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When MDM consolidation is too successful

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GDPR compliance should mean revisiting--and cleansing--your data files

Want to know the main reason you're liable to run afoul of the new European Union General Data Protection Regulation? Because your Master Data Management view of data across applications and analytics may have been just a little too successful.

 

Companies that have been extra diligent in that good old "360-degree view of the customer" have a master file chock-full of customer information, all of which is consolidated, but much of which is out of date and duplicated across systems.

 

And that means your MDM processes are probably filled with rule-busting red flags, courtesy of the new GDPR regulations.

 

Let's unpack this a bit, examine the key regulatory changes, and see where you're probably at risk.

 

The EU's GDPR, which becomes effective May 25, addresses consumer concerns about safeguarding digital information, regardless of where in the world it's stored, and erasing it when requested. If you've ever done business with people in any EU country, their information is in your CRM system, marketing automation, or master customer information file.

 

SO MANY DATA FIELDS, SO LITTLE TIME

 

What data are we talking about here? Just about anything, which pretty much summarizes your potential problem.

 

Newly protected data not only includes name, address, email, and phone, but also photos, bank details, social networking posts, and individual computer IP addresses. CRM and marketing automation systems normally include birthdays, payment methods, URL visits, and shopping habits, regardless of whether the information is about a person's private, professional, or public life, and those are GDPR-protected as well.

 

Running afoul of the GDPR is no small matter. It can result in fines of up to 20 million euros--roughly $24.5 million--or alternately 4 percent of a company's entire annual global revenue.

 

On the upside, the Euro folks are more serious than ever about their citizens' data and privacy.

 

On the downside, trying to identify your organization's relevant exposure can become an intolerable burden.

 

While the complete GDPR is extensive, a key element is Article 17, detailing consumers' "Right to Erasure." GDPR requires you to erase any EU citizen information upon request "without undue delay," and with only a handful of exceptions.

 

Surveys indicate that this Right to Erasure is the most challenging requirement for businesses. And this is where your Master Data Management projects are most vulnerable.

 

The problem is that data management processes that supposedly have merged siloed files across applications are geared toward inclusion, not efficiency.

 

It's likely you've got tons of duplications--numerous iterations of the same person, each containing slightly different fields, conflicting data, and even errors from one entry to another. Small wonder: Data entries are made individually over years and via different processes.

 

Even if entries are flagged, typical MDM false negative results mean truly duplicated records will remain behind, and uncorrected. After all, nobody wants to run the risk of erasing a unique and valid contact.

 

NIGHTMARE ON MDM STREET

 

Consider: You receive a request to have an individual's data erased, you think you've  complied, but in reality have overlooked iterations across applications that are of the exact same contact.

 

The result: A regulatory nightmare and a potential financial disaster.

 

What's a chief data officer to do?

 

First, start with an internal audit conducted by Melissa, one that goes far beyond any Privacy Impact Assessment process you may already have in place. A thorough top-down approach will uncover problems and trends, determine your true risk, and provide recommendations for redress.

 

But you'll want to go further.

 

Melissa technology tackles databases to standardize, correct, complete, and verify customer records. If there are any remaining identify questions about who's who in your database, and if any duplications continue to exist, Melissa employs matching engines to resolve them.

 

You've done your best to assure that 360-degree view of the customer through optimal MDM processes, and congratulations to you. Now, take the next step to protect your company from regulatory snafus.

 

To ensure you're compliant with the new GDPR regulations, call Melissa.

Tips & Tricks for Global MatchUp Matching Strategies

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by Tim Sidor, Data Quality Analyst

In the past we've discussed implementing different matching strategies based on how you would like your records grouped. For example. By "Address"? or by "Name and Address". The former would match 'John' and 'Mary Smith' at the same household, whereas the latter would identify them as unique entities.


For Global processing, even after determining and selecting a general strategy, 'Address' for example, it might still require knowing the expected address formats of the source data that needs to be compared and thus reevaluate the logic.

 

At first glance, a 'Global Address' matchcode might appear to be a safe accurate matching strategy...

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But knowing that some countries don't have a reliable Postal Code, which is usually the component MatchUp uses for efficient 'neighborhooding' (also known as 'grouping' or 'clustering'), how can we accurately match these records? Simply removing the Postal Code component would incorrectly match similar addresses that were in different parts of the country.

 

US & Canada users are so used to using the reliable Postal Code that we rarely use City (Locality). But for processing countries without Postal Codes, or databases with multiple countries, adding a Locality can bring back accuracy and efficient clustering.

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Configuring this matchcode to allow 'blank matching' on the Postal Code will accurately match records for most worldwide addresses and is a default distributed matchcode.

 

However, many countries distinguish addresses by also using a different hierarchy structure which may include a combination of Dependent Locality, Administrative Area and or Sub Administrative area. Or the use a Dependent Thoroughfare to distinguish the delivery address. So knowing the primary data types used in a countries standard address can help you decide the proper matchcode components to include in your matchcode.

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How do I know how to construct a good matchcode for specific region processing? Our 'Global Address, Locality' matchcode is a good basic strategy, but using Melissa's resources - such as Global Verification documentation and or actual record processing and parsing can help you determine the necessary components to construct a matchcode to produce accurate results.

Discover Data Quality Issues Before they Arise

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By Taky Djarou, Data Quality Analyst


Melissa has released its new data Profiler API. The Profiler Object offers a unique approach to profiling your data, combining years of contact data quality experience, the power of many Melissa Objects, and data source tables to help you dig deeper into your data and return hundreds of properties about the input table, columns and individual values.

For example, many existing Profilers will allow the user to set a RegEx to capture an email pattern. The Melissa Profiler offers that function, as well as checking the syntax, the domain, and whether it's disposable, has a spammy reputation, or is invalid and will return counts that reflect all of the above.

Data validation is also performed on city, state/province, ZIP and postal code fields to report any discrepancies in your data. Even if you accidentally put a phone number in a name field, Melissa's Profiler can detect and report it.

The Profiler Object returns counts of duplicate records using four different matching criteria (Exact, Address Only, Household, and Contact.) Using the power of our flagship deduplication solution MatchUp, the number of unique records, duplicates and the largest group of duplicate counts will be reported for all four matching criteria.

Melissa's Profiler also provides value specific iterators (pattern, word, data, date, Soundex, etc.) that allow the user to loop through any column in an ascending or descending order to retrieve those values and their respective counts.

The date iterator for example, allows the user to see the busiest/slowest time/day of the month/day of the week using a time stamp field of when a record was created.

To demo the Melissa Profiler, please visit us at:  http://www.melissa.com/data/profiling.html or call 1-800-MELISSA (635-4772) and one of our Sales Representatives will set you up with a free trial.

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