AI Whitepaper

An AI-Driven Approach to Data Quality

In this AI Whitepaper we cover a real-world application of how Machine Learning can be used to improve consistency in entity resolution and reduce manual intervention by up to 45%.

Topics covered in this whitepaper

Data Quality as a Foundation for Machine Learning

"Poor data quality can cost an institution on average $15m annually...only 12% of companies apply data-driven intelligence to inform business objectives..."

An Overview of AI and Machine Learning

"These mathematical models learn from large volumes of data, identify patterns...and use this knowledge to make predictions..."

Machine Learning in Entity Resolution

"By selecting a confidence threshold of 0.9 and above, we can reduce the manual review by 32% and save over 3 days of manual review..."

In this AI whitepaper we provide an overview of Artificial Intelligence (AI) and Machine Learning (ML) and their application to Data Quality and Entity Resolution. We highlight how tools in the Datactics platform can be used for key data preparation tasks including cleansing, feature engineering and dataset labelling for input into ML models.

A bit about the author

Dr. Fiona Browne is Head of Artificial Intelligence at Datactics with over 15 years’ research and industrial experience.

Prior to joining Datactics, Fiona lectured in Computing Science at Ulster University teaching Data Analytics and undertaking research on applied artificial intelligence and data integration. She was a Research Fellow at Queen’s University Belfast and a Senior Software Developer at PathXL. Fiona received a BSc (Hons.) degree in Computing Science and a PhD on Artificial Intelligence in Bioinformatics from Ulster University.

Fiona Browne, AI
Dr Fiona Browne, Head of AI

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