AI Magazine June 2023 | Page 43

AI / ML

“ Fortunately , software solutions providers are leading the charge in innovative data quality tools and techniques that help businesses identify and fix data quality problems quickly and efficiently ,” he says . “ However , there ' s still much work to be done . In a recent survey , 97 % of the people Talend surveyed indicated they face challenges in using data , and their top concern is ensuring data quality , coming in first with almost half of all respondents ( 49 %). That means there ' s a massive opportunity for improvement - and the rewards can be huge for businesses that get it right .”

Data quality workflows of the future According to a report by Gartner , by 2025 , at least 50 % of all data management tasks will be automated . Most will be completed using AI / ML-powered automation , such as generative language models , so it ' s time for old-fashioned data management techniques to move aside , as ChatGPT and other generative language applications promise to shake the market up .

From content creation to development task automation , these technologies are already making waves in the business world – and their impact on data management and data quality initiatives is , quite frankly , exciting .

“ By automating and simplifying data management tasks like never before , these technologies promise to revolutionise how organisations handle their data ,” Pelosi comments . “ The prospect of nextlevel automation and efficiency means it ' s easier than ever for businesses to ensure their data ' s accuracy , completeness , and consistency . Let ' s take the example of a data quality workflow .

“ First , a technical data quality assessment is conducted using machine-learning algorithms to identify anomalies and

DAVIDE PELOSI MANAGER , SOLUTIONS ENGINEERING , TALEND

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