By Gerhard Svolba Ph.D.
Analytics deals many services and techniques to degree and enhance facts caliber, and SAS is ideally suited to those initiatives. Gerhard Svolba's Data caliber for Analytics utilizing SAS specializes in selecting the best facts resources and making sure info volume, relevancy, and completeness. The publication is made from 3 elements. the 1st half, that is conceptual, defines info caliber and includes textual content, definitions, motives, and examples. the second one half indicates how the information caliber prestige might be profiled and the ways in which information caliber will be enhanced with analytical tools. the ultimate half information the results of bad facts caliber for predictive modeling and time sequence forecasting.
With this booklet you'll learn the way you should use SAS to accomplish complicated profiling of information caliber prestige and the way SAS might help enhance your facts caliber.
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Additional info for Data Quality for Analytics Using SAS
These case studies are intended not only to whet your appetite for the data quality topic but also to highlight typical data quality specifics and examples of analyses. Svolba, Gerhard. Data Quality for Analytics Using SAS®. , Cary, North Carolina, USA. ALL RIGHTS RESERVED. com/publishing. 18 Data Quality for Analytics Using SAS A classification of data quality features that were discussed in the case studies follows. This classification is detailed in chapters 3 through 9. Data availability • GPS data were only available for two boats.
This classification is detailed in chapters 3 through 9. Data availability • GPS data were only available for two boats. No wind data were collected. • No recording of the trim setting on the sailboat was done. • Static information on the items to be forecasted was not entered into the system or maintained over time. • Historic data or historic versions of the data (like the static information on the items from 12 months ago) were not available. • Point-of-sale data from historic periods that were captured in the previous operational system could not be made available or could only be made available with tremendous effort.
For the preceding time period, data were stored in different systems that were no longer online. To capture the historic data from these older systems, additional effort in accessing historic backup files from these systems was required. • Another source of potential problems in data completeness was that for some item characteristics, no responsibility for their maintenance and update was defined. This is especially true for data provided in in the form of spreadsheets. Therefore, in the analyses, because it was uncertain whether an updated version of these data would be available in a year, care had to be taken when using some characteristics.