Page 41 - MSDN Magazine, August 2017

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MSDN MAGAZINE VENDOR PROFILE
MSDN Magazine Vendor Profile
The Build vs. Buy Data Quality Challenge for Optimizing Accuracy
Q&A with Bud Walker, Vice President of Enterprise Sales & Strategy
Q Who is Melissa, and what solutions do you provide?
A Melissa Global Intelligence was founded in 1985 as Melissa Data. For over three decades, we have been a leading provider of data quality, ID verification, and data management solutions. Our software, cloud services, and data integration components leverage comprehensive and authoritative reference data to profile, verify, standardize, consolidate, match/dedupe, enrich, and update U.S. and global contact data, including names, addresses, phone numbers and email addresses for improved analytics, efficient operations, and strong customer relationships.
Q What are the most common causes of poor data quality?
A The major cause of bad data is from typographical errors and non-conforming data entered during the data entry process— either by employees or customers filling out contact forms. The next biggest cause of bad data comes from migrating data due to irregular, missing, or misplaced data values that cause surprises. Bad data costs businesses between 10-25% of revenue each year, and in 2016 cost US businesses over $3.1 trillion. That’s why implementing real-time validation of contact information like addresses, email, phone, and other important information is essential, as well as establishing a data governance team in charge of understanding the impact of data quality.
Q What is the difference between rules-based and active data quality, and why is active data quality so important?
A Data that is mostly static, internally generated and controlled— like KPIs—employee performance metrics, new product develop- ment, supplier payment optimization, and inventory reduction data, can usually be well managed by rules-based validation.
Active data like customer names, addresses, emails, phone numbers, company names, and job titles, constantly change and
require complex parsing rules and multisourced reference data for verification. For instance, the telephone number 949-555- 5659 might look valid from a rules-based perspective, but is it actually callable, and associated with the right customer?
Active data quality relies on deep domain knowledge of contact and location data to parse, format, cleanse, enrich, and match customer records to provide the organization with accurate, timely, and actionable information.
Q Should companies build out a DQ solution or purchase an off-the-shelf one from a trusted vendor?
A We recommend a Hybrid approach. Companies should look to Build their own data quality solutions to handle rules-based data quality processes—those where they have the expertise and experience with their own internal data. Where active data quality is required, a Buy approach will usually be more efficient and cost-effective in the long run. Combining both Build and Buy can result in the best-of-both world results. We urge organi- zations to strive for small wins first—solve one problem at a time in discreet phases. This will help you show tangible results quickly and get the buy-in you need for future projects. Now, rinse and repeat.
Q How are Melissa’s solutions employed?
A Melissa offers every kind of integration option you can imagine. We have on-prem APIs and Cloud services that allow you to build into existing or custom applications. We also offer plugins for data integration platforms like SQL Server®, Pentaho® and Talend®, and CRM software like Salesforce® and Dynamics® CRM. Our smart, sharp tools approach means we can help you create the best solutions based on your budget and needs and achieve data quality without breaking the bank.
For more information, please visit g www.melissa.com