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Contextualizing Industrial Data for Operational Success

Learn how contextualizing industrial data with IoT integration and DataOps practices can enhance operational decision-making, streamline workflows, and ensure successful digital transformation.

Table of Contents
1. How to Get Contextualized Data Where It Matters
2. Industrial Data Contextualization and IT Pain Point
3. The DataOps Advantage in Industrial Data Contextualization

The process of contextualization implies the augmentation of the raw data with additional information such as time, location, or source. This in turn provides a structure that makes the data easier to comprehend and apply. The rise of IoT-enabled devices has exponentially increased the availability of operational data, with IDC projecting that 41.6 billion IoT devices will generate 79.4 zettabytes of data by 2025. Such devices include machines, sensors, cameras, and industrial tools, among others. However, for this data to be useful, it needs to be incorporated into other systems and presented to users in a relevant and meaningful manner based on their role across the organization. Essentially, this article discusses how to successfully contextualize data, what difficulties arise with it, and what benefits come from the use of DataOps.

1.  How to Get Contextualized Data Where It Matters

In these industrial settings, it is important to give line-of-business users timely and relevant information. These users include departments such as quality control, maintenance, engineering, research and development, regulatory, and product management. Historically, these teams were only able to get the necessary information through reports, which were provided at best on a weekly or monthly basis. In some instances, custom coding and manual data cleansing were applied to combine functional systems. This is a daunting task since OT applications are diverse, have varying architectures, and may not be tagged or named uniformly.

To understand where the IoT data belongs and how it is connected to the rest of the processes in an organization, it is important to incorporate it with other platforms like ERP or CRM. This integration assists in bringing more context into data analysis so that users are able to make quicker decisions. For instance, maintenance departments may use data from sensors that monitor the performance of machinery together with records of previous maintenance to anticipate a failure and plan for preventive maintenance.

2. Industrial Data Contextualization and IT Pain Point

The methodology of merging and adapting received data from various sources is time-consuming and potentially problematic. This can lead to potential failures in digital transformation efforts. A 2021 IDC survey on the future of operations revealed that 36% of IT and operations professionals polled said that ML or AI projects did not generate ROI. This puts into perspective the challenges involved in the handling and application of industrial data.

The first concern is the lack of uniformity when it comes to various data inputs and outputs. Since OT applications may employ their own format and protocols, merging data can be a challenge. Furthermore, data cleansing and integration done manually are cumbersome and error-prone tasks that increase the likelihood of inconsistencies. These challenges highlight the need for data integration and contextualization to be largely automated and normalized.

3. The DataOps Advantage in Industrial Data Contextualization

By adopting DataOps, which is a framework of practices meant to enhance the quality and speed of data handling and analysis, these challenges can be greatly mitigated. DataOps helps with the integration of data and their on-demand processing, thereby improving the quality of the data used in operational decision-making.

The best practices of DataOps include the employment of data pipeline automation, which simplifies the data collection, transformation, and delivery processes. These pipelines are capable of processing large amounts of data from various sources and always provide the latest and most accurate data. Applying concepts such as data integration and contextualization can enable organizations to streamline the data management process, freeing up time for analysis and decision-making.

In addition, DataOps encourages effective communication between IT and operational departments, where data projects are aligned with organizational objectives. This approach assists in breaking down the barriers between producers and consumers of data to enhance their comprehension and use.


Understanding the context of industrial data is necessary in order to get the most out of it and make operations as effective as possible. The continued increase in the use of IoT devices implies vast amounts of data collected; however, these values are latent if not contextualized. By recognising the key issues in data integration and adopting DataOps, organizations can improve the decision-making process based on data and make proper business decisions for better performance.

Data contextualization provides a suitable environment in which other raw data can make sense and be useful. The inclusion of IoT data with other systems, as well as the implementation of DataOps practices, can revolutionize how organizations approach data handling and use. In this way, it becomes possible to obtain more effective results from the transition to digital, guaranteeing that investments in ML and AI lead to outcomes that meet expectations. As a result, the emphasis on contextualized data provides new opportunities for organizations to achieve new levels of analytical understanding and operational improvements in today’s data-centric business environment.

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