Industry 4.0-empowered solutions help manufacturers become more nimble, integrated, efficient, safe and sustainable.

They just need data to get there. 

Manufacturers create enormous amounts of data, but they have to extract it, or “mine” it in order to transform it into actionable insights (which should lead to automation). After all, data serves no purpose without knowledge discovery.

Data mining helps businesses envision patterns and trends amid raw data. These insights can lead to faster, more informed decision making. 

Cloud-based applications, as well as IoT and other solutions, help manufacturers quickly process and digest data.  

They need connectivity to do this. Stable and saleable connectivity delivers straightforward insights, insights that tell manufacturers how to optimize their processes in real-time. In other words, they’d be able to make smarter decisions, faster than usual.

This helps them to better align supply plans with demand forecasts. They can detect problems, predict machinery wear and schedule maintenance. This helps businesses to optimize production.

Data collection isn’t always easy. Sometimes you’ll come across redundant or incorrect data values, due in either human error or software issues. Data mining also invites security and privacy concerns. 

How does data mining work?

First, manufacturers gather data and, either manually or in an automated fashion, transfer it to a data warehouse. The stored data will ideally be stored on the cloud, as opposed to in-house servers. Organizations often use data visualization tools to scan for relevant information.

Then business analysts, management teams, and information technology professionals take it from there. 

Data should be offered in an accessible format and shared across everyday business operations.

Manufacturers who need help mining through their excess data to create insights should start with use cases before moving into acquisition, analysis and integration strategies. Simply compiling data and attempting to filter out information isn’t efficient.

Here are some tips to start:

  1. First, know your objective: Understanding your end goals and working backward provide crucial direction and clarity in what can otherwise be a sea of data. 
  2. Bring stakeholders together: Informational technology (IT) and operational technology (OT) departments may speak different languages and want different things. Working out a compromise that satisfies both teams help organizations avoid pitfalls.
  3. Know your infrastructure: Manufacturers harvest overwhelming amounts data. Understanding how your systems and infrastructure work can help you identify the endpoints that matter.  
  4. Curate teams: Cherry pick individuals from existing teams to form a collaborative group dedicated to data insights. This accelerates curiosity, cooperation and, ultimately, insight.