Insights Blog | Pimsoft

Workflow Planning in Oil and Gas: Why Data Quality Is the New Competitive Advantage

Learn how high-quality process data – achieved through mass balance and data reconciliation – drives digital refinery transformation. This Q&A with Sigmafine case studies (Astron Energy, etc.) reveals how better data quality reduces losses, boosts profitability, and accelerates digital initiatives in oil & gas. Discover the strategic value of process data quality and how to get started on your refinery’s digital transformation.

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How Chemical Companies Are Turning Messy Data into Actionable Insights

Chemical producers struggle with messy, siloed data. Learn how “data conditioning” transforms raw data into reliable insights for better decision-making.

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Data Conditioning

Discover how data conditioning transforms raw process data into valuable digital assets for advanced analytics and digital transformation. Learn about cleansing, validation, reconciliation, and the role of infonomics in creating high-quality datasets that drive operational excellence in process industries.

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Transforming Data into Digital Assets

Being digital refers to data and information stored digitally. Being an asset refers to having control of the data and information to produce positive economic value. Being a digital asset consequently refers to data & information we own and control and from which we can generate sustainable and tangible benefits. This has been the job of Sigmafine and its users for almost three (3) decades now.

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COTS vs. Custom Development Pros & Cons

As a solution provider for the OSIsoft PI System, we are often asked about commercial off the shelf software (COTS) vs. custom development. It is a good question. Here are our thoughts:

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Tolerance for bad data

Whether it is called “Industry 4.0”, “Edge Computing”, “Big Data” or “IOT”, the tolerance of People, Applications and Business Processes to poor data quality in industrial plants is diminishing rapidly. The Modern process industries thrive on readily usable information and credible data to deliver sustainable tangible business results.

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What is the Industrial Plant Dataset

The “Industrial Plant Dataset” is a complex amalgam of synchronous and asynchronous data types and data sources which must be collected, checked, structured and organized to service the business and operational scenarios of users, applications and business process.

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Data Quality, a two-sided Benefit Model

The business case for improving Data Quality is blessed by a two-sided benefit model: downside avoidance & upside realization. Data Quality professionals have a rule which they call the “Rule of 10”: “It costs 10 times as much to complete a unit of work when the input data are defective as it does when they are perfect”.

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Defining Data Quality in the Process Industries

Data Quality is an abstract concept until we are confronted with bad data or information which is not usable, not credible, not presented correctly, not accurate, etc.. Then Data Quality becomes a very concrete experience. While, there are many definitions for Data Quality, all of them converge to the same focal point. “fitness for use” by people, applications and business processes.

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Trusting Data for Action

In process and manufacturing, information systems should be implemented with a system of checks and balances to maximize the quality of data and the economic potential of data. Creating an environment where data can be trusted is not an afterthought. Data quality, similar to product quality, needs to be built into our business and data management activities so that we can trust data for action at all times.

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