cAse study: IDQI
Automated dataset analysis using Artificial Intelligence
Industries are facing challenges in the handling of ever-increasing quantities of data; the upstream Oil and Gas sector in particular is severely affected by this “data deluge” challenge and is at risk of failing to extract valuable information from their exploration and production data.
So far, numerical data cleaning and curation have been considered as synonyms for numerical data quality improvement, but the latter is much more than simply addressing gaps, outliers, noise and bias in numerical datasets typically composed of time series of some kind.
HyperDap has invested in its Intelligent Data Quality Improver (IDQI) tool to address the issue of digital exploration and production data quality in the Oil and Gas industry, but it is also very capable of addressing similar challenges in many other sectors. IDQI built on novel data quality improvement knowledge and technologies previously devised by HyperDAP and drew on guidance and expertise from academic research staff from the University of Aberdeen.
The IDQI project was sponsored by the Oil & Gas Innovation Centre and The Data Lab.
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