Industrial Data Contextualization

Industries do not need more data; they need to add context to the data they already have.

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Data is only useful if you can derive meaning from it. Otherwise, it is merely sets of text and numbers which offer no value. In this era of the Industrial Internet of Things (IIoT), industries have access to terabytes of data, and it can be difficult to parse all the data to find actionable insights. But none of this is possible if the data has no context.

In this guide will learn more about:

1. Data contextualization

2. Drowning in Meaningless Data

3. The Changing Face of OT Data Management

4. Critical Aspects of Industrial DAMA Management

5. Essential Components of Industrial DataOps Solutions

6. Strategies for Industrial Digitization in the Manufacturing Industry

7. Conclusion

Data contextualization

Data contextualization is the process of adding related information to data to make it more meaningful/useful. Context is the missing link that brings out relevant correlations, patterns, or trends that inform actionable insights. Without context, it is difficult – even impossible – to interpret or decipher big data.

Why should you care about data contextualization? The following insights on the future of data management can help:

  • The future value of data management relies on automated discovery of data relationships
  • By 2023, 30 percent of organizations will be applying data contextualization tech for faster knowledge extraction and proactive decision-making
  • By 2023, there will be 5x more cloud-based AI used by organizations

 The world is scurrying towards AI, which frees up traditional IT experts for high-level data management processes. Meanwhile, the role of the citizen data scientist is emerging, powered by data contextualization software and other technologies.

Change is inevitable – but you can benefit by working proactively to position yourself ahead of the curve. Learn everything about applying data contextualization strategies for business gains.

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Drowning in Meaningless Data

The manufacturing industry is undergoing yet another phase of the industrial revolution, popularly christened Industry 4.0. The first was the adoption of external power (the 1700s-1800s), the second was factory electrification (late 1800s) and the use of motors to drive machinery (early 1900s). The third revolution related to the automation of control motors in the mid-1900s.

In the ongoing revolution, industries are finding ways to use business intelligence (BI) and predictive analytics (PA), both powered by AI, to get real-time feedback and inform proactive decision-making. This age of industrial digitalization is about leveraging disparate data sources (machinery and Cloud) to bring better, more timely insights to decision-makers.

Unfortunately, the Internet of Things came along faster than the industrial world was prepared to handle it. For many years, companies struggled with finding qualified professionals to handle the newly-acquired volumes of data. Even those with qualified staff found that humans had limited processing capacity considering the volumes of data in question.

Data contextualization was labor-intensive and time-consuming. Many businesses were still starving for insights even while surrounded by terabytes and petabytes of data.

The companies at the forefront of Industry 4.0 thought they could well connect their IIoT big data to visualization and analytics software. However, they quickly found inconsistencies in machinery. Without context to explain the data, few meaningful insights could be derived from the data.

This problem created the need for new software solutions – data contextualization software – to contextualize and standardize data. This new branch of data analytics is called DataOps, or Industrial DataOps in the context of the industry.

 

Data Contextualization in the Manufacturing Industry


Data science for the industry has been a long journey. The manufacturing industry has experienced disappointment from industrial data science initiatives: while promising improved safety and performance and predictive maintenance, they were expensive, inefficient, and hard to scale.

It was for lack of data – for years manufacturers have been running data lakes and warehouses. However, the data scientists spent most of their time sorting and cleaning data instead of refining algorithms and deriving advanced analytics to generate value.

Data science can only be valuable if DataOps is embraced in its purest form – delivering the right data at the right time for the right problem to the right user in the right context. The problematic data lakes should be eliminated – unformatted, raw data stored without context may be meaningful for only a select few who understand the data intuitively.

The true Industry 4.0 can only be achieved if we wholly embrace the disruption that modern cloud architectures bring. We must think beyond traditional data management and operationalize data science in industry. And this is only possible if we understand how to leverage DataOps for maximum business benefits.

Learn more about why industrial digitalization needs data fusion and contextualization.

 

The Changing Face of OT Data Management

Machine learning-driven operations or MLOps has long been the shiny new toy within the industrial sector, especially in the heavy asset industries like oil and gas (O&G). These technologies are lauded for their ability to handle large volumes of new data, such as the digital twins common in heavy asset businesses. However, some have stuck with physics-based modeling while others have adopted more balanced AI modeling.

Many industry-based businesses have not accepted full-AI approaches, especially businesses that have zero risk tolerance in critical systems. Others still have never gone beyond the pilot stage of AI or hybrid data integration. Still, the next few years will see a host of changes in the adoption of industrial digitization, including:

  • The use of graph data modeling to facilitate rapid data fusion and contextualization
  • The purchase of data in formal online marketplaces will rise by 10 percent in the next two years (2022)
  • The adoption of public cloud services for innovations in data science and analytics, among others.

But the truth of industrial AI at this stage is that it will not begin with fancy robotics and the awe-inspiring demos characteristic of annual conferences. The shift will affect the smallest parts of the business, to grant universal access to meaningful (normalized and contextualized) data. The focus should be on scaling and refinement rather than delivery and design – the idea is to empower citizen data scientists to do more.

Preparing data for analytics is becoming more automated – freeing up data experts to focus on advanced manipulations and development of algorithms. Automation also brings with it higher data quality, which is critical to inform decision-making in real-time on the factory floors for production optimization.

Industrial DAMA management is still evolving: PWC reports that industrial AI adoption will move beyond IT departments to operational technologies (OT). The training of the broader workforce in necessary data manipulations (with the help of standardization and contextualization software) will remove the pressure of getting industrial data science experts, who are in short supply.

Gartner collaborate these findings: the next 2-5 years, they say, will see massive adoption of augmented analytics platforms (hybrid AI systems) to empower non-professional data consumers. Heavy asset O&G industries may have to build their own data systems rather than customize off-the-shelf solutions. Enterprise AI solutions may not meet the needs of such a large and complex data ecosystem, and companies must invest in building systems that align with their business use cases.

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Critical Aspects of Industrial DAMA Management

The majority of manufacturers have begun to create a newer digitalized industrial future, each working in its own way. Regardless of what they choose to call it, the core of this new transformation is mastering plant data management. All automation, analytics, and connectivity must be based on contextualized and correlated real-time production data.

This involves seven key steps, which are:

- Collecting data in different formats from disparate data sources

- Preparing data for analytics, including cleansing to improve data quality

- Data normalization to create a coherent picture and bring agreement between data sets

- Storing critical data to improve decision-making

- Ensuring stored data maintains data quality standards and consistency

- Enriching data meaning by supplying context, typically using other data streams

- Data analytics for real-time plant optimization


Manufacturers are looking to use these steps to power their digital transformation (Industry 4.0). Most programs are shifting towards integrating IT and OT data with IIoT sensor systems and integrating plant, equipment, and other enterprise systems.

But for all industries, industrial DataOps solutions can only prove valuable if it takes care of the five essential components listed below.

Essential Components of Industrial DataOps Solutions

Data Processing – Standardizing, Normalizing, and Contextualizing

Industrial data is created for control – to manage machinery, conveyors, sensors, valves, and other plant equipment. The data is drawn from hundreds of machine controllers, PLCs, remote terminal units (RTUs), and smart sensors.

However, these machines and sensors are often acquired separately – from different vendors at different times. Often, as the factory grew and its needs changed, products evolved, and so did the kinds of controls that were deployed on the factory floor. What’s more, data points on every machine are different between controllers, which creates the challenge of enforcing consistency.

Generally, IIoT data points do not have any consistent standardization, documentation, or contextualization of data packets. They were designed to allow communications and only, and not for predictive maintenance or high-level analytics characteristic of the industry 4.0 age.

Therefore, to derive the full value of analytics data collected from machinery and controllers, there must be a way to make meaning across machinery, products, and processes. These data points – easily in the tens of thousands – must apply standard modeling often regulated in the DataOps solution. These models help to correlate disparate data from different processes, products or types of machinery and present useful data to other applications or citizen data scientists for further action.

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Connection to Industrial and IT Systems

IT and OT systems communicate in various ways. OT systems, usually industrial devices, machinery, and systems, apply proprietary protocols. However, there is increasing support for OPC UA and the other open protocols in recent times. Similarly, IT systems have their own protocols used to communicate, with bespoke integrations and widespread API usage.

To communicate with Edge, IT systems have started using MQTT, which minimizes cybersecurity exposure for improved safety and facilitates secure, encrypted communication. MQTT is highly flexible, has few overheads, and applies a pub/sub methodology to enable the benefits as mentioned above.

Industrial DataOps solutions must have seamless integration with data sources and devices in the operations layer. Simultaneously, they must leverage industry standards to offer value for business applications in line with the current IT best practices.

Managing Information Flow

Information flows within industries should be appropriately managed: one must be able to identify, modify, and enable or disable as needed. Managing information flows helps to understand the impact of plant floor changes (device and machinery), ensuring that useful data goes to storage, and any changes do not interfere with the established connections.

Where security is concerned, knowing which data is moving through the various systems and the ability to turn it off and on has its benefits. Outside vendors are now using machine data to enable service enhancement.

The operations team on the factory/plant floor should also be able to control the kind of data that flows out, and the set of conditions or frequency with which it is distributed. They will need the ability to disable data flows when the vendor no longer needs it or for other reasons that may arise. As such, any industrial DataOps solution should include robust information flow management.

Security and Scalability Features for the Industrial Setting

Industrial data differs from the usual data types stored within IT systems. In the industrial setting, data flows in from hundreds, even thousands, of sensors and devices/ machines. All this data must be captured, contextualized, and distributed according to the unique needs for each use case, for example, for analytics or data visualization and decision-making. Often, this data is needed/applied within seconds of its creation to inform real-time predictive maintenance or operational optimization.

Therefore, the batch processing solutions (extract, transform and load, or ETL) solutions in transactional IT data won’t work efficiently for industrial data. This has led to the adoption of data twinning and other strategies to curate and contextualize data close to its source and before storage.

Considering that within industrial data is the fundamental intellectual knowledge of an industrial plant, the DataOps solution must secure the data and deliver it separately to all applications that use it.

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Living on the Edge

Finally, industrial machinery comes in all shapes and sizes, and they are run in dozens of environments. Depending on the needs or capabilities of the OT system and the analytics or visualization application in use, data processing may happen close to the machinery, in the Cloud, or on an on-premise data center.

Regardless of location, DataOps solutions should run close to the device, feeding all applications the data they need at the required frequency and within the set parameters. The solution must also facilitate sharing across the company and factory, allowing data standardization, contextualization, and normalization. It must rise to the occasion for changing needs.

Strategies for Industrial Digitization in the Manufacturing Industry

Even though digital transformation has been around for nearly a decade, n many factories, digitalization has never gone beyond pilot projects and partial deployments. Many manufacturers put off this inevitable change because it seems too time-consuming, costly, and complex to implement.

But the IIoT is here, and sooner or later, manufacturers will have to assess which offerings are best suited to their plant. Digitalization of industry need not be an all-or-nothing deployment, and it doesn’t need to overwhelm an interested business. It is easy to leverage IIoT for your benefit, to gather actionable machine data that informs high-level decision-making and factory floor operations.

Determine What You Want

Today, we have more data within our reach than at any other time in history. Therefore, your bid to operationalize data science must begin with deciding your goals, and the actionable data that can inform decision-making towards this goal. For instance, if the goal is energy efficiency, you should know current energy consumption from various sources to identify potential areas of wastage.

With your goals defined, you need to partner with a reliable IIoT and DataOps technology provider. These vendors can help you define your baseline and map out insights within the IIoT systems. This way, you can proactively address operational bottlenecks to prevent breakdowns or other issues in the future. Once you have collected more data, you can start benchmarking applications against each other and applying historical data to inform predictive analytics.

Start Small and then Scale

The potential investment of IIoT, enterprise AI and industrial data science is prohibitive to all but a handful of businesses. There are complex data architectures requiring high-level hardware and software solutions. They require a considerable investment of person-hours, materials, and financial resources in set-up and deployment.

However, as mentioned, digitalization need not begin with all the bells and whistles of a company-wide deployment. A simple approach is to begin small and then scale capabilities over time. Outline your goals, design the system, and define your ROI parameters, then run a pilot program on a section of machinery. Monitor the improvement vis-à-vis the goals and ROI. Once you see tangible benefits, you can expand to other machinery in the plant.

Use Open Tools and Protocols

This may be hard to hear in an ecosystem that thrives on proprietary everything. However, it is critical to ensure any AI and IIoT products or systems do not restrict your plans to expand. Implementing open-source protocols and tools is a great way to side-step this pitfall. With these, it is easy to customize systems according to your changing needs and to adjust or redirect data based on your evolving goals and RIO objectives.

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Conclusion

You can leverage industrial DataOps to define your data modeling and manage integrations. Using DataOps OT teams can give much-needed data to the systems and users that need it through efficient and controlled channels.

With information from DataOps OT teams can make changes in the factory, react in timely manners to critical failures, and add new applications as needed. Including data contextualization makes data from different sources more meaningful, enabling industries to use qualified data for proactive and reactive decision-making.

Industrial DataOps is an irreplaceable component of the industry 4.0 journey and all it brings, including industrial digitalization and smart manufacturing.

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Operational Digital Twin: How Data Contextualization Provides A Complete, Actionable Understanding Of Industrial Operations

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