A data quality book can be the solution to many problems in decision making, procedural performance or overall business balance. The data quality in a company affects productivity, business integration and sustainability. As revealed Gartner in one of his studies about it, Poor data quality is the main reason why the 40% of business initiatives fail to achieve estimated benefits. Perhaps it would not hurt to obtain some extra knowledge through the 5 readings that we propose: the 5 books on data quality more useful.
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A book on data quality
Arkady maydanchik is an enthusiast of the data quality. Speak passionately about the subject and write clearly and simply, facilitating the understanding of the content. Reading his work is entertaining and the approach is very attractive, following the thread of a comparison between the passage from the stone age to the bronze age, which equates to some of the great challenges we face today in the information age. .
Data quality assessment is an essential book with a clear practical orientation and based on the vast experience in the field of data quality by its author, Arkady Maydanchik. On its pages you will find all the information you need to:
Understand, correct and prevent data quality problems. In the organization.
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Identify and analyze data errors.
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Implement data profiling techniques.
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Know how to interact with metadata in a quality environment.
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Design of data quality rules.
The best guides on data quality
There are several manuals and some data quality book that, for its practical approach and the clarity of its presentation, facilitate understanding of the topic. This is the case of Data quality. The essential factors in an organization, that enables you to discover:
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How to measure data quality.
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What are the consequences of poor data quality in a company?
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How to design a quality strategy.
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What are the benefits of using Data quality tools.
In a complementary way, it may be interesting to expand knowledge with these two guides where the quality of the data is also discussed:
– Data integrity. The strategic value of integration.
– ETL processes. The foundation of business intelligence.
More reading on data quality
apart from data quality book proposed, your reading can be complemented with other points of view, that are contributed by the following works:
1. Assessment and improvement of data quality, by Brenton Crozier.
The main value of this work is to be able to put into practice many theories in the field of data quality, illustrating them with well-selected examples that provide a systematic approach.
Its reading will allow you:
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Examine the capabilities of an information system.
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Manage the quality of the data through the methods it proposes.
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Discover new alternatives to improve the quality of data in the company.
2. The data asset. How Smart Companies Rule Their Data for Business Success, por Tony Fisher.
A necessary job to take full advantage of the data governance functions in any organization and that, at the same time, it cannot be stated in a more practical way. It is data quality book change the way you understand the discipline and approach your interaction with business information. His reading encourages proactivity and he sits down to class with some of his explanations, which could be categorized as principles of action.
When you have finished reading it you will know:
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What works and what doesn't in terms of data quality.
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You will know the best practices in information management.
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You will discover real cases through examples of companies from different sectors and different systems.
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You will notice, more clearly than ever, from Influence of data quality on business decision making.
3. Data quality. Precision dimension, by Jack E. Olson.
It is data quality book seeks to become a benchmark with respect to the data profiling techniques that it proposes, it's a great support for ETL developers and his advice to build a data warehouse has been recognized even by Ralph Kimball himself, who proposes this work as recommended.
4. Business knowledge management: the data quality approach, by David Loshin.
It differs from other proposals due to its business orientation, that is enriched by providing real details on the costs of low-quality information. As stated in its title “Business knowledge management: the data quality approach”, the author seeks to frame the data quality in an economic framework, making it easier to interpret your more business-oriented perspective. One of its objectives is exactly that institutions know how to take advantage of the benefits of their most critical asset: information.
Your reading adds great value in the following areas:
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