Why Data Quality Should Start At The Beginning, Not The End

Data quality issues emerge from multiple failure points from development practices to production life cycle, each compounding ...
Fast Company

Out of the bad data purgatory: How to solve the data challenges slowing your AI initiatives

The data purgatory hits major technology initiatives like AI projects when obstacles are created by data accuracy, quality, and accessibility. Fast Company has put a spotlight on the make-or-break ...
InfoWorld

Solving the data quality problem in generative AI

It’s no longer how good your model is, it’s how good your data is. Why privacy-preserving synthetic data is key to scaling AI. The potential of generative AI has captivated both businesses and ...
Business Wire

Data Quality is Not Being Prioritized on AI Projects, a Trend that 96% of U.S. Data Professionals Say Could Lead to Widespread Crises

PHILADELPHIA--(BUSINESS WIRE)--Qlik®, a global leader in data integration, data quality, analytics, and artificial intelligence (AI), today released a new survey of 500 professionals working with AI ...
SD Times

The hidden crisis behind AI’s promise: Why data quality became an afterthought

Value stream management involves people in the organization to examine workflows and other processes to ensure they are deriving the maximum value from their efforts while eliminating waste — of ...

Designing A Healthcare Data Strategy That Actually Moves Margin

For healthcare data strategy projects, there are four essential elements to turn aspiration into a plan for recurring contribution margin: value-backed data vision; domain-driven use case selection; ...
Bloomberg L.P.

Need to Know: Data quality

In this episode of Need to Know, our series covering the topics and issues influencing markets and the global conversation, Michael Beal, Co-Head of Enterprise Data Science at Bloomberg, discusses the ...