Recently in Data Enhancement Category

How to Know Customers Are Who They Say They Are

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In 2015 alone, 62% of companies were targets of payment fraud. As customers increasingly conduct their financial affairs online and via mobile devices, risk management and fraud prevention become more and more difficult. So, how do you know customers are who they say they are?

Short of reaching through the computer, tablet, or smartphone screen and verifying a customer with your own eyes, there are a plethora of ways that help you better know your customer, manage risk, and even prevent fraudulent transactions.

Age & National ID Verification

The first step to customer authentication is to match a customer's national ID (for example, their social security or driver's license numbers) and date of birth. Better authenticate a customer's ID documents and simplify compliance with any age restrictions or purchase laws, while improving customer service at the same time. Instantly verify that the customer purchasing your age-restricted goods is old enough to legally make that purchase.

Name-Address Matching

The second step is to match name to address to confirm the person buying your product or service isn't giving you false information. Personator leverages a comprehensive dataset containing billions of records to confirm and match current names and addresses with the highest degree of accuracy. Our powerful, real-time tools and services help you achieve entity resolution and compliance, as well as better know your customer and reduce, or even eliminate, the need for manual review.

Address Correction & Formatting

Next, you'll want to add in what's missing from customer data entry, legacy systems, sales input, and anywhere else your records come from. Add missing street suffixes, state/province/administrative area info, and standardize addresses to specific country formats using Advanced Address Correction (AAC) to verify that addresses are accurate and deliverable to real locations.

Contact Data Validation

Validation concerns more than just a name or address - it needs to look at all aspects of people data, from names and addresses to phone numbers, email addresses, geocodes, IP locations, demographics, and more. Determine that the given postal address for every customer is deliverable, the email address exists, the name associated with a mobile device and whether the phone number is active and callable, and the given name is in a valid format. You can even trace customers with geocodes and IP locators to manage risk and ensure compliance.

Melissa's Personator® World Edition can help meet all of these needs. Personator is a customizable web service that fits all your ID verification process and risk management requirements. It can help optimize onboarding and fraud detection in Ecommerce, AML Compliance, Customer Due Diligence, Card Not Present, Know Your Customer (KYC), and FinTech/RegTech arenas.

Try Personator free for 30 days to see how it can transform your business's safety and compliance.

The last major update to the Business Coder Web Service includes the addition of employee contacts for the entered business, allowing for the retrieval of employee names and their respective titles. The next major update to Business Coder will feature an upgrade to the employee contact list returned. Aside from the name and title, contacts will now also give back both their email address and phone number if available. 


This is what the contacts array used to return, versus what it returns with the latest update:


businesscoder-input.png
businesscoder-output.png

Make sure to take advantage of these new fields in the next Business Coder update in order to enrich and improve your business contact information. Leveraging additional contact information will certainly allow for new opportunities with business prospects and allow for better marketing segmentation.

 

Try our Business Coder API now.

Meet Melissa: Global Intelligence

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Melissa Data is officially now Melissa.

 

As we welcome our 32nd year in business, we are excited to announce an important change at Melissa Data. We've decided to drop the "Data" from our brand identity. We are simply Melissa now. This is part of a new branding effort to reflect Melissa's growth, and more importantly, the changes in the data quality space. While authoritative data sources power our products and services, we want to continue developing new solutions that deliver data-driven results for better business intelligence.

 

This forward thinking change is reflected in our new logo with the design emphasis on the "i" for intelligence. You will see this focus on intelligence in our new ID verification services, our industry-specific solutions to help with Know Your Customer initiatives, risk management and compliance, and in our robust customer data management and data integrations platforms.

And, you'll see it in our new website at www.melissa.com. 


Our goal with this new website is to provide our visitors an easier way to learn about Melissa's services and solutions. Immediately, you will notice streamlined menus, simple navigation, and quick access to the information you need.

 

We look forward to working together with all of our existing customers on more opportunities and better solutions for global intelligence. Please feel free to reach out and let us know how we can better assist you.

How to Do It All with Melissa

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With Melissa, you can do it all - see for yourself with the brand new Solutions Catalog. This catalog showcases products to transform your people data (names, addresses, emails, phone numbers) into accurate, actionable insight. Our products are in the Cloud or available via easy plugins and APIs. We provide solutions to power Know Your Customer initiatives, improve mail deliverability and response, drive sales, clean and match data, and boost ROI.

 

Specific solutions include:

·         Cleaning, matching & enriching data
·         Creating a 360 degree profile of every customer
·         Finding more customers like your best ones with lookalike profiling
·         Integrating data from any source, at any time

Other highlights include: global address autocompletion; mobile phone verification; real-time email address ping; a new customer management platform; as well as info on a wealth of data append and mailing list services.

 

Download the catalog now:

http://www.melissa.com/catalogs/solutions/index.html

 

Take the headache out of maintaining clean contact data with our new Listware Online! With a simple data upload (there's no software to install), this Cloud-based service verifies, corrects, and standardizes U.S. and Canadian addresses, and adds missing name, address, phone, and email information. Listware Online also enriches your data with additional information, including lat/long coordinates, property data, census data, and demographic data. Every month Listware Online users get 1,000 free credits, making it an affordable way to clean and enrich your data. 


For more info, please visit our website



A Guide to Better Survivorship - A Melissa Data Approach

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By Joseph Vertido

The importance of survivorship - or as others may refer to as the Golden Record - is quite often overlooked. It is the final step in the record matching and consolidation process which ultimately allows us to create a single accurate and complete version of a record. In this article, we will take a look at how Melissa Data uniquely differentiates itself in approaching the concept of survivorship compared to some of the more conventional practices.

The process of selecting surviving records means selecting the best possible candidate as its representation. However, best in the perspective of survivorship can really mean a lot of things. It can be affected by the structure of data, where the data is gathered from, how data comes in, what kind of data is stored, and sometimes by the nature of business rules. Thus techniques can be applied in order to accommodate certain types of variations when performing survivorship. We find that there are three very commonly used techniques in determining the surviving record:

I. Most Recent

Date stamped records can be ordered from most recent to less recent. The most recent record can be considered eligible as the survivor.

II. Most Frequent

Matching records containing the same information are also an indication for correctness. Repeating records indicate that the information is persistent and therefore reliable.

III. Most Complete

Field completeness is also a factor of consideration. Records with more values populated for each available field are also viable candidates for survivorship.


Although these techniques are commonly applied in survivorship schemas, its correctness may not be as reliable in many circumstances. Because these techniques apply to almost any type of data, the basis in which a surviving record is created conforms only to "generic" rules. This is where Melissa Data is able to set itself apart from "generic" survivorship. By leveraging reference data, we can steer a way to generating better and more effective schemas for survivorship.

The incorporation of reference data in survivorship changes how rules come into play. Using the Most Recent, Most Frequent or Most Complete logic really has more of an aesthetic basis for selection. Ideally, the selection of the surviving record should be based off an actual understanding of our data.

And this is where reference data comes into play. What it boils down to at the very end is simply being able to consolidate the best quality data. Thus by incorporating reference data, we gain an understanding of the actual contents of data, and create better decisions for survivorship. Let's take a look at some instances on how reference data and data quality affect decisions for survivorship.

I. Address Quality

Separating good data from bad data should take precedence in making decisions for survivorship.

Address Quality Sample

In the case of addresses, giving priority to good addresses makes for a better decision in the survivorship schema.

II. Record Quality

It could also be argued that good data may exist in a single group of matching records. In cases like these, we can assess the overall quality of data by taking into consideration other pieces of information that affect the weight of overall data quality. Take for example the following data:

Record Quality Sample

In this case, the ideal approach is to evaluate multiple elements for each record in the group. Since the second record contains a valid phone number, it can be given more weight or more importance than the third record despite it being more complete.

Whether we're working with contact data, product data or any other form of data, in summary, the methodologies and logic used for record survivorship become dependent primarily on data quality. And however we choose to define data quality, it is imperative that we keep only the best pieces of data if we are to have the most accurate and correct information. In the case of Contact Data however, Melissa Data changes the perspective as to how the quality of data is defined, therefore breaking the norm of typical survivorship schemas.


Structural Differences and Data Matching

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By David Loshin

Data matching is easy when the values are exact, but there are different types of variation that complicate matters. Let's start at the foundation: structural differences in the ways that two data sets represent the same concepts. For example, early application systems used data files that were relatively "wide," capturing a lot of information in each record, but with a lot of duplication.

More modern systems use a relational structure that segregates unique attributes associated with each data concept - attributes about an individual are stored in one data table, and those records are linked to other tables containing telephone numbers, street addresses, and other contact data.

Transaction records refer back to the individual records, which reduces the duplication in the transaction log tables.

The differences are largely in the representation - the older system might have a field for a name, a field for an address, perhaps a field for a telephone number, and the newer system might break up the name field into a first name, middle name, and last name, the address into fields for street, city, state, and ZIP code, and a telephone number into fields for area code and exchange/line number.

These structural differences become a barrier when performing records searches and matching. The record structures are incompatible: different number of fields, different field names, and different precision in what is stored.

This is the first opportunity to consider standardization: if structural differences affect the ability to compare a record in one data set to records in another data set, then applying some standards to normalize the data across the data sets will remove that barrier. More on structural standardization in my next post.

By David Loshin

One of the most frequently-performed activities associated with customer data is searching - given a customer's name (and perhaps some other information), looking that customer's records up in databases. And this leads to an enduring challenge for data quality management, which supports finding the right data through record matching, especially when you don't have all the data values, or if the values are incorrect.

When applications allow free-formed text to be inserted into data elements with ill-defined semantics, there is the risk that the values stored may not completely observe the expected data quality rules.

As an example, many customer service representatives may expect that if a customer calls the company, there will be a record in the customer database for that customer. If for some reason, though, the customer's name is not entered exactly the same way as presented during a lookup, there is a chance that the record won't be found. This happens a lot with me, since I go by my middle name, "David," and often people will shorten that to "Dave" when entering data, so when I give my name as "David" the search fails when there is no exact match.

The same scenario takes place when the customer herself does not recall the data used to create the electronic persona - in fact, how many times have you created a new online account when you couldn't remember your user id? Also, it is important to recognize that although we think in terms of interactive lookups of individual data, a huge amount of record matching is performed as bulk operations, such as mail merges, merging data during corporate acquisitions, eligibility validation, claims processing, and many other examples.

It is relatively easy to find a record when you have all the right data. As long as the values used for search criteria are available and exactly match the ones used in the database, the application will find the record. The big differentiator, though, is the ability to find those records even when some of the values are missing, or vary somewhat from the system of record. In the next few postings we'll dive a bit deeper into the types of variations and then some approaches used to address those variations.

By Joseph Vertido

For many, the concepts of data integration and data quality are separate and have no commonality. But in reality, when you combine them - they create a partnership that excels. Where data quality leaves off, data integration begins, and vice versa. A new product - Contact Zone - fuses these two concepts together into one revolutionary solution for where data integration and data quality converge.

Data integration tools simplify data migration and data warehousing procedures - both of which are concerned with the issue of data management, i.e. keeping data organized. Data quality, on the other hand, is concerned primarily with an understanding of the nature, and validity of the contents of the actual data, i.e. keeping data clean. Maintaining an organized database is not the same as keeping it clean - they are two different approaches to handling data - but they can be combined, or should they?

The short answer is yes.

In essence, data integration allows for the migration of data from a given source to a given destination. Typically, users take advantage of data integration to accomplish data warehousing initiatives - allowing for easy migration and manipulation of data, which ultimately leads to maximizing the efficiency of business intelligence and analytics.

However, Gartner states that "only 30 percent of business intelligence and data warehousing implementations fully succeed." Why? The top two reasons for failure are budget constraints and data quality. So, although the architectural constraints of building a data warehouse can be addressed by utilizing data integration tools, it still leaves the problem of poor data quality - something that most data integration tools handle with mediocrity at best.

That's where Contact Zone comes into play. It's a data integration tool optimized for data quality, allowing you to shoot two birds with one stone.

Contact Zone connects to virtually any source, overcoming an obstacle our clients frequently encounter when implementing data quality, namely there is such a variety of database format and platforms today that the types of environments and combinations can be overwhelming.

Whether you have an IBM DB2 database or PostgreSQL, leveraging Contact Zone allows for data integration for almost any form of database format, while making sure that all data is clean, correct, standardized, and valid.

By David Loshin

What I have found to be the most interesting byproduct of record linkage is the ability to infer explicit facts about individuals that are obfuscated as a result of distribution of data. As an example, consider these records, taken from different data sets:

A:
David
Loshin
301-754-6350
1163 Kersey Rd
Silver Spring
MD
20902

B:
Knowledge Integrity, Inc
1163 Kersey Rd
Silver Spring
MD
20902

C:
H David
Lotion
1163 Kersey Rd
Silver Spring
MD
20902

D:
Knowledge Integrity, Inc.
301
7546350
7546351
MD
20902

We could establish a relationship between record A and records B and C because they share the same street address. We could establish a relationship between record B and record D because the company names are the same.

Therefore, by transitivity, we can infer a relationship between "David Loshin" and the company "Knowledge Integrity, Inc" (A links to B, B links to D, therefore A links to D). However, none of these records alone explicitly shows the relationship between "David Loshin" and "Knowledge Integrity, Inc" - that is inferred knowledge.

You can probably see the opportunity here - basically, by merging a number of data sets together, you can enrich all the records as a byproduct of exposed transitive relationships.

This provides us with one more valuable type of enhancements that record linkage provides. And this is particularly valuable, since the exposure of embedded knowledge can in turn contribute to our other enhancement techniques for cleansing, enrichment, and merge/purge.

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