IOT Optimized Data to Empower Engineers

Intelligent use of the wealth of data that operational technology (OT) systems produce is central to industrial organizations' efforts to improve operational excellence. OT data is the raw material that enables organizations to build more efficient and resilient operations and improve employee productivity and customer satisfaction.

Industrial organizations, however, struggle to generate value from their increasingly connected operations — with IDC research showing that only one in four organizations analyzes and extracts value from data to a significant extent.

What is modern industrial Data Ops?

IDC defines DataOps as a methodology for industrializing data management and the data analytics value chain. It applies automation, agile methods, and DevOps practices to the data life cycle, improving time to value, quality, predictability, and scale of data analytics.

Industrial DataOps platforms help data workers deploy automated workflows to extract, ingest, and integrate data from industrial data sources, including legacy operations equipment and technology. They offer a workbench for data quality, transformation, and enrichment, in addition to intelligent tools that apply industry knowledge, hierarchies, and interdependencies to contextualize and model data. This contextualized data is then made available through specific application services for humans, machines, and systems to leverage.

What to Consider When Adopting Industrial DataOps

Asset-intensive organizations should look to industrial DataOps to unleash ET, OT, and IT data's full potential and transform their traditional operating model. When starting on this journey, companies should:

1. Think of AI as a critical tool for both fact-driven decision-making and efficient management of the data supporting it. Bypassing human "midstream" data handling is
2. critical.
3. "Data liberation" is critical to maximizing value from DataOps. Maximizing your data extraction capabilities will make it easier to plug DataOps into your existing IT and OT
4. architecture, limiting the need to invest in additional systems integration and OT data sources.
5. Develop an IT/OT governance model with data governance at its core. This will dictate how new data is connected and integrated into the overall data architecture. It will also help serve a growing population of data and analytics business users.
6. Prioritize data organization over-centralization. Start driving the connection and mapping of all relevant data sources with a clear list of target use cases in mind. As part of the governance model, all new data sources must have a connection, tagging, sharing, and integration plan.
7. Note that not all DataOps platform vendors have the same capabilities. Domain expertise and industry track record should drive selection criteria.

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