data Analytics & Management
Every day, thousands of terabytes of data are generated across the industrial space. Locked within this data are valuable operational insights that can offer a wealth of benefits to companies — as long as it is collected, stored, analysed and managed effectively.
There are many different facets of industrial data management for businesses to be aware of, but the prospective rewards range from increased productivity and return on investment (ROI) of industrial tech investments to reduced losses and optimised energy consumption.
What is Industrial data management?
Industrial data management is, put simply, a way of strategically managing the data generated, collected, stored and analysed in an industrial business.
The advancement and shrinking cost of sensor technology has made it possible for industrial businesses to monitor and collect data from more equipment, machinery and processes than ever before. This data might hold valuable information that can help improve operations or inform business decisions, but only if it is strategically stored and effectively analysed.
There are several ways that businesses can improve their data management, from developing a structure for effectively storing data to establishing a scalable system for analysis that offers relevant insights for different stakeholders.
Defining a data strategy
The cornerstone of effective industrial data management is to define and implement a practical data strategy.
An effective strategy is one where collection and aggregation is supported for every process, practice and piece of machinery that is pertinent to operations. From this data, a company can perform suitable analysis to scale granular information in such a way that it provides macro insights. These can be viewed by, and reported to, any stakeholders with an interest in an area of operation.
In essence, the most effective way of thinking of data like a multitool. It’s most useful to use one system to collect and store specific data sets, with various tools that are applicable to other people, teams and situations. Granular data collection systems can aggregate upwards to add meaning to people at every level.
Read about how to define an effective data strategy here.
Redefining the boundaries of automation
One of the barriers to an effective industrial data management strategy is a misconception that data generated and collected on the plant floor or in the field is of minimal value to higher level insights. It’s the idea that the technical data that engineers need for equipment maintenance has little-to-no bearing on the energy management data that plant or utilities managers requires, or the overall plant performance data relevant to regional or country heads.
A more effective option is to understand how field data can, with the right analysis, underpin top level strategic insights. When collected data is analysed in the context of historical data, maintenance and line managers can then see that trends begin to emerge. Field data can also be scaled to contribute towards calculating key strategic performance indicators for executives.
This all supports the idea that the boundaries of field and automation data are moving away from local areas and extending to all levels of a business. Rethinking how applicable this data is to different areas of a business is proving increasingly important as a factor in developing effective industrial data management.
Read an example of how impactful redefining the boundaries of automation data can be.