Data Management
Big DataData quality in healthcare: Current problems and possible solutions
This article gives a detailed introduction to data quality and the current status it has in healthcare.
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This article gives a detailed introduction to data quality and the current status it has in healthcare.
Data models are used to represent real-world entities, but they often have limitations. Avoid these common data modeling mistakes to keep data integrity.
Ingesting large volumes of disparate data can yield a rich source of information — but it's also a recipe for data chaos. Use these tips to improve data quality as your data lake grows.
Discover the top trends for data integration in 2022, including data accessibility, integration tools and process improvement.
Jack Wallen compares two heavy hitters in the form space, Jotform and Google Forms to discover which service is best suited for which users.
Data Ladder performs data quality reviews as a service to ensure your data is clean, complete and accurate. Discover more now.
IBM InfoSphere QualityStage provides tools to manage the quality of your information throughout the data lifecycle and improve your business processes. Read more about this solution now.
Data quality management is important for enterprise data accuracy and integrity. These frameworks can help you identify and fix problems before they impact your business.
Data cleansing is a process by which a computer program detects, records, and corrects inconsistencies and errors within a collection of data.
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?
This data integration tools list features some of the best-known and most powerful applications. Learn more about the top data integration tools of 2022 now.
Most projects benefit from having a data model. This article gives an overview of the most common types.
Identify which data modeling tools are right for your business. Discover the top tools of 2022 now.
Data quality refers to the accuracy, completeness and consistency of the information in an enterprise database. Discover the top 10 benefits of having data quality in your organization.
Data integration is a crucial aspect of workplace efficiency and maintaining compliance. Learn how data integration tools provide greater visibility and control.