Download White Paper
Data Fitness™: The Quality-as-a-Service cloud-based solution. Poor data quality costs the US economy alone an estimated $3.1B/year and is directly responsible for loss of life. With the massive increase in data sources from numerous sources, the need to quantify geospatial data quality has become paramount. Apply optimized statistical sampling to review your data in an unbiased and repeatable manner to find hard-to-measure data quality errors of omission, commission, and attribute accuracy. Evaluate, score and quantify your geospatial data's fitness-for-use™.
Geospatial data is an abstraction of reality and as such there will always have some semblance of error in the data. There are well accepted criteria for evaluating the spatial accuracy of the data but there is no standardized method for evaluating the softer aspects of quality such as errors of omission, errors of commission and attribute correctness and completeness. What is your geospatial data good for? How does the quality of a new data set compare to that of an existing one? Don’t guess - use Data Fitness to measure the quality of your data.
Standards exist to assess the relative and absolute accuracies of geospatial data but there is no standard for assessing the quality of data - until now. Data Fitness is a Quality-As-A-Service offering that delivers a quality diagnostic for data that helps evaluate and quantify the data's fitness for use. Our Map Tolerance Percent Defect (MTPD)™ diagnostic quantifies errors of omission (things missed), errors of commission (over collected), and attribute accuracy which allows you to then compare one data set to another - particularly valuable when new data is pouring into your enterprise. Now you can clearly answer which data set is better and by how much. In addition, it offers the ability to categorize data by criticality and do 'What If' adjustments to see the impacts of acceptance against a given use case.
Data Fitness is a cloud-based software product that uses unbiased and repeatable statistical sampling to systematically set targets, measure your dataset's results and perform what-if scenarios on what should be adjusted to meet your requirements. Data Fitness minimizes the amount of data to be reviewed, charecterizes feature class criticality based on a use case, tracks the lineage of review and modifications as metadata, and provides a score that justifies the data’s fitness for use.