The above attributes are ones that differentiate DaaS businesses from more traditional data companies. Startups looking to build sustainable, high-growth companies should heed these critical elements.

If your data warehouse is starting to look like Miss Havisham’s decaying mansion, you may have a data quality problem. A new survey of 500 data professionals from open source data quality tool Great ...

As audience data’s importance continues to grow for media buyers and sellers alike, concerns over quality have dramatically increased. In fact, 84% of CEOs worry about the quality of data within ...

Why Data Quality Is Not One-Size-Fits-All. Applying uniform rules across all data can lead to inefficiencies and missed opportunities. Different GenAI use cases have different requirements ...

As data-centric AI, automated metadata management and privacy-aware data sharing mature, the opportunity to embed data quality into the enterprise’s core has never been more significant.

Data is a cornerstone of strategic decision-making and operational efficiency for businesses across all industries in today’s ...

A clear majority of employees (87%) peg data quality issues as the reason their organizations failed to successfully implement AI and machine learning. That’s according to Alation’s latest ...

Data quality encompasses several attributes that include the parameter measured, its accuracy, precision, date/time stamp, and additional metadata for context. Such attributes assist when merging data ...

Multi-attribute methods are more feasible than in the past due to work by instrument manufacturers to make their technology more accessible to non-specialist users.