By 2026, it is anticipated that the amount of fresh data would have more than doubled. The industrial and manufacturing sector is one of the few where the effects of big data are more obvious. In fact, more than any other sector, manufacturers anticipate a growth in data quantities.
A significant difficulty is the sheer amount of data: Industrial data is only going to increase in size and importance, and if organizations can’t keep up, operations and decision-making will get out of hand. Simply said, the time of big data is over, and now it is the age of smart data.
What exactly is the issue with big data?
Today’s manufacturers have more data analytics tools and digital technologies available to them than ever before, whether it be sensors that can identify recurrent failure patterns, analytical insights that enhance sustainability, or models that eliminate bottlenecks and optimize process design across distributed factories.
For instance, manufacturing procedures in high-temperature, chemical, or other delicate settings, such as a gas turbine, food preparation, or metal melting, can use virtual sensors that extrapolate information from other sources. Virtual sensors can offer data that would otherwise be unavailable or unreliable by using contextual information from other steps in the process, whereas traditional sensors might malfunction or damage performance in those circumstances.
Why aren’t manufacturers getting more value out of industrial data with all the use cases there are? The answer is that a lot of firms are mired in a big data mindset, straining to handle enormous amounts of data rather than gathering useful, practical insights from it.
Every one of the expanding number of data sources, digital platforms, and analytics tools has its own access restrictions, user rights, and data formats. Manufacturers miss out on possibilities to inform, develop, and optimize production processes when they are constrained by data silos and disconnected information because they are unable to get a full picture of their business.
How can you utilize your industrial data to its fullest?
Manufacturers require concise, contextualized data that offers meaningful information, usable insights, and newfound value as the world enters the Fourth Industrial Revolution (also known as Industry 4.0).
To create a smart data strategy that can spur advancements, aid in better decision-making, and create value throughout the full manufacturing lifecycle, take into account the measures listed below.
• The first step is always the hardest. More than a third of manufacturing organizations are concentrating on predictive analytics for improved forecasting and planning, while close to half of them prioritize connectivity and data visualization to provide operational transparency. If your organization hasn’t yet begun working on data analytics initiatives, that ought to serve as motivation. Whether you’re working to test models, monitor assets, improve data quality, or optimize process design, start with simple data analytics initiatives that you can develop, improve, and extend as your organization matures. No matter where you are in your data journey, making investments now will pay off later.
• It’s crucial to have a solid data foundation. Smart data necessitates that your organization has the appropriate technical tools and infrastructure in place. Effective data management methods support a work environment and culture that value and fully utilize data across the organization. A dedicated repository, or “data historian,” is an essential element that gathers, stores, and disseminates data from many sources. Given the volume and complexity of data generated by industrial facilities, data historian systems have grown in importance. Your data projects will not only get off the ground but also maintain support over time if you have a solid data foundation.
• Data quality can make or break findings. The quality of your data insights depends on the data used to create them. Take a sensor with a floating ground as an example: Teams may base judgements on faulty data if the sensor is providing inaccurate and inconsistent data points, and by the time they discover flaws, it is too late to change anything. Manufacturers must constantly monitor and improve data quality practices as AI, machine learning, and industrial IoT solutions proliferate. In example, you may (and should) analyze prior scenarios and track data over time to enable teams to automatically assess sensor quality and make changes as needed.
• Context is more crucial than ever. You can have all the data you want, but without the proper context to connect it all and draw useful conclusions, it won’t be very useful. In advance, think about all the details required to connect each data application. A high amount of context is necessary for even one sensor: You require information about the specific sensor, the asset it is monitoring, and its previous performance, as well as information on the location and conditions of the facility and how the data will be used in a given use case. Contextualizing data from many sources enables you to improve usability and productivity for both ongoing projects and potential future use cases.
• Use the appropriate resources and your imagination. Lack of knowledge about what is possible is the biggest barrier to smart data. IT domain experts in manufacturing have a special set of abilities and knowledge, but even the most technologically savvy companies might struggle if their knowledge isn’t disseminated throughout the company. An industrial data scientist can help with that. This relatively new position integrates expertise in the relevant topic with knowledge of toolchains, algorithms, and other data-intensive procedures. Industrial data scientists are in the ideal position to identify use cases, execute end-to-end deployments, and imagine new features and capabilities. Work with external partners, colleges, or businesses if it is not practical to create your own data team.
Huge data by itself is not always equivalent to huge value. Manufacturers who collect data without a defined strategy or practical use cases run the danger of losing out to rivals and slipping behind the curve. Your organization can unleash the full power and potential of its industrial data by adopting a smarter data-processing strategy.
It is obvious that we are entering the era of smart data, and success will depend on how well industrial data is managed and incorporated. Are you prepared to utilize it to the fullest?
