Data quality is the area that establishes how the organization will address data quality issues.
Inaccuracy, incompleteness and inconsistence are common characteristics of monitored field data, which then often needs intensive pre-processing to address data quality issues and improve data analysis results.
This chapter discusses data analysis and preparation in great detail and explains what to look for in data and how to address typical data quality issues.
Consequently, a great deal of research effort in academia and industry has been devoted to addressing the data quality issue, especially the data accuracy, latency, and loss issues.
With large scale sequencing projects there are a number of data quality issues that must be addressed, as biases are introduced at multiple levels.
At the same time, a Data Quality Framework was introduced by CIHI and new edit checks were implemented to address incorrect application of coding standards and other data quality issues.
It is acknowledged that this does not take into account adjustments for misclassification, bias and other data quality issues, as these issues were addressed to some extent in the sensitivity analyses, as described above.
Volunteered Geographic Information (VGI) has great potential for enhancing analysts' capabilities, assuming that VGI data quality issues, such as credibility and precision, are properly addressed.
However, our study shows that there are data quality issues and key data gaps that need to be addressed before the model can be further refined, validated and then used for decision making.
It is solving the data quality issues that continue to plague most companies, a problem that will only get worse if companies dont address it seriously today.
