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Scaling Digital Transformation: Challenges and Considerations

Scaling Digital Transformation: Challenges and Considerations

Digital transformation offers opportunities for improved productivity, through projects focused on condition-based maintenance or overall equipment effectiveness. Better visibility into asset health and operations, using analytics, promotes safety. These analytics are often deployed for assets at the local plant level for a handful of use cases.

Existing investments in analytic tools, such as statistical modeling applications, BI reporting, and data science notebooks, make it easy to build new analytics to drive insights. Yet, organizations remain stifled, balancing their priorities between redundant data preparation activities for new projects and complex workflows to maintain existing solutions.

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Connecting Data Sources to create a comprehensive PI Asset Framework

Currently, most OT data systems have their own hierarchical structure on how they organize data. For instance, your organization may maintain separate hierarchies for your work management system, engineering specifications, production deferrals, process historians, and failure databases, just to name a few. This classification is adequate when your data is contained within a single system but fails when data spans multiple systems and organizations.

What if all this data could be unified and harmonized in an asset-centric common data model that provides a consistent organizational structure? What if your PI Asset Framework was synced to your SAP Asset Registry?

Starting today, Element is announcing the general availability of Asset Connect, a module bundled with Element AssetHub™. Asset Connect provides users with the capability to easily connect to their most important IT and OT systems.

What is Asset Connect?

Asset Connect is an integration platform that connects the enterprise asset model management capabilities of AssetHub to producing systems of record.

As an integration platform, building integrations to static text files, spreadsheets, XML files, custom SQL databases, or even IT/OT systems of record takes minutes or hours, not months or years.

Sameer Kalwani Heathcliff Howland
Dec 3, 2019

Sameer Kalwani

Heathcliff Howland

OSIsoft customers know they need Asset Frameworks. But few are deploying them. Why?

OSIsoft customers across a spectrum of industries see a huge need for Asset Frameworks for their PI System data. Despite this, only a small percentage have deployed these capabilities. Companies that are using asset frameworks generally aren’t happy, complaining about the lack of quality and consistency. And few companies – less than one in five – have taken steps to make their asset frameworks more valuable by adding data related to quality, financials and other metrics.

So, what is the hold up on AF Adoption? What are the challenges? More importantly, what are the solutions?

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Welcome, Silicon to the Element Family!

With this release, we introduce a brand new, data-structuring function called Transpose Columns in Element AssetHub’s library of data pipeline transformations.

Equipment data consists of continually-changing time series data as well as static, or slow-changing, data that site engineers often collect and store in a variety of documents and formats. While building a pipeline, you may have several columns of static and non-static tabular data that are not directly compatible with the format required in AssetHub. With the Silicon release, AssetHub now offers Transpose Columns as a powerful data-shaping function for re-ordering this data into an acceptable row-based format, making it easy for AssetHub to consume and use.

Additional new features in this release enhance the user experience when you log in to AssetHub or when you build the Asset data model.

Here are the new features and updates:

  • Transpose Columns transformation for reshaping data
  • Pipeline enhancements
  • Bulk management of templates
  • User control of jobs
  • Security updates

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Software Versioning: Because Dependencies Matter

It’s been a long couple of sprints, but your team has managed to pull together and complete the features promised for delivery. Your unit tests have passed, and you feel confident in rolling your changes out to production. However, there’s a last minute ask to fix a minor bug by updating one of your project dependencies. There’s no change to your code base, and everyone thinks it should be a low risk change, so it’s pulled in last minute. But as soon as the change is included, all the tests start failing, and chaos ensues. After a day of painful troubleshooting, it’s determined that the newer dependency broke backwards compatibility with your application, and your application stopped working as a result. Good version management could have helped you avoid this situation.

Software under active development evolves over time as bugs are fixed and new features are added. Simply releasing every change as soon as it's finished has a high potential of causing chaos. To help keep things simple, changes are usually bundled into a known good build and released together. However, since there are now multiple releases of the same piece of software, it becomes important to know what features and bugs are present in any given release. To distinguish these releases, software teams use various versioning schemes to tag their releases so they can easily communicate to their users what to expect in any given release.

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