1.9.Importance of Data Quality

 

Data quality is the completeness of attributes in order to achieve the given task.  Data is one of the most valuable resource. Data created from different channels with different techniques can have discrepancies in terms of resolution, orientation, and displacements. Data quality is a pillar in any GIS application as reliable data are important to obtain meaningful results. Improved data quality leads to better decision-making. If the quality of the data is higher, the more will be the confidence in taking decisions. New technologies are also increasing the importance of data and its quality. Technologies such as artificial intelligence and automation have enormous potential, but success with these technologies depends heavily on data quality.

            Spatial Data quality can be categorized into Data completeness, Data Precision, Data accuracy, Data Consistency, Data Relevancy, Data Validity and Timeliness.

•   Data Completeness: It is basically the measure of totality of features. A data set with minimal number of missing features can be termed as Complete-Data.

•   Data Precision: Precision can be termed as the degree of details that are displayed on a uniform space.

•   Data accuracy: Accuracy refers to how well the data describes the real-world conditions it aims to describe.

•   Data Consistency: Data consistency is the lack of difference between multiple versions of a single data item in a database. A data item should be consistent both in its content and its format.

•   Data Relevancy: The data you collect should also be useful for the campaigns and initiatives you plan to use it for. It’s important to set goals for your data collection so that you know what kind of data to collect.

•   Data Validity: It refers to how the data is collected rather than the data itself. Data is valid if it is in the right format, of the correct type and falls within the right range. If data does not meet these criteria, you might run into trouble organizing and analysing it.

•   Timeliness: It refers to how recently the event the data represents occurred. Data typically becomes less useful and less accurate as time goes on.

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