Data Quality for Analytics Using SAS

The reason I gave it the highest rating was that the subject matter in the book touched basis on every aspect of Data Quality for Analytics. To explain, the material had all the information that I needed, which potential employers were asking for in job descriptions. It used industry terminology that coincided with terms being published in job sites. All of this helped me define and learn what was being asked for and what was needed in order to make me a better canidate for hire. The author has a previous book, which it mentions, that I will be purchasing in the near future. With all this being said, I was more than satisfied with my purchase and the reason I gave it the highest rating.

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.

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!

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.

Ne fonctionne pas ceci est scandaleux... En espérant que le problème sera résolu rapidement.Bien cordialement,Ps : Ceci peut être un problème de Kindle