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Data Quality Assessment: Value and Pattern Frequency

Blog Administrator | Analyzing Data, Analyzing Data Quality, Data Profiling, Data Quality, Data Quality Assessment | column value analysis, data entry errors, data profiling tools, data quality, mapping, Melissa Data, pattern frequency

By David Loshin

Once we have started our data quality assessment process by performing column value analysis, we can reach out beyond the scope of the types of null value analysis we discussed in the previous blog post. Since our column analysis effectively tallies the number of each value that appears in the column, we can use this frequency distribution of values to identify additional potential data flaws by considering a number of different aspects of value frequency (as well as lexicographic ordering), including:

Range Analysis, which looks at the values, and allows the analyst to consider whether they can be ordered so as to determine whether the values are constrained within a well-defined range.

Managing Customer Connectivity

Blog Administrator | Analyzing Data, Analyzing Data Quality, Customer Centricity, Data Management, Data Quality | customer records, data quality tools, data values, good quality data, household grouping, mapping, Melissa Data, parse data, record similarity, standardization

By David Loshin

At the end of our last entry, we had come to the conclusion that standardization of potentially variant data values was a key activator for evaluating record similarity when looking to group customer records together based on any set of characteristic attributes. From an operational standpoint, this activity is supported using data quality tools that can parse and standardize data.