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How can Cloud Data Quality Lead to MDM?

I recently attended a joint panel webinar between Informatica Cloud, StrikeIron, and Address Doctor (a subsidiary of Informatica Cloud) discussing Informatica Cloud’s latest service from its Winter ’12 offering, the Contact Validation Service.

Cloud data integration has really taken off in the past couple of years with the proliferation of SaaS applications, and the need for these applications to access and interact with data stored in on-premise systems. But data integration alone only solves part of the problem. In order to maximize your “Return on Data”, you need to ensure that you’re dealing with high quality and trustworthy data. As Ted Friedman, a Distinguished Analyst from Gartner aptly points out,

“Organizations cannot be successful in their data integration work unless they have a very strong focus on data quality built in. That’s because it’s not only about delivering stuff from here to there. You also have to make sure you’re delivering the right stuff.”

I also learned of the 1-10-100 rule – a Bloor Research whitepaper that states that it takes $1 to verify a record as it is entered, $10 to cleanse and de-dupe it and $100 if nothing is done, as the ramifications of the mistakes are felt over and over and over again.

Contact Validation ensures an abundance of benefits for all departments and industries. Marketing functions can benefit from higher campaign response rates by filtering out leads with incorrect contact information – in fact, a BtoB Online article claims that validating and managing data in the earliest stages of collection can lead to better lead scoring and lift conversion rates by about 25% between the customer inquiry stage and the point where marketing qualifies the leads.

In addition to marketing, warehouse managers can calculate correct shipping costs and legal departments can ensure regulatory compliance. Industries such as Insurance can use geocoding information to check whether properties are near a fault line, flood plain, or landslide zone, while banks can use contact validation in their deduping process.

With 30% of people changing their email every year, invalid emails can trigger bulk email flags, causing future emails to be filtered as spam, and result in the sender being blacklisted by ISPs and spam filters. This is why email validation is so important. The $16,000 per violation fine from the FTC also underscores the importance of phone validation against the Do Not Call registry.

I also learned that Contact Validation fits into the Data Quality spectrum, and is only a small part of it. But before even embarking upon a data quality project, it’s important to first of all integrate all of your data. Then, the next step is to cleanse, enrich, & augment contact data as a first step towards achieiving higher data quality. Once this is done, parsing and standardization, deduping, and finally monitoring and reporting round out the rest of the data quality activities. Moreover, these data quality activities must be done for all data types, and not just contact data.

With data quality processes properly implemented, a company can then finally take it to the next level and develop data governance policies, and hierarchy management for a full-fledged Master Data Management Solution. You can view the full replay of the webinar below.

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