Justifying Data Quality Management

Blog Administrator | Address Quality, Analyzing Data, Analyzing Data Quality, Data Quality, Data Warehouse | , , , , , ,

By David Loshin

Last week I shared some thoughts about a current client and the mission to justify the development of a data quality program in an organization that over time has evolved into one with distributed oversight and consequently very loose enterprise-wide controls.
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Data Quality Assessment: Column Value Analysis

Blog Administrator | Analyzing Data, Analyzing Data Quality, Data Cleansing, Data Enrichment, Data Profiling, Data Quality, Data Quality Assessment | , , , , ,

By David Loshin

In recent blog series, I have shared some thoughts about methods used for data quality and data correction/cleansing. This month, I’d like to share some thoughts about data quality assessment, and the techniques that analysts use to review potential anomalies that present themselves.
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Get it Right the First Time

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By Elliot King

People generally think of data quality as a remedial exercise. During the ongoing course of business, for a variety of reasons, companies find themselves with incorrect data. The goal of a data quality program is to identify the incorrect data and fix it.
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Achieving “Proactivity?”

Blog Administrator | Analyzing Data Quality, Data Enrichment, Data Management, Data Profiling, Data Quality | , , , ,

By David Loshin

Standardizing the approaches and methods used for reviewing data errors, performing root cause analysis, and designing and applying corrective or remedial measures all help ratchet an organization’s data quality maturity up a notch or two. This is particularly effective when fixing the processes that allow data errors to be introduced in the first place totally eliminates the errors altogether.
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Reactivity vs. Proactivity

Blog Administrator | Analyzing Data Quality, Data Management, Data Quality | , , ,

By David Loshin

In the past few months, we have looked at technical approaches to data quality and the use of data quality tools to parse, standardize, and cleanse data. In this next series of posts, it is time to look at harnessing the power of these tools and techniques to support a data quality management program.
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