Industrial companies need contextualized data and digital architecture that scales. Learn how Industrial DataOps helps enterprises meet these requirements.
You are paying high, variable Cloud storage fees for raw industrial data without a strategy for how the data will be used.
Orchestration in a DataOps solution is made possible by comprehensive IT/OT connectivity, data pipelines that can transform data payloads, and an ability to work with various types of data (streaming, transactional, historical) and data movement methods (publish, subscribe, request). By coordinating data pipelines, integrating diverse data sources, and ensuring efficient data processing, data orchestration provides scalability, reliability, and agility in data management.
Observability encompasses the ability to understand, diagnose, and manage data health across multiple solutions by continuously monitoring, tracking, and troubleshooting data issues. By monitoring live/historic data pipelines, your DataOps solution should provide insights into pipeline performance.
The usability of a set of data for its intended purpose is determined by the emphasis on quality that a DataOps solution can enforce. This means data quality is measured by attributes such as accuracy, completeness, consistency, reliability, and timeliness to ensure it meets business and analytical needs. High-quality data is essential for effective decision-making, compliance, and operational efficiency.
To manage the scale that a DataOps solution must provide, it should offer governance capabilities that simplify the administration of process and technology. Data governance involves enterprise administration considerations like setting policies, roles, and responsibilities to ensure data is managed effectively, securely, and in compliance with company policies and regulations.
DataOps inherently avoids offering full-stack capabilities that would offer device connectivity, data storage, visualization, and data analysis. Instead, DataOps is middleware designed to be open and ready to connect with a wide range of other systems and devices that the enterprise already uses. This allows you to rapidly move across your data and deploy it in a way that works best for your use case.
With DataOps deployed at the edge, data management is placed closer to the point of data creation. This encourages domain experts at the plant level to support data contextualization efforts. It also allows you to make data usable as far downstream as possible by filtering redundant data, standardizing edge-data faster after creation. The result is optimized solution performance and cloud storage costs while ensuring secure and contextualized data reaches downstream/consuming systems consistently.
To be useful at the edge, an Industrial DataOps solution should not be hard to install, integrate, or maintain. The ideal approach is to use software that is flexible and able to be deployed both on physical systems and in a container. Being light weight allows it to be deployed in a variety of architecture patterns, while being codeless empowers OT domain experts to leverage the technology.
Using a DataOps solution builds an abstraction layer that allows data engineering work happening in one instance to be applied in another. This is beneficial in industrial settings where digital environments vary widely across lines, areas, and sites. Adopting Industrial DataOps saves you from the headache of rework and wasted time while improving the growth potential of digital efforts across your company.
"By 2026, two-thirds of manufacturers will be pursuing industrial DataOps strategies to enable the execution of AI-supported use cases, thereby improving operational efficiency."
IDC FutureScape: Worldwide Manufacturing 2025 Predictions, Doc #US51711224
"Data and analytics leaders seeking to deliver high-quality, trusted data products to their businesses should invest in a DataOps tool to streamline their data delivery operations and reduce the manual effort required to manage complex data pipelines."
Gartner Market Guide for DataOps Tools, ID G00784179, August 2024
“Industrial DataOps transforms raw data into actionable insights, driving real-time innovation, operational excellence, and future-proof decision-making for industries in an era of digital evolution.”
Frost & Sullivan Top Growth Opportunities for Industrial, 2025
