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.

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

Gaining funding for data quality projects is increasing as a challenge, though data quality is vital to data-driven enterprises. The survey revealed that cloud complicates, not eases, data quality ...

Data quality rules refer to programmatic checks implemented in the REDCap project that are executed in real time during curation. The data quality rules aim to identify missing data and any impossible ...

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

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 ...

Innodata was able to grow revenues from its other customers while showing robust operating leverage. Learn why INOD stock is ...