As a result of ongoing data quality challenges, projects are taking longer to complete, decision-making is delayed or flawed and resources are being wasted. This is not sustainable.

C-level executives should also ensure their AI strategies and business goals are well-aligned. Whether the objective is to ...

Ataccama ONE and Precisely Trillium are data quality tools that streamline the data cleaning process. Decide which one is right for you.

They encounter countless articles explaining why these projects fail—poor data quality, unrealistic expectations, lack of focus on ROI, talent shortages and more.

Inadequate data strategy, poor governance and a lack of collaboration between teams all contribute to companies relying on poor-quality data.

Data quality initiatives are not wide­spread enough to enable consistent qual­ity, and the discovery of issues is often through informal processes. The greatest risk factors to data quality come from ...

DQM is a core capability for organizations that need to make better data decisions. What are the responsibilities of different roles in DQM?

Solargis' Marcel Suri reports on the mixing of datasets in solar project planning to artificially enhance financial ...

There may be no client relationship management project quite as challenging as data migration and de-duplication. Three law firms tackled these issues and learned valuable lessons while rolling ...