Industrial data management is guided by five objectives that ensure data from sensors, on-site collection systems, subject matter expertise, and maintenance records are ready for valuable decision support.
Here are five keys for industrial data management:
1. Integrity
Data integrity is the completeness, accuracy, and consistency of industrial asset data as part of enterprise-wide governance. It's enabled by the continuous monitoring of completeness and accuracy through the lifecycle of data across the organization to improve business decisions, enterprise reporting, and regulatory compliance.
2. Cyber Resilience
Cyber resilience is the security of industrial asset data from unauthorized access or use. It ensures the safety of operations and protect proprietary information.
3. Enrichment
Data enrichment is the enhancement of industrial asset data through aggregation, contextualization, cloud computing, and operationalization for various uses, among them to assure productivity, optimize maintenance, mitigate risk, improve safety, streamline compliance reporting, and strengthen environmental, social and corporate governance (ESG) initiatives. Context is key here, especially as non-traditional consumers of OT data, including non-engineer data scientists, look to improve operational decisions.
4. Portability
Data portability is the cost-effective movement of industrial data from the plant to the enterprise cloud for use within the organization or by third-party data consumers. It's enabled by interoperability with current OT and IT systems as well as through future-proofing (like open APIs or data formats) for digital transformative initiatives.
5. Simplicity
The most difficult to maintain above all other principles is simplicity. It's the ease of time and money with which operators can effectively do all of the above.