Data Quality Assessment - Global Intelligence Blog

Discover Data Quality Issues Before they Arise

Blog Administrator | Data Audit, Data Cleansing, Data Enhancement, Data Enrichment, Data Matching, Data Profiling, Data Quality, Data Quality Assessment, Tips & Solutions | clean data, data, data profiler, data quality, Profiler

By Taky Djarou, Data
Quality Analyst

Melissa has
released its new data Profiler API. The Profiler Object offers a unique
approach to profiling your data, combining years of contact data quality
experience, the power of many Melissa Objects, and data source tables to
help you dig deeper into your data and return hundreds of properties about the
input table, columns and individual values.… Read More

Better Marketing Starts with Better Data

Blog Administrator | Address Verification, Analyzing Data, Analyzing Data Quality, Big Data, Big Data Blend, Data Cleansing, Data Governance, Data Integration, Data Management, Data Matching, Data Profiling, Data Quality, Data Quality Assessment, Global Address Verification, MDM | alteryx, Business intelligence, data analysis, global address verification, improve data quality, melissa, predictive analysis

Improve Data Quality for More Accurate
Analysis with Alteryx and Melissa

Organizations
are under more pressure than ever to gain accurate contact data for their
customers. When your consumer base ranges from Los Angeles to Tokyo, it can be
challenging. Poor data quality has a critical impact on both the financial
stability as well as the operations of a business. Verifying and maintaining
vast quantities of accurate contact data is often inefficient and falls short
of the mark.… Read More

Data Quality Dimensions Can Raise Practical Concerns

Blog Administrator | Analyzing Data, Analyzing Data Quality, Data Quality, Data Quality Assessment | data quality assessment, data quality standards, duplication, Melissa Data, up to date data

By Elliot King

As everybody knows, data quality is usually measured along seven dimensions–the four Cs of completeness, coverage, consistency, and conformity plus timeliness, accuracy and duplication. And the general method to judge data quality is to establish a standard for each of these dimensions and measure how much of the data meets these standards.

For example, how many records are complete; that is, how many of your records contain all of the essential information that the standard you established requires them to hold?… Read More

More About Data Quality Assessment

Melissa Team | Address Quality, Analyzing Data, Analyzing Data Quality, Data Management, Data Profiling, Data Quality, Data Quality Assessment | column values, data profiling, data quality measurement, data quality rule, data quality standards, data values, Melissa Data

By David Loshin

In our last series of blog entries, I shared some thoughts about data quality assessment and the use of data profiling techniques for analyzing how column value distribution and population corresponded to expectations for data quality. Reviewing the frequency distribution allowed an analyst to draw conclusions about column value completeness, the validity of data values, and compliance with defined constraints on a column-by-column basis.

Data Quality Assessment: Value Domain Compliance

Melissa Team | Analyzing Data, Analyzing Data Quality, Data Profiling, Data Quality, Data Quality Assessment, USPS | compliance, data profiling tool, Melissa Data, reference data sets, value domain

By David Loshin

To continue the review of techniques for using column value analysis for assessing data quality, we can build on a concept I brought up in my last post about format and pattern analysis and the reasonableness of data values, namely whether the set of values that appear in the column complies with the set of allowable data values altogether.