Recently in Data Audit Category

When MDM consolidation is too successful

| No Comments | No TrackBacks

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the customer" have a master file chock-full of customer information, all of which is consolidated, but much of which is out of date and duplicated across systems.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.

personator-world-main-graphic.png

By Kevin Ubay-Ubay, Sales Engineer


Personator World is Melissa's new powerful cloud based web service that gives a simple and single way to clean and verify your contact data globally. This service leverages our experience in name, phone, address & email validation to provide comprehensive contact checking and combines high quality identity level verification.

Some examples of where our new web service can be applied:

·         Age verification

·         Name-address verification

·         Anti-fraud applications

·         Online shopping cart & eCommerce platforms

·         FinTech/Banking

Trusted Reference Data

Personator World uses a number of trusted reference data to verify the identity of an individual. These types of data sources include:

·         Citizen/national databases

·         Credit agency/bureau

·         Utility and telecom sources

·         Driver's licenses

·         Electoral rolls

Personator World can then determine if the identity has been found and matched against those datasources. An example JSON response from the web service may contain something like this:

{

    "DatasourceName": "CREDIT-2",

    "Results": "KV03,KV04",

    "Messages": [

        {

            "ResultCode": "KV03",

            "Description": "First/given/forename matched"

        },

        {

            "ResultCode": "KV04",

            "Description": "Last/surname matched"

        }

    ]

},

{

    "DatasourceName": "CONSUMER-1",

    "Results": "KV01,KV14,KV13,KV12,KV10",

    "Messages": [

        {

            "ResultCode": "KV01",

            "Description": "Address matched"

        },

        {

            "ResultCode": "KV14",

            "Description": "Premise/house number matched"

        },

        {

            "ResultCode": "KV13",

            "Description": "Thoroughfare matched"

        },

        {

            "ResultCode": "KV12",

            "Description": "Locality matched"

        },

        {

            "ResultCode": "KV10",

            "Description": "Postal code matched"

        }

    ]

}


As you can see here, datasources where a match has been found are listed as well as what components of the input had been matched.

Using Personator World

To give a walkthrough of this service, when you send your contact data that you want checked and verified, Personator World starts off by standardizing and validating your data. At this stage, the service will parse and standardize your data as well as check for the following:

·         Name is in a valid format

·         Address is deliverable

·         Email address exists

·         Phone number is callable

Input

Output

Full name: John doe

Phone: 8458692102

Email: john.doe@yaoo.com

Address Line 1: 1 unicorn

City: norwich

Administrative Area:

Postal Code: NR33AB

Country: GB

Full name: John Doe

Phone: +44 8458692102

Email: john.doe@yahoo.com

Address line 1: 1 Unicorn Rd

City: Norwich

Administrative Area: Norfolk

Postal Code: NR3 3AB

Country: GB

 

In the diagram above, you can see the service standardizing (changing casings, abbreviations, etc.) and making corrections to our input data - add in missing street suffixes, add in phone country dialing code, add in missing state/province/administrative area info, correcting typographical email domain errors (yaoo.com -> yahoo.com), etc.

Next, Personator World takes your data and compares it against trusted reference data in order to verify that individual's information.

In this verification stage, the web service takes the standardized name, phone, email and address from the previous checking stage and performs ID verification. Additional input such as the individuals national ID provisioned by the country's government and date of birth can be verified as well, depending on the country.

 

Input

Output

Full name: John Doe

National ID: HJDO840230HVZRRL05

Date of birth: 2/30/1984

Phone: +56-222-226-8000

Email: john.doe@yahoo.com

Address Line 1: Paseo De Los Conquistadores 2000

City: Guadalupe

Administrative Area: NL

Postal Code: 67170

Country: MX

Results:

 

·         KV01 - Address matched

·         KV02 - National ID matched

·         KV03 - First name matched

·         KV04 - Last name matched

·         KV05 - Phone number matched

·         KV06 - Email matched

·         KV07 - Date of birth matched

 

 

 

The diagram above shows how the service will return back result codes indicating which pieces of information got matched. By observing what kinds of result codes are returned, you can determine how reliable your data is.

Availability

Personator World is currently available as a web service hosted on our servers - meaning you won't have to worry about updates and maintenance. Information about the countries supported and the level of coverage can be found by visiting our online wiki at wiki.melissadata.com. Sample code and documentation is also provided to help you get jump started on building applications and services incorporating our ID verification technology.

 

Introducing Business Coder for United Kingdom

| No Comments | No TrackBacks

data-enhancements-businesscoder-uk.png

Melissa is happy to announce the release of our Business Coder service for UK, where you can verify, validate, and append firmographics for businesses in the UK. Originally servicing only the US, our webservice now expands to the UK as one of our more popular demands for business identification.

Our Business Coder UK webservice can help provide address cleansing and verification, along with business verification and appending of firmographic data. Our SIC Codes and Descriptions can help identify industries that the targeted businesses belong to, along with incorporation dates to help verify the correct businesses.

Similar to Business Coder US, our familiar result codes can help determine any issues with address or business validation. Ensure that the data returned along with our results help verify correct businesses in your data.

Powered by our Global Address engine, our address validation system thoroughly covers streets across the United Kingdom. With quality data ensured by experts and reliable sources such as the Royal Mail, be sure that you standardize and correct any addresses for your list of businesses to ensure that your deliveries do not go to waste.

And don't forget, this is only an announcement for our initial release. We're constantly striving to improve our webservice, as we plan to add additional fields and features to Business Coder UK. Check out our wiki and be on the lookout for new updates for the webservice!

Please note that for our release, Business Coder US and Business Coder UK will have separate URLs to make web service requests to. Additionally, Business Coder UK requires a separate subscription from Business Coder US. Please visit our wiki for more information on how to what to expect out of the new Business Coder.

Business Coder UK wiki: http://wiki.melissadata.com/index.php?title=Business_Coder_UK

Discover Data Quality Issues Before they Arise

| No Comments | No TrackBacks
melissaprofiler.png

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.

Ask First, Fix Later

| No Comments | No TrackBacks
By Elliot King

Elliot King
Like the Boston Red Sox breaking their fans' hearts, almost inevitably (stress on the almost) you will discover that some percentage of your data is wrong. The realization that you have data quality problems may come about for few reasons: 1) you've looked under the hood of your data systems by conducting a data assessment or 2) a data audit revealed that the data you have is not what you think you have.

Or a problem may have percolated to the surface. Perhaps a direct mail campaign failed to yield the anticipated results or customer service representatives find themselves with incorrect information during critical interactions. So what do you do then?

With most rude awakenings, people want to act right way. After all, the data is broken, so let's get it fixed. With data quality, however, the impulse to act immediately may be a mistake. Indeed, the first question to ask is, does it really matter? The sad fact is that we live in a world of inaccurate and incomplete data.

Data sets will never be perfect. Inaccurate data may have little or no impact on ongoing processes and the investment required to remediate the data may be more than the return better data will provide. Identifying the impact of the data quality is essential. Have the problems resulted in lost revenue? Has customer service been compromised? Have the issues driven up costs? And so on.

Once the impact of the problem has been isolated, the next step is to better understand the nature and scope of the problem. What are the processes through which incorrect or poor data is entering the system? As most data professionals know, often data problems have more ways into your system than a freeway has on-ramps. Can the sources of incorrect data even be fixed? If they can, how much investment will be required and how much improvement can be expected? Finally, what will be the expected return on investment?

Though it seems a little counter-intuitive and perhaps even a little uncomfortable, the first step after data quality issues are discovered is to think. You may not want to act at all.


Why You Need a Data Audit

| No Comments | No TrackBacks
Elliot King
Everybody makes mistakes and those mistakes have consequences. As enterprises rely more heavily on data to make decisions and drive processes, the quality of that data becomes more critical to the overall success of the organization.

In many cases, the impact of bad data is not too hard to identify--direct marketing campaigns with high numbers of email bounce-backs or undeliverable mail; marketing efforts with poor response rates because the offering has not been correctly tailored to address the "wants and needs" of the customers; prospects who turn away because their names are spelled wrong or other information is incorrect.

With the growing need to integrate data from so many sources, both internal and external as well as the dynamic nature of business itself, the chances of introducing mistakes into critical databases is growing exponentially. It's not really a question if there are mistakes in your databases; the question is how many mistakes are there and what kind of negative impact will they have on your business?

The answer to those questions can be determined by conducting a data audit. A data audit is just what the name implies--a systematic look at data to insure that it is what it is supposed to be.

Conducting a data audit is pretty straightforward. First, determine which records and which fields should be examined. If customer records are to be audited, fields such as names and addresses perhaps; buying history, payment history or other critical fields may be included. The field selected should be those associated with a specific process or activity.

Then assess the fields to determine which do not conform to the data dictionary and the business rules that govern them. A wide range of errors may surface. Values may be missing. Values (such as dates) may fall outside of a specific range. Fields may be incomplete or formatted incorrectly.

The next step is to determine the source of the error; how the errors can be rectified (if, in fact, they need to be rectified); and how mistakes can be minimized in the future. Among the most common sources of errors are faulty data conversions and inconsistencies in integrating data from different sources. Not every error always has to be fixed or the underlying process changed. In most situations, there will always be some errors in the data that can be tolerated because they do not have a crippling impact on the business activity being supported.

How often should data audits be conducted? Not surprisingly, the answer is elastic. Data audits should be conducted often enough that faulty data does not impede reaching your business goals.

Categories