Just getting the data to end users does not produce results.

Early adopters of Industry 4.0 focused on integrating OT and IT systems to collect business and process data in a single source (a data warehouse or lake). This addresses the problem of data accessibility but not the problem of a lacking data context.

In his article about Industrial DataOps, John Harrington, Co-Founder and Chief Product Officer at HighByte makes an interesting point about a lacking data context:

Business users did not have the same understanding of the manufacturing controls system and required rich data to optimize business performance. The data from industrial environments was inconsistent across machinery and correlated to the controls equipment—not to how business users think. ― John Harrington ―

Process data was not intended to be analyzed by humans - it was used for process control by machines, i.e. automation. Users need a lot of domain knowledge about the manufacturing process to interpret raw process data coming from machine sensors.

If companies want to share their process data across the company, they have to provide a context: metadata that explains what this data is and how it relates to the process. One approach is to document this metadata in so called data catalogs. You’re essentially manually “maintaining a Wikipedia repository” of your company’s data which requires lots of labor.

More recently, Industrial DataOps has been pushed as the solution to the problem of data accessibility and context. The article linked above provides an excellent introduction.