AI can be used in data profiling
Posted: Tue Feb 11, 2025 5:52 am
Data modeling and architecture: AI can help create business glossaries, document and link logical and technical data models, build data classifications, and integrate and aggregate data from various sources.
Data quality: translating data quality requirements written in business language into data quality checks in technical languages, recommending DQ rules based on data source systems scans, and cleansing data.
Data lifecycle management: AI and ML can optimize data russia whatsapp number data processing and storage of significant data volumes using distributed computing, data compression, and predictive caching techniques. These technologies can be used for real-time processing to ingest, analyze, and act upon data arrival.
Other use cases: Purani predicts the following emerging GenAI use cases: “producing synthetic data helping augment scarce and incomplete data,” “enterprise applications proactively suggesting actions based on historical transaction data,” “encapsulating and modernization of legacy systems code,” “assistance role in managing complex projects,” “improving process workflows,” and “negotiating contracts and optimizing bids.”
AI requires governance.
According to Stephanie Paradis and Gretchen Burnham of First San Francisco Partners, data governance should focus on defining business cases, curating data sets taken for training, controlling syntactic data sets for production, and controlling models’ governance.
Douglas R. Briggs of Daugherty Business Solutions stressed that good governance for AI should “balance support for innovation with risk and impact,” take into account the concerns of a “broad spectrum of interested parties,” “provide clear and effective guidance for practitioners,” integrate with “existing organizational governance,” and “remain flexible and agile to adapt.”
Data quality: translating data quality requirements written in business language into data quality checks in technical languages, recommending DQ rules based on data source systems scans, and cleansing data.
Data lifecycle management: AI and ML can optimize data russia whatsapp number data processing and storage of significant data volumes using distributed computing, data compression, and predictive caching techniques. These technologies can be used for real-time processing to ingest, analyze, and act upon data arrival.
Other use cases: Purani predicts the following emerging GenAI use cases: “producing synthetic data helping augment scarce and incomplete data,” “enterprise applications proactively suggesting actions based on historical transaction data,” “encapsulating and modernization of legacy systems code,” “assistance role in managing complex projects,” “improving process workflows,” and “negotiating contracts and optimizing bids.”
AI requires governance.
According to Stephanie Paradis and Gretchen Burnham of First San Francisco Partners, data governance should focus on defining business cases, curating data sets taken for training, controlling syntactic data sets for production, and controlling models’ governance.
Douglas R. Briggs of Daugherty Business Solutions stressed that good governance for AI should “balance support for innovation with risk and impact,” take into account the concerns of a “broad spectrum of interested parties,” “provide clear and effective guidance for practitioners,” integrate with “existing organizational governance,” and “remain flexible and agile to adapt.”