Over the years, the term Data Quality has gained tremendous popularity in the SQL Server community. So much so that itís almost become a necessity for anyone storing contact data; which includes addresses, phone numbers, email addresses and names. The fact is, bad contact data is unworkable data. And unworkable data means loss of opportunities, high inefficiencies, and added costs.
As the popularity of SQL Server continues to grow along with the amount of data that is being stored, data quality is becoming a bigger issue. There are basic database techniques that can be utilized, there are some components within SSIS and also the new Data Quality Services, but this is only the beginning of the process.
In order to take control of the quality of your data, it is essential that you have a good understanding of its contents, know the kinds of problems that arise (especially the not so obvious ones) and be able to implement a suitable and efficient solution.
In this session we will discuss:
Joseph Vertido is a data quality analyst at Melissa Data. He is an expert in the field of data quality, working with numerous clients in understanding their business needs, analyzing their architecture and environment, and recommending strategic solutions for how to successfully integrate data quality within their infrastructure. He has also written several articles on implementing data quality solutions and techniques, and is a regular contributor to Melissa Dataís Data Quality Authority blog. Joseph holds a degree in Computer Science from the University of California, Irvine.
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