Seven steps to making your industrial data fit for purpose
John Harrington
is the Chief Product Officer of HighByte, focused on defining the company’s business and product strategy. His areas of responsibility include product management, customer success, partner success, and go-to-market strategy. John is passionate about delivering technology that improves productivity and safety in manufacturing and industrial environments. John received a Bachelor of Science in Mechanical Engineering from Worcester Polytechnic Institute and a Master of Business Administration from Babson College.
A modern industrial facility can easily produce a terabyte of data each day. With the proliferation of sensors and the recent wave of real-time dash-boarding, artificial intelligence, and machine learning technologies, we should be seeing huge productivity gains. Unplanned maintenance of assets and production lines should be obsolete.
But this is not the case. Access to data does not mean it is useful. Industrial data is very raw and must be made “fit for purpose” in order to extract its true value. Furthermore, the tools used to make the data fit for purpose must operate at the scale of an industrial facility.
With these realities in mind, I’ve written a practical, seven-step guide for manufacturers and other industrial companies to make their data fit for purpose.
But this is not the case. Access to data does not mean it is useful. Industrial data is very raw and must be made “fit for purpose” in order to extract its true value. Furthermore, the tools used to make the data fit for purpose must operate at the scale of an industrial facility.
With these realities in mind, I’ve written a practical, seven-step guide for manufacturers and other industrial companies to make their data fit for purpose.
Step 1: Start with the Use Case
Information Technology (IT) and Operations Technology (OT) projects should all begin with clear use cases and business goals. They may focus on machine maintenance, process improvements, or product analysis to improve quality or traceability to name a few. Ensure the right cross-functional stakeholders participate in and prioritize the project—and can reach consensus on project goals.
Step 2: Identify the Target Systems
Next, identify the target applications that will be used to accomplish these goals. Characterize the target application by documenting where the application is located, how it receives data, what information it requires, how frequently the data should be updated, and what causes the data to update.
Step 3: Identify the Data Sources
Industrial data is critical for addressing industrial and business use cases, but it can be difficult to collect, correlate, and standardize. You can better understand the specific challenges your project will face by identifying your data sources. Document what data is available, where it is located, if it is dynamic or static, and if the data is currently available in the correct format or will need to be derived prior to ingestion.
Step 4: Select the Integration Architecture
Once you better understand your target systems and data sources, you can determine and document your integration architecture, which will require you to choose between making direct API connections or implementing an integration hub (like HighByte Intelligence Hub). To make this choice, you will need to know how many applications need to be integrated, if your equipment and sensor data requires curation and preparation, and how frequently your factory equipment and systems change. I’ll go into more depth on this topic in my next blog post.
Step 5: Establish Secure Connections
Now that the project plan is in place, begin system integration by establishing secure connections to the source and target systems. Understand the protocols you will be working with (open vs closed) and the security risks and benefits they provide. Security is not just about usernames, passwords, encryption, and authentication but also about integration architecture. Protocols like MQTT require only outbound openings in firewalls, which security teams prefer because hackers are unable to exploit the protocol to get on internal networks. While security has sometimes created tension between IT and OT teams in the past, ensuring a secure network is actually a great opportunity for these two functions to collaborate and add value to the project when they can agree upon common goals.
Step 6: Model the Data
It’s crucial to define standard models required in the target system to meet the business goals of the project. At the core of the model is the real-time data coming off the machinery and automation equipment. Once the standard models are created, they should be instantiated for each asset, process, and/or product. This is generally a manual task but can be accelerated if the mapping already exists in Excel or other formats, if there is consistency from device to device that can be copied, or if a learning algorithm can be applied.
Step 7: Flow the Data
When the modeling is complete, establish and control your data flows model-by-model. This is typically performed by identifying the model to be moved, the target system, and the frequency or trigger for the movement. Over time, data flows will also require monitoring and management.
Wrap Up
Making industrial data fit for purpose is critical for manufacturers looking to scale their IIoT projects and wrangle data governance. I hope this post provided you with practical advice for starting your next project. For more information on this topic, please download the full-length article, “How to make industrial data fit for purpose”.
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