Data Quality for Analytics Using SAS, by Gerhard Svolba

Data Quality for Analytics Using SAS, by Gerhard Svolba

Data Quality For Analytics Using SAS, By Gerhard Svolba. Reading makes you much better. Which says? Several wise words claim that by reading, your life will certainly be a lot better. Do you believe it? Yeah, confirm it. If you need the book Data Quality For Analytics Using SAS, By Gerhard Svolba to check out to prove the wise words, you could see this page perfectly. This is the website that will certainly supply all the books that most likely you require. Are guide's compilations that will make you feel interested to check out? Among them here is the Data Quality For Analytics Using SAS, By Gerhard Svolba that we will propose.

Data Quality for Analytics Using SAS, by Gerhard Svolba

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Analytics offers many capabilities and options to measure and improve data quality, and SAS is perfectly suited to these tasks. Gerhard Svolba's Data Quality for Analytics Using SAS focuses on selecting the right data sources and ensuring data quantity, relevancy, and completeness. The book is made up of three parts. The first part, which is conceptual, defines data quality and contains text, definitions, explanations, and examples. The second part shows how the data quality status can be profiled and the ways that data quality can be improved with analytical methods. The final part details the consequences of poor data quality for predictive modeling and time series forecasting.

Data Quality for Analytics Using SAS, by Gerhard Svolba

• Amazon Sales Rank: #2686217 in eBooks
• Published on: 2015-05-05
• Released on: 2015-05-05
• Format: Kindle eBook

Data Quality for Analytics Using SAS, by Gerhard Svolba

Review Data quality is the key ingredient for a successful analytics project. In our past research, we have extensively shown that the best investment to boost the performance of any analytical model is by understanding and improving data quality.

In his book, Gerhard Svolba provides comprehensive coverage of the topic by defining data quality first, followed by providing well-articulated guidelines to profile and improve data quality, and concluding with illustrating the impact of poor data quality in predictive modeling and time series forecasting, among other examples. The book is well-structured and written, with lots of practical examples clarifying the ideas presented. In short, I consider the book a must-read for anyone working on developing high-performing analytical models! --Dr. Bart Baesens, Professor, Department of Decision Sciences and Information Management, KU Leuven

About the Author Dr. Gerhard Svolba is a senior solutions architect and analytic expert at SAS Institute Inc. in Austria, where he specializes in analytics in different business and research domains. His project experience ranges from business and technical conceptual considerations to data preparation and analytic modeling across industries. He is the author of Data Preparation for Analytics Using SAS and teaches a SAS training course called "Building Analytic Data Marts."

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Most helpful customer reviews

5 of 5 people found the following review helpful. What are the consequences of low data quality? By M. Denk This book fills a gap in the data quality literature. I very much like the presentation of consequences of low data quality. The book does not only discuss these consequences from a qualitative point of view (such as low trust in results, postponing of projects, ...), it also shows quantitative results based on simulation studies. It is interesting to see the impact of different degrees of data quality on the accuracy of the outcome. Especially the simulation studies for predictive modeling provide much insight into the relationships between data input and model outcome.Read it - and apply the gained knowledge to your data projects!

5 of 5 people found the following review helpful. highly recommended! By C. Hallwirth It is good to finally see a book that focuses on the analytical part of data quality. The author fills this gap and shows what analytics really requests from data quality. I was delighted to see that "data availability" topics like the need for historic snapshots are well discussed also with relation to predictive modeling. The book helps me to explain my data requirements to my counterparts in a much more precise way and brings many convincing examples why data that is usable for simple reporting might still be unusable for analytics.

2 of 2 people found the following review helpful. Data Quality for Analytics - Taking the "vital signs" of data before embarking on an analytical project By Lessia Shajenko Dr. Svolba's new book about data quality for analytics encompasses everything you need to know about the topic as a practitioner / hands-on analyst or an analytic manager. With his signature style to give clear definitions and to present encyclopedic plethora of information in a very organized manner, Dr. Svolba doesn't stop at outlying a solid data quality framework for analytics, but reviews various convincing real-life case studies with quantifiable impact of data quality on the project outcomes. The book offers SAS programs downloadable together with test datasets from the book's website, which is a part of SAS Community Wiki and is continuously updated with new material (a nice bonus to the book)."Diggers" like me may enjoy selecting a fact from the book and researching it deeper to find to their ultimate satisfaction that the author has covered and thought of it all. You may also like rolling up the sleeves and trying out some of the code examples and approaches on your own data. Curiously, while at it, I discovered that option ZEROMISS in ID statement of PROC TIMESERIES is not documented in SAS Help, but it works as described in PROC ESM. You may learn something new or refresh your knowledge even if you have years of experience in data analytics. For example, my current work focuses on text mining, but I used to built ARIMA models using stock prices and currency exchange rates and was glad for an opportunity to read about data quality assessment for forecasting projects. If you've never used SAS HASH object, you may try to use it following the example of Dr. Svolba that checks data completeness and data integrity.To summarize, the more you apply the concepts and methodologies from the book in your own projects, the more value you gain from the book and the more appreciation you have for the author who has done the "leg work" and made information conveniently available for many SAS users to benefit.

See all 4 customer reviews... Data Quality for Analytics Using SAS, by Gerhard Svolba

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Data Quality for Analytics Using SAS, by Gerhard Svolba

Data Quality for Analytics Using SAS, by Gerhard Svolba

Data Quality for Analytics Using SAS, by Gerhard Svolba

Data Quality for Analytics Using SAS, by Gerhard Svolba