DataOps for Manufacturing: A 4-stage Maturity Model

Summary

The promise of Industry 4.0 has many manufacturing leaders thinking big. They envision a future in which real-time data access opens the door to new levels of operational flexibility, predictability and business improvement. The unfortunate reality that many manufacturers have discovered is that early-stage wins do not indicate future success. These manufacturers are seeing their larger, more ambitious projects stall or fail to scale because their data infrastructure cannot support the increased project complexity.

Enter Industrial DataOps.

DataOps (data operations) is the orchestration of people, processes and technology to securely deliver trusted, ready-to-use data to all the systems and people who require it. The first known mention of the term “DataOps” came from technology consultant and InformationWeek contributing editor Lenny Liebmann in a 2014 blog post titled, “DataOps: Why Big Data Infrastructure Matters.”

According to Leibmann: “You can’t simply throw data science over the wall and expect operations to deliver the performance you need in the production environment—any more than you can do the same with application code. That’s why DataOps—the discipline that ensures alignment between data science and infrastructure—is as important to Big Data success as DevOps is to application success.” ​
 

Breaking down the model

DataOps solutions are necessary in manufacturing environments where business users need to leverage data aggregated from industrial automation assets and systems for different purposes throughout the company and supply chain.

HighByte designed and developed a DataOps solution specifically for the manufacturing sector, allowing manufacturers to create scalable models that standardize and contextualize industrial data from real-time, transactional and time-series data sources. Over the years, we have worked with many manufacturers through varying stages of their DataOps implementations, all with different goals.
  
Based on these insights, we’ve created a maturity model to help data leaders at industrial companies understand where they are on their own maturity journey—and where they need to go to achieve the results they expect.

The model defines a four-stage process.

1. Data access: The data access stage is useful for optimizing controls and other key operational functions. However, many companies find the data is not suitable for higher-level business analytics or most use cases beyond process monitoring.
2. Data contextualization. The data contextualization stage provides contextualized and standardized data points to the operations team, enabling them to compare similar data points. Using newly accessible analytical information, the Operational Technology (OT) team can make more informed operating decisions.
3. Site visibility. The site visibility stage provides information payloads to business users outside of operations. This data is typically used to improve quality, research and development, asset maintenance, compliance, supply chain and more.
4. Enterprise visibility. The enterprise visibility stage provides the broadest value to companies, allowing them to aggregate information across sites with common dashboards, metrics and analytics. It also allows them to implement sophisticated, data-driven decision-making and Cloud-to-Edge automation.

The successful attainment of each stage—and the benefits associated with them—is dependent on three parameters:

• Team
• Data handling
• Data structure

Figure 1 provides an overview of these four maturity stages and how team, data handling and data structure impact the process.

 Conclusion

The key takeaway here is that manufacturers can’t achieve the benefits of enterprise visibility with the approach of data access alone. 
 
Many companies have been sold the benefits of enterprise-wide data visibility and usage but do not recognize the data requirements to do so. Yes, we are in the age of APIs. However, when working with manufacturing data, it is not just about providing access to the data and letting the data scientists conjure business performance through artificial intelligence. Business users must work with the teams who support the factory, data must be curated, and solutions must be designed to be implemented at scale across the site and enterprise. Only then can data-driven decision-making and Cloud-to-Edge automation be achieved.