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There have been a number of reports recently on the topic of Data Quality. I'm not sure if it's the time of year, or perhaps that it's simply a perennial topic, but this article caught my eye.

Gartner is kind enough to throw out some large numbers, provide a some glittering generalities for *creating* a case for it, and encourage you to visit their website for more details... We're less about *tips* and more practical advice... and a proven path. Based on Gartner's data, I would anticipate that if you (or I) went into an average organization, informed them that they have a $9.7M financial impact from poor Data Quality, and announced that we had a solution guaranteed to fix it for $9.7M (i.e. 100% ROI in 1 year)... we would be escorted out of the building, and thrown to the curb.

So then, data quality isn't just simply about dollars. At Datawa,re, we think it's more of a lifestyle, and that lifestyle is supported by technology that reduces the friction in identifying data quality issues, and subsequently, resolving them.

Here's how we facilitate improved data quality within Datawa.re community.

We believe in Source, Business Logic and Data Model transparency. Many organization have a litany of SSIS packages, stored procedures, scripts, etc. sprinkled "here and there" throughout their environment, making it tough to identify exactly where a problem is, and impossibly frustrating and time consuming to diagnose, let alone, fix it. 

On the contrary, Datawa.re's architecture is 100% table-driven. This means that the SQL pulling data from source systems, business logic transforming data, exception logic identifying issues, data definitions, etc. are all stored and cataloged together in a collection of tables, managed by our app, in your SQL Server environment. Further, Datawa.re's audit logs provide atomic-level detail and system messages as to the status of every element of your ETL process.  As a result, reports can document data lineage from the moment it enters the ETL process, throughout every transformation, and into the data mart. So, identifying where the problem happens is literally turn-key.

Once the problem has been found, how easy is it to fix and test? This is where Datawa.re again differentiates itself. Because it has a table-driven architecture, you can make changes in the Datawa.re app with the confidence, knowing that it won't impact other things in the ETL process. And, because these changes can be immediately implemented and run discretely, you can apply updates immediately... or, roll-them back without issue.

While you've likely never heard of our organization, you'll be familiar with companies we've built data solutions for, including Yahoo!, Viacom, Time Inc., Keihin, Sallie Mae, etc. 

Visit our customer engagement page (www.datawa.re/customers), and see how we help organizations like yours accelerate and streamline SQL Server data warehousing and processing initiatives, and dramatically improve their data quality and their level of confidence, and ability to respond, to changing business needs.

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