What is HQ Data?
The problems with data quality don’t only begin with incorrect data; data that is inconsistent is a problem as well.
Data is generally considered high quality if it is “fit for [its] intended uses in operations, decision making and planning”. Moreover, data is deemed of high quality if it correctly represents the real-world construct to which it refers.
To ensure data quality companies are investing huge amounts of resources and time to make data useful. Some of the processes to convert raw/dirty data into quality data include data cleansing, standardization, profiling, and matching.
To be considered ‘High-Quality’, data must meet the attributes of:
- Accuracy and Precision
- Legitimacy and Validity
- Reliability and Consistency
- Timeliness and Relevance
- Completeness and Comprehensiveness
- Availability and Accessibility
- Granularity and Uniqueness
Tess patented technology harvest clean, pure data from any source connected to a network. We match these attributes and eliminate the data cleansing phase + add value to the pipeline by including data preparation processes, to produce the purest and most reliable outcome for smart data analytics that buyers can use effectively: doing more with less time and resources.