Insights Blog

Build carbon trust with Sigmafine—ensuring transparent CO₂ tracking and MRV compliance across every step of carbon capture, transport, and storage.

Carbon Capture, Utilization, and Storage (CCUS), data reconciliation, MRV (Measurement, Reporting & Verification), energy transition, and digital transformation in oil, gas, and chemicals.

Sigmafine Express is a cloud-based industrial software solution for data reconciliation and data conditioning in Oil & Gas, Chemicals, Petrochemicals, Metals & Mining and other industries with complex production processes. Learn how this data validation, recociliation and conditioning tool on AVEVA CONNECT ensures trusted, reconciled process data with minimal on-premise infrastructure.

Workflow Planning in Oil and Gas: Why Data Quality Is the New Competitive Advantage  Q: Why is high-quality process data critical for refinery workflow planning?   A: In oil refining, missing or inaccurate data comes at a high cost. Without trustworthy process data, planning and decision-making suffer – leading to unaccounted losses, regulatory compliance risks, and […]

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

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.

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.

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:

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.

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.

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”.

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.

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.