How to digitize and integrate your engineering data

Paper-based engineering systems are inefficient and costly. Continuing to rely on them in today’s market means putting yourself behind the competition. There’s more than one way to digitize your systems. Whether you rely on cloud databases or use machine learning to improve your processes, you can find something that works best for your business.

By Robert Hopkin, Utilities Industry Specialist, Accenture

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Save costs and time: Benefit from digitizing and integrating engineering data. | Image: Sven Mieke / Unsplash

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When it comes to data management, most engineering departments are struggling. That’s because their record-keeping, data sharing and data integration practices still aren’t what they could be. “Data silos”, paper documents and lack of insight are the rules, not the exception. In fact, eight in ten executives estimate that most of their data even “inaccessible”.

That’s a huge hindrance to productivity, flexibility, and innovation, of course. So how can executives fix the issue?

Digitization and integration can help organize fragmented and inaccessible information. This unlocks significant value for engineering organizations.

Why digitize your data?

Data digitization and integration are a critical foundation for deploying cutting-edge engineering capabilities. Without these efforts in place, getting the full value from an organization’s data can be a challenge. And harnessing methods like digital twins or virtual prototyping and testing outright impossible.

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Companies that invest in integration, however, can expect to secure significant benefits:

• Digital Continuity: Integrated digital data makes enforcing data consistency easy. This, in turn, makes data easier to share between systems and easier and enables next-gen methods like the digital twin. Engineering organizations can move faster and cheaper than ever possible before.
• Decision support: Digitized data is, of course, “machine-actionable”, meaning companies can feed it into all kinds of algorithms. Leading engineering departments are already benefitting from this by feeding their digitized data into decision support systems, generative AI solutions, or even production-planning software, to automate key management steps.
• Real-time Insights: Big data analysis has made it easy to extract insights from large volumes of data. It is useful if organizations can standardize their data and keep it in a single location. Engineering organizations can access impactful, real-time insights that can have a huge impact on the bottom line.

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The World Economic Forum (WEF) reckons that digitization has the potential to create upwards of $1 trillion in value for oil and gas firms alone. The biggest obstacle the WEF sees is integration and preparation. If you're not implementing digitization and integration efforts, this is value that flows to more proactive competitors.

How to digitize your data

But even with all these benefits behind them, the sheer effort of digitizing and integrating all a company’s engineering data can seem prohibitive. Especially when it works with a huge amount of paper-based and unstructured data.

Luckily, the cost and risk involved aren’t as high as they might seem. Because new, AI-enabled digital solutions can help. These enable to streamline the “digitization part” and can support the integration of digital data into existing systems, too.

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Traditional digitization efforts need specialists to manually enter data. This is a mind-numbing task that's costly and time-consuming to complete at scale.

For example, digitizing a piping and instrument diagram (P&ID) involves not only creating a list of assets with their respective identification tags. It is also looking at all the piping codes and interpreting them for pipe sizes, service, metallurgy and other details. You may also need to record the physical location or other attributes of each asset.

That’s why more and more successful engineering organizations are relying on AI-enabled digitization tools and highly targeted, step-by-step approaches which cut the costs of transition and maximize long-term value.

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Breaking down the silos: Integrated systems and data empowers companies to maximize value. Click on the Image to find out more.

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Computer vision and machine learning are core to these efforts and automate much of the process with far less human intervention. After certain ML models have been trained to “understand” scanned plans and schematics, the process of turning such documents into machine-actionable data sets becomes more scalable than manual data entry for large organizations.

For example, these technologies can identify objects in the P&ID and predict the asset type, as well as extract any tags and associate them with the relevant asset. You don't need to use a dedicated specialist's time to read through each P&ID and manually enter the data—AI/ML algorithms can do these same tasks in a fraction of the time.

How outsourcing can help

Accenture uses these new technologies to provide Engineering Data Digitization (EDD) as a service with specialist skills available on demand.

Clients rely on this offering to outsource their data digitization efforts so they can pursue specific value drivers with maximum efficiency and quality without binding their own engineering experts. Depending on the specific business case, this outsourcing approach can drive extraordinary value.

The process involves a few steps:

• Accenture experts collect and categorize documents based on their type and then scale them with the required quality parameters. The documents are then uploaded to our proprietary Engineering Data Digitization platform.
• Then, operation analysts set up a new analysis by uploading the documents and training the AI/ML model to recognize the relevant attributes and classify assets.
• Next, the platform has advanced algorithms to run an image quality check and send rejected documents back for rescanning. The remaining documents are then analyzed by the AI/ML model which recommends the best match options along with two near matches for validation.
• Finally, operation analysts check and either confirm the AI’s asset classification from the available options or override the possibility. The documents are then validated against the original documents, and a completed CSV file is delivered.
• The CSV can then be used to feed data in any kind of platform or app.

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While the process of data digitization has been costly and time-consuming, artificial intelligence and machine learning algorithms can now automate much of the process and make it much faster and easier—if they’re used the right way, and with the right models.

Accenture provides a hands-off way to outsource digitization and integration of engineering data by combining new technologies with operational expertise. That way, engineering organizations can focus on their core workflow without diverting their specialists' time toward manual data entry and other tasks that technology can better handle.

Robert Hopkin

Rob is a utilities industry specialist focused on digital transformation across the value chain at Accenture. Get in touch with him via LinkedIn.