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ARCHIVE OF QUESTIONS & ANSWERS FOR DATA QUALITY
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Please define data reconciliation and database reconciliation. I also need a definition of data synchronization.
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Could you please provide me with some resources/direction for data profiling tools that will give me a better understanding of the quality of my data?
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I'm trying to make a benchmarking about data quality software. What are the most important characteristics to compare?
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It has been suggested that we can skip user acceptance testing of the Dimension tables in order to speed up implementation. This is regardless of whether the tables are new or existing. I am adamantly opposed to the idea. I would appreciate your thoughts on the subject.
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If a piece of information traverses several applications before placement in the DW is the data quality of that information more in question than data that arrives directly from the operational source? If so, is there research that supports this?
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If a given DW field/element/column can be updated from several operational sources, it would seem that the risk of erroneous data is heightened. Is this true and if so is there an authoritative source that I can quote?
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According to Mr.Inmon, data cleansing is most important phase for data warehousing. He says that it's almost 60 percent of the whole DWH project. Do you have any special methodology to handle the data cleansing process?
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Is there a "known" list of data quality principles?
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As part of a corporate-wide, one-instance implementation of ERP (SAP R/3) we are rethinking master data maintenance and data quality ? both the process and how to organize to implement the process. Initially, it would seem major changes are needed especially in the areas of customer, material and related financial data. I am familiar with Larry English's work. What other sources are there to direct our efforts? Any body of knowledge of real-life lessons learned, etc.?
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We are working with a data warehouse project where we extract data from the source (ETL) and create fact tables and dimensions depending on them. Actually, the end users would query our cubes using their UI Cognos. I need quality assurance and I need to test the complete application. Is there any tool available which can be useful to my case? If not, how should I approach the problem at hand of quality assurance?
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It seems that there is very little academic research done in the data warehouse area in spite of the need for this by the business community. What areas in data warehousing can benefit from academic research?
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What kind of tools can you recommend to perform data auditing and quality analysis?
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Can you please direct me to a resource that touches on how you can place value, preferably financial value, on data, intellectual property and information quality management?
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My question is what is "recommended" related to the hardware configuration for a "typical" production environment. In the client/server development arena, I am used to the classical Production, Quality, Development environments with versioning etc. However, since we are "fixing" so many data integrity issues getting the data correct in "one" environment [i.e., production] is challenge enough. What is typical? Are there research sources you can point me to?
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Data quality is a critical issue affecting data warehouses, (DWs) yet there appears to be no sustainable solution with regard to quality input into operational systems that ultimately feed DWs. Is there any evidence to suggest tying bonuses or other forms of incentive to pre-determined data quality hurdle rates improves data quality at the input end, thereby alleviating some of the dependency and additional expense of a DW data cleansing tools?
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Could you please give me an idea of the typical range on a per record basis for a deduplication(data cleansing) project?
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When we are cleaning data from a relational source, and some values cannot be cleaned. Should it be put in an exception/error table? What do you suggest the structure of this table be?
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Can you please provide me with articles/white papers that discuss the quantified benefits of the application services like enterprise information portals, data cleansing and data duplication?
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Should data be changed in the data warehouse and/or data mart making it different from the source system it came from? What are the pros and cons of making the change in the data warehouse/mart vs. the source system?
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Where can we find information about the types of tests to apply in the databases of the data warehouse?
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Is there a rule of thumb that would give me a 95 to 99 percent confidence level that the data in our warehouse is correct?
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Do I need a data warehouse to do data mining?
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We are interested in measuring the quality of the data populated in our data warehouse. Are there publications out there from which we may borrow some ideas?
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How can we justify the startup cost of a data warehouse infrastructure (for migration, transformation, administration tools, reporting technologies and OLAP tools)?
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What are the quality edits and procedures for loading warehouses and verifying data before releasing to the business groups?
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Could you let me know how to tackle such large data warehousing design issues?
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What is the typical rate within data warehousing for promoting changes that effect transformation and data cleansing for existing subject areas that are in production?
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Where can I find a product that extracts data from any platform, cleanses the data and reformats it in a consistent manner?
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