Where to Start with Data Quality

Blog Administrator | Analyzing Data, Analyzing Data Quality, Data Cleansing, Data Quality | , , , ,

By David Loshin

In one of my previous posts I noted that there were two common questions about a data quality program. In my last post we dealt with the first about developing a business justification, so this week we will look at the other one.

I believe that there are three key tasks that must be done at the beginning of a data quality initiative, all intended to help laser-focus the plan to best address the most critical needs:

  1. Solicit data quality requirements from the business users.
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Validation of Data Rules

Blog Administrator | Address Quality, Address Validation, Analyzing Data, Analyzing Data Quality, Data Profiling, Data Quality | , , , ,

By David Loshin

Over the past few blog posts, we have looked at the ability to define data quality rules asserting consistency constraints between two or more data attributes within a single data instance, as well as cross-table consistency constraints to ensure referential integrity. Data profiling tools provide the ability to both capture these kinds of rules within a rule repository and then apply those rules against data sets as a method for validation.
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Cross-Table Integrity

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By David Loshin

One of the most challenging data flaws that appear in relational database systems is the absence of referential integrity across different tables. For example, consider a transaction processing system in which one table captures the quantity and total costs for purchased items, and each record refers to a product reference code that can be looked up in a product master table.
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Improving Identity Resolution and Matching via Structure, Standards, and Content

Blog Administrator | Analyzing Data, Analyzing Data Quality, Data Cleansing, Data Enhancement, Data Enrichment, Data Integration, Data Management, Data Profiling, Data Quality, Duplicate Elimination, Record Linkage | , , , ,

By David Loshin

One of the most frequently-performed activities associated with customer data is searching – given a customer’s name (and perhaps some other information), looking that customer’s records up in databases. And this leads to an enduring challenge for data quality management, which supports finding the right data through record matching, especially when you don’t have all the data values, or if the values are incorrect.
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Key Trends in Data Quality

Blog Administrator | Data Quality | , , , , , ,

By Elliot King

“More people are doing it for more reasons.”
“If you learn how to do it, you will do it better.”
“And most companies have a long way to go.”

Those are the most striking trends in data quality, according to several
studies conducted by market research companies over the past several months.

Let’s start this tour of recent research with Gartner’s latest Magic
Quadrant report on data quality vendors.… Read More