Data masking is the process of replacing sensitive data with fictitious or anonymized data to protect sensitive or private information and to comply with privacy requirements. Data masking is used in training or testing scenarios when real data is not needed, or when sharing data with third parties. You can also use masking to ensure you’ve eliminated all personal data when writing AI prompts or training an AI model.
What it means for customers: Customers feel more confidence when companies protect sensitive and personally identifiable information.
What it means for teams: Teams can easily follow privacy requirements while still having functional data to use in testing, training, or development.
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Data mining
Data mining is the process of discovering patterns afghanistan phone number list in large datasets. It uses techniques like machine learning, statistics, and database systems to turn raw data into useful information.
What it means for customers: Your customers get predictive recommendations about what they want and need, often before they know they need it. Customized recommendations, reminders, and add-on product offerings are all powered by data mining.
What it means for teams: A deeper understanding of customer behavior keeps all your marketing and sales strategies efficient and effective.
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Data science
Data science is a field that combines scientific methods, statistics, algorithms, and data mining techniques to generate insights from structured and unstructured data.
What it means for customers: Customers experience faster service and improved personalization with data science tools like recommendation algorithms, which provide tailored suggestions and machine learning algorithms that automate specific support tasks.
What it means for teams: Teams use data science to continually improve and iterate on service and product offerings to create more relevant, efficient, and satisfying customer experiences.