Tag: data values | Global Intelligence Blog

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.

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.

The Format of Nothing

Blog Administrator | Analyzing Data, Analyzing Data Quality, Data Governance, Data Management, Data Profiling, Data Quality | data field, data governance, data profiling, data sets, data values, Melissa Data

By David Loshin

The first question I always wonder about missing data is about the format of the missing data, especially in systems that predate the concept of the “system null” value. For example, early systems maintained files storing tables with fixed-width columns.

All or Nothing?

Blog Administrator | Address Quality, Analyzing Data, Analyzing Data Quality, Data Quality | complete data quality, data completeness, data creation, data values, missing data, shipping address data

By David Loshin

One of the most frequently referenced dimensions of data quality is completeness. At a formal level, completeness implies rules specifying mandatory assignment of values to particular data elements. In layman’s terms, that specifies rules to make sure critical attributes are populated with values.

Record Linkage and Fuzzy Matching Part 1

Blog Administrator | Uncategorized | cleansing, cleansing and standardization, data driven rules, data governance, data quality, data streams, data values, match tools, matching algorithums, MDM architecture, SQL, SSIS

This will be the first in a series of posts identifying similar records between two different sources or grouping of records from a single source, based on existing column string of values. We will define an approach, review actual implementations with various tools and vendor’s products.… Read More