintenance in advance, inventory management, and efficient production processes throughout the manufacturing network.
Data can take many forms and fulfill many purposes in an intelligent factory environment. Data makes discrete information on manufacturing environmental conditions such as humidity, temperature, and pollutants accessible. It also helps self-optimize devices, addresses numerous manufacturing problems, improves process control, and guides while responding to new requirements.
According to a recent study conducted by the World Economic Forum, nearly 75% of surveyed manufacturing executives consider advanced analytics to be critical for success. However, only a few companies capture the full value that data and analytics can unlock to help address the manufacturers’ most pressing challenges.  Establishing a strong technological backbone is a prerequisite to effectively scale data and analytics applications for manufacturing success in the post-pandemic world.
Industry 4.0 is data-driven and when its technologies are growing exponentially, the frequency of data collection will also increase. Those organizations that are under-using data or are still not AI-ready run the risk of remaining on a lower rung of the business ladder.
On a lighter note….
Although the D word is of BIG importance in the manufacturing world, we stand divided on the pronunciation. There is a valid reason behind the ‘proper’ pronunciation of the word Data. Ideally, it should be pronounced: day-taa, (not daa-taa) as data is the plural of datum (day-tum).
 Omar, Y. M., Minoufekr, M., & Plapper, P. (2019). Business analytics in manufacturing: Current trends, challenges and pathway to market leadership. Operations Research Perspectives, 6, 100127. https://doi.org/10.1016/j.orp.2019.100127