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

This paper describes a replicable process for standardizing disparate databases and methods to calculate cost and quality measures within and across states. Objectives: To describe how all-payer ...

The integrity of database systems is increasingly dependent on maintaining high data quality while enforcing robust consistency rules. Data quality encompasses the accuracy, completeness, and ...

eSpeaks’ Corey Noles talks with Rob Israch, President of Tipalti, about what it means to lead with Global-First Finance and how companies can build scalable, compliant operations in an increasingly ...

With more data streaming in from more sources, in more varieties, and being used more broadly than ever by more constituents, ensuring high data quality is becoming an enterprise imperative. In fact, ...

In today’s tumultuous computing landscape, the DBMS (database management system) remains core and central to the IT infrastructure. And, as such, it is central to the applications used by modern ...

Tools that clean or correct data by getting rid of typos, formatting errors, and unnecessary and expendable data are known as data quality tools. These tools help organizations implement rules, ...

As organizations look to adopt the new wave of coming technologies, like automation, artificial intelligence and the Internet of Things, their success in doing so and their ability to differentiate ...

DQM is becoming a core capability for organizations that need to make better decisions with data. What are the responsibilities of different roles in DQM? Image: WrightStudio/Adobe Stock Data quality ...

It can be tough to manage data manually, and doing so can sometimes lead to errors or inefficiencies. Spreadsheets can get overly complex, and data quality can suffer. This has become a large enough ...