From Data to Information: The Power of Industrial DataOps

With every industry buzzword, there lies a kernel of innovation ready to revolutionize business practices.

Enter Industrial DataOps, the latest framework to take the world of Industry 4.0 by storm.

So, what is Industrial DataOps exactly? Where does it come from? What does it do? And how does this discipline fit into the industrial 4.0 landscape?

That’s the focus of this article, so let’s tackle the biggest buzzword first and get some context because that will help.

Industry 4.0 is the era we are entering and experiencing right now. Kicking off roughly a decade ago, the movement has significantly gained momentum in the last five years. Unlike the third industrial revolution, which was defined by automation and the creation of data, the fourth focuses on transforming this data into actionable information. Industrial DataOps is critical to this mission.

Cloud computing, Artificial Intelligence (AI), and Machine Learning (ML), a trio that redefines agility, scalability, and efficiency in data management are core to innovation and data-driven practices. Positioned as the ‘Fourth Pillar’, Edge Computing allows data processing nearest to its source, reducing latency, enhancing operational speed, and allowing real-time analytics. This accentuates the power of the pre-existing trio with increased efficiency, as Edge Computing mitigates the strain of data transfer, enhancing responsiveness, and enabling faster insights.

Digital transformation is the strategy that makes Industry 4.0 possible. Put simply, it means shifting from manual, paper-based operations to streamlined, unified digital systems that deliver information in real-time. Enhancing efficiency, it eliminates the need for traditional, time-consuming data collection and reporting methods.

Industrial DataOps, the focus of this article, drives digital transformation. It deals with the collection, normalization, and contextualization of data coming from all your assets, ensuring it’s usable for Operations, IT, Data Science teams and executives.

This discipline focuses on the conversion of this raw data into actionable information you can use to drive operational improvements and better decision-making.

It aims to bridge the gap between OT data sources to modern IT infrastructure and analytics, drawing inspiration from the principles of continuous delivery and automated testing found in DevOps.

DataOps finds its roots in the principles of DevOps, a methodology introduced by Patrick Debois in 2009 to streamline and automate processes between software development and IT operations.

Created in response to the dysfunctional separation of roles observed within tech organizations, DataOps tackles a similar issue but in a different context. It addresses the unique needs of managing vast data volumes across multiple sources, ensuring high data quality, and delivering insights swiftly.

This adaptation is crucial for modern organizations, which need to harness their data comprehensively in their decision-making processes. DataOps focuses on the seamless management of data flows, from connection to solution, emphasizing real-time accuracy and accessibility.

By breaking down silos between data scientists, engineers and analysts, it paves the way for a collaborative and efficient data handling culture.

Industrial DataOps applies the core principles of DataOps to the industrial sector, marking a significant shift in how data is processed and utilized in manufacturing and production environments.

This innovative approach addresses the core challenges of industrial data management for OT/IT convergence—fragmentation, complexity and disempowerment—head-on, offering a strategic pathway to enhanced collaboration, efficiency and decision-making in the industrial sector and between OT and IT teams.

Industrial DataOps leverages event-driven architectures, like the Unified Namespace (UNS), to dismantle data silos. This strategy ensures data is universally accessible, transforming disconnected OT and IT landscapes into cohesive, shared information that foster greater collaboration and operational insight across all levels of an organization.

By simplifying complex OT data sources into actionable insights, Industrial DataOps accelerates the usability of data and quicker time to value for Industry 4.0 use cases at scale. This facilitates swift responses to market changes and operational inefficiencies, ensuring that businesses can scale use cases effectively across various assets and locations, tailored to specific user needs.

Industrial DataOps aligns data science with business objectives, making data insights universally accessible and understandable. A notable challenge faced by manufacturing companies is the disconnect between those intimate with the data (usually OT personnel) and the end-users (such as data scientists and engineers from various departments).

Bridging this gap means making data both accessible and interpretable to a broader audience, thus facilitating informed decision-making and driving innovation.

This democratizes the decision-making process, enabling informed choices across the entire organization and bridging the gap between technical and non-technical stakeholders.

Industrial DataOps serves as the bridge between your industrial data and actionable insights. However, to cross over to the other side, there are 8 key areas you need to consider.

1. 1.

   Industrial Connectivity

2. 2.

   Data Collection & Normalization

3. 3.

   Data Analysis & Visualization

4. 4.

   Contextualizing and Data Modelling

5. 5.

   Data Integration

6. 6.

   Data Governance & Security

7. 7.

   Data Sharing

8. 8.

   Managing Scale

The process starts with the seamless connectivity of your OT devices and systems, enabling the real-time collection of all your data. Every piece of data, from temperature readings in a food processing plant to RPMs in automotive manufacturing, is captured with precision.

It is essential to note that the success of Industrial DataOps as a catalyst to drive digital transformation hinges on ensuring each step in the multi-step framework is as seamless and scalable as possible.

The UNS, an event driven architecture coined and popularized by Walker Reynolds plays a key role in this process.

Acting as a central hub, it promotes efficient communication between smart components and establishes a single source of truth that maintains the accuracy, reliability, and real-time relevance of data.

Our quick guide to implementing the Unified Namespace in your enterprise offers practical insights into harnessing these benefits, providing a roadmap for integrating this powerful framework into your own Industrial DataOps approach.

Already predicted to be the next big thing for 2023, Industrial DataOps is set to take a long stride forward. Now that its purpose and advantages are clearer, it is ready to show its full power.

This year is expected to be a milestone, with Industrial DataOps taking its rightful place as a critical part of digital transformation strategy, helping industrial companies not just keep up, but lead in their markets by enabling data-driven operations.

In part 2 of this article, learn how you can leverage Litmus to check off all 8 steps in your Industrial DataOps strategy.

Check out success stories to see how Litmus works closely with customers on their digital first journeys.

Talk to our internal experts with your Industrial DataOps questions today!