Key Takeaways By deploying technologies that can learn and improve over time, companies that embrace AI and machine learning can achieve significantly better results from their data quality...

Key Takeaways: Trusted AI requires data integrity. For AI-ready data, focus on comprehensive data integration, data quality and governance, and data enrichment. A structured, business-first approach...

Key Takeaways: Data integrity is essential for AI success and reliability – helping you prevent harmful biases and inaccuracies in AI models. Robust data governance for AI ensures data privacy,...

Businesses around the world are facing major challenges due to higher manufacturing costs, disruptive new technologies like artificial intelligence (AI), and tougher global competition. This means...

Key Takeaways: Data democratization is a process that gives virtually anyone in your organization the ability to understand data for better decision-making. Data democratization has immense benefits,...

How compliant is your organization with the GDPR (General Data Protection Regulation) requirements that keep personal data only as long as needed for the purpose it was collected?  How easily could...

Key Takeaways Data quality ensures your data is accurate, complete, reliable, and up to date – powering AI conclusions that reduce costs and increase revenue and compliance. Data observability...

Confidence is everything. In successful businesses, it comes from the ability to make clear, timely decisions based on trusted data. But data is complicated, and 84% of CEOs are concerned about the...

Each of the following data mining techniques cater to a different business problem and provides a different insight. Knowing the type of business problem that you’re trying to solve will determine...