5 Keys for Industrial Data Management

With initiatives to uncover value in operational data through asset performance management and advanced industrial analytics, best practices for industrial data management are critical.

But without a coherent strategy for integrating data sources or leveraging subject matter expertise, many industrial businesses have found themselves revisiting the same question.

How can data sources be combined in the cloud – through a data lake, data estate, or enterprise datastore – in a scalable and cost-effective way?

In this blog, we lay out some considerations for industrial data management and five best practices for generating the most value from industrial data analytics.

In the past, we’ve likened the position of industrial companies to an advanced analytics trap – there are not enough data, too much data, not enough of the right kind of data or analytics, too many of the analytics that become just more information to parse through.

Analytics can quickly become an expensive way to run into the same challenges. Teams need to make decisions faster.

And to have visibility into operations, risk, and sustainability, enterprises must have a secure and cost-effective foundation to use data for different ends.

It’s important to establish a data management strategy that sees to their incorporation in a single source of truth. Cloud computing, for example, enables secure access to data to more users than on-premise systems.

If an original data source cannot be integrated for proprietary or cost reasons, or if this data needs to be moved repeatedly in one-off instances to the cloud, then the enterprise is losing out on valuable support for better decision-making.

Industrial businesses can begin with understanding the state of their data today and their valuable assets that would benefit from industrial intelligence.

The need for analytics vary by industry, company, business unit, and even specific data consumers, but the basic requirement of data readiness remains the same. Unified data management is the enabling force behind the possibilities of industrial intelligence.

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.

The power of industrial data management done right is scalability and flexibility. It has a high-value impact across business units and individual teams.

Operational and financial teams have harnessed the computing power of a mature and interoperable IT infrastructure to track and improve CapEx vs. OpEx decisions. Teams focused on safety and sustainability have documented records, and created an audit trail for compliance and investors. And maintenance and reliability teams have validated the value of certain preventative maintenance (PM) tasks and eliminated others, moving to a predictive maintenance (PdM) program for high-value, high-risk critical assets.

Data historians and their licensing requirements, pre-modeling, and implementation services have largely kept OT data on-premise, or have made decentralized and scalable use of industrial data too expensive. No longer does that have to be the case.

Asset-intensive organizations today are able to realize the value of industrial data with the instrumentation and cloud environment they already have in place.

Uptake Fusion is empowering people to leverage their OT data, retain its context, and enrich it in a secure environment. Prepared for operational applications like Industrial AI/ML, industrial data management is lighting up areas of improvement across the enterprise.

Ready to put your data to work?