Data Quality

Problem

• Mine sites typically have limited ability to assess their data quality
• Time is often wasted dealing with multiple versions of the same data circulating in various systems and spreadsheets
• Mine sites are amongst some of the most challenging operating environments and data can often be lost when sensor are damaged or go out of calibration
• Paper based processes compound the problem as the data never finds its way into an electronic format from which additional insights and value could be generated
• Dealing with localized issues such as instrumentation malfunctioning and data errors

Solution

• brains.app combines and relates available data sources into a single data lake usable for holistic reporting and process improvements
• Automatic ingestion and manual upload from sources is available and decreases occurrences of errors introduced by data movements from spreadsheet to spreadsheet, as well as direct input through the interface enabling digitalisation of paper based data by operators
• Data quality is reported in the system to help identify data issues and ensure that models are applied to high quality data that generates robust outputs and recommendations

Value

• Enable better decision making in mine and plant processes through the use of a single consolidated source of trusted data, including previously unavailable data captured on paper
• Advanced warning for sensor drift, calibration needs etc. Gain insight into the process at a more granular level, which allows site management to respond to issues in real-time