Clean Data is Good Data

Blog Administrator | Analyzing Data Quality, Data Cleansing, Data Enrichment, Data Integration, Data Management, Data Quality | , , , , ,

By Elliot King

The cliché is as old as computing itself–garbage in, garbage out. And that cliché is as true now as ever, if not more so. Unfortunately, with information flowing into companies from so many sources including the Web and third-party providers, mistakes should not just be expected; they are basically inevitable. Garbage data is going to get in your data systems.
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Standardizing Your Approach to Monitoring the Quality of Data

Blog Administrator | Address Standardization, Analyzing Data, Data Cleansing, Data Integration, Data Management, Data Profiling, Data Quality | , , , , , , ,

By David Loshin

In my last post, I suggested three techniques for maturing your organizational approach to data quality management. The first recommendation was defining processes for evaluating errors when they are identified. These types of processes actually involve a few key techniques:

1) An approach to specifying data validity rules that can be
used to determine whether a data instance or record has an error.

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Garbage In …

Melissa Team | Data Cleansing, Data Quality | , ,

By Elliot King

We all know that dirty data is not really dirty; it is just incorrect. Data
cleansing consists of correcting mistakes in the data.

Mistakes make their way into contact data in several different ways. It may just
be wrong or incomplete; it may not be updated; and it may be duplicated if small
variations are entered into the contact information each time a customer gets in
touch your organization.… Read More