Download The Practitioner's Guide to Data Quality Improvement by David Loshin PDF

By David Loshin

  • "There is not anything like this in the market that i'm conscious of, and definitely not anything from a person with comparable stature as David Loshin."--David Plotkin, Wells Fargo Bank

    "The ebook offers a finished examine facts caliber from either a enterprise and IT standpoint. It doesn't simply disguise know-how concerns, yet discusses humans, method, and expertise. and that's very important, simply because this is often the combination that's wanted so that it will begin any form of caliber development regimen."--Data know-how at the present time Blog

Front matter

, Pages i-iii

, Page iv

, Pages xi-xii

, Pages xiii-xix

, Pages xxi-xxii
About the Author

, Page xxiii
Chapter 1 - company affects of terrible facts Quality

, Pages 1-16
2 - The Organizational facts caliber Program

, Pages 17-34
3 - info caliber Maturity

, Pages 35-51
4 - firm Initiative Integration

, Pages 53-66
5 - constructing A enterprise Case and a knowledge caliber street Map

, Pages 67-90
6 - Metrics and function Improvement

, Pages 91-113
7 - information Governance

, Pages 115-128
8 - Dimensions of knowledge Quality

, Pages 129-146
Chapter nine - info standards Analysis

, Pages 147-165
10 - Metadata and knowledge Standards

, Pages 167-189
11 - facts caliber Assessment

, Pages 191-206
12 - Remediation and development Planning

, Pages 207-218
13 - facts caliber carrier point Agreements

, Pages 219-240
14 - info Profiling

, Pages 241-259
15 - Parsing and Standardization

, Pages 261-278
16 - Entity identification Resolution

, Pages 279-297
17 - Inspection, tracking, Auditing, and Tracking

, Pages 299-312
18 - facts Enhancement

, Pages 313-325
19 - grasp info administration and information Quality

, Pages 327-350
20 - Bringing all of it Together

, Pages 351-383

, Pages 385-398

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Extra info for The Practitioner's Guide to Data Quality Improvement

Sample text

S. Postal Service two-character abbreviation of CA. 4 Identity Resolution Because operational systems have grown organically into a suite of enterprise applications, it is not unusual that multiple data instances in different systems will have different ways to refer to the same real-world entity. Alternately, the desire to consolidate and link data about the same business concepts with a high level of confidence might convince someone that a record might not already exist for a real-world entity when in fact it really does.

Because of this, the expected benefits of improved information value can only be achieved when all participants willingly contribute to successful data quality management. Frequently, defined data quality activities largely focus on evaluation and procurement of data quality tools, but won’t encompass the management, technical, and operational infrastructure that must be in place to support a generalized conformance to acceptability levels of properly defined and documented expectations. Yet measuring this conformance demonstrates that the effort is succeeding.

2 Data Quality Requirements Analysis This process is a top-down identification of data quality expectations. At this point, the analysts will synthesize data quality expectations for consumed data sets based on the business Chapter 2 THE ORGANIZATIONAL DATA QUALITY PROGRAM impact analysis. Data sets will be identified and targeted for assessment, and specific dimensions of data quality (see Chapter 8) will be isolated for review. The selected dimensions will be used to list specific measures that will be evaluated in relation to the business impacts.

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