Industrial data management is a multi-disciplinary function covering data management across the supply chain, operations, and production.

Real-time data and its management are pertinent to multiple subdomains in manufacturing. IIoT data management facilitates innumerable combinations among these subdomains. Manufacturers can use data to quickly and more effectively align with external and internal dependencies like market demand or raw material availability, among others.

The transformation towards a connected manufacturing ecosystem like connected factories has emphasized the importance of IoT data management. An IoT enabled industrial data management system is a natural complement to technologies powered by artificial intelligence, machine learning, and big data. Let us take a look at the benefits and challenges in IoT data management for industrial IoT.

Benefits Of IIoT Data Management

Improve Operation Costs And Facility Management

IIoT data management is responsible for improving operating costs and facility management. The multiple data forms involved in operations like inventory levels, purchase pipeline, and capacity utilization are covered here. Appropriately planned and executed data management system enables better decision making for cost optimization and resource utilization.

An IIoT data management system further ensures that the benefits of identifying leading indicators of quality and efficiency have a more significant positive impact. It also helps reduce, if not eliminate, the effect of lagging indicators.

Improve Production

IIoT data management is a critical driver for meeting and exceeding production targets. Data management ensures the quality of data being collected and processed by internal stakeholders, dependent processes, and data processing tools. Data management creates a seamless flow of valid and clean data between these multiple interlinked systems is made possible.

The advantages of a connected manufacturing system are amplified through data management. Helping increase the effectiveness of predictive quality, asset utilization, and in decreasing wastage.

Improve Industrial Safety

Data management in an industrial manufacturing set up involves:

• Standardization of data formats, file types, and sources
• Definition of rules for data access, transport, and analysis
• Compliance management for operations and safety regulations

These steps work in close cohesion to identify and process data much faster. In the process, it also helps reduce the time required to predict incidents or its precursors. The reaction time involved in handling handle exigent circumstances can also be considerably reduced.

Improve IoT Security

IIoT is a powerful tool, and yet it also opens facilities and infrastructure to newer vulnerabilities. Data management as a discipline and a function inside an industrial environment help build and maintain security protocols to safeguard your data.

Challenges Of IIoT Data Management

Let us consider a brief overview of some of the commonly faced challenges in IIoT data management:

Scalability And Agility

Digital transformation drives competitive advantage in no uncertain terms. With connectivity and algorithm defined, user-driven communication being a bulwark for effective digital transformation. A central tenet of connectivity is its ability to scale in volume and features. Data management needs to recognize and conserve this ability in the managed IIoT deployment.


Implementing data security protocols in this increasingly hostile cyberspace is a tall task. The lack of compatibility between previously unconnected systems inside a plant makes this task harder.

Lack Of IoT Standards

It is a challenge to standardize a system that could be built on innumerable combinations. The present lack of IoT standards impacts the scalability, agility, and security of IIoT implementations. And this makes data management even more important while completing the task ahead more difficult.

Hidden Costs To Avoid With IIoT Data Management

Hidden costs can always clandestinely erode bottom-line performance. Lets us take a look at three hidden costs that manufacturers need to be mindful of.

Cost #1 Unrequired Inventory

Data management ensures that inventory levels maintained are optimal. This, while also being ready to absorb fluctuations due to product demand or raw material availability. Recording in-process user consumption patterns and efficiencies also help in optimizing operational expenses.

Cost #2 Service And Maintenance Costs

Data management ensures clean data is available for analysis. Quality data strengthens the effectiveness of decision support systems that include predictive service and maintenance.

Cost #3 Capital Investments

IIoT data management also helps validate the timing and requirement for additional or complementary optimization of production capacity. For instance, decisions like investing in new manufacturing lines or increasing line capacity are made more frequently and efficiently.

IIoT Data Management And Manufacturing Success

Data management in an IIoT enabled industrial environment is a critical requirement for manufacturing success. IIoT provides granular access to all areas that impact the plant, and in its practical implementation, these granular areas roll up to optimized manufacturing efficiencies. An effective implementation demands that stakeholders from the different manufacturing domains work together and that data management infrastructure and data security are ensured.