A name is a person, but the person is more than their name — just ask Taylor Swift. He’s a 29-year-old photographer from Seattle.

Name matching is about more than just the person’s name.

Across industries and organizations, the volume of consumer profile data collected is growing at an exponential rate — and most companies rely on this data day in and day out.  With the sheer volume of records being entered and re-entered by businesses across systems, how do companies ensure that contact data is accurately and correctly matched? Between spelling variations, misinterpretations, lack of address standardization, and cultural differences, there are a million ways that contact data can be corrupted, wrongly matched, or duplicated.

To solve for these unique challenges, we must first understand the complexities of contact data. 360Science attempt to do just that in a new whitepaper, “Understanding The Complexity of Name Matching,” by explaining what it is that makes name matching is such a unique challenge—one that is only becoming more complex with time—and offering solutions to some of the most common contact data challenges. The following are some highlights from the paper, offering insight into the contact data conundrum along with some recommended measures to ensure ongoing data cleanliness.

Think360° Summary

  • CRM and customer data are unique — and matching people data is more complex than other form data matching.

There are several ways that contact data can be skewed. Whether it’s a press of the wrong key, lazy input, a lack of standardization, or someone’s information heard wrong, these mistakes cause system-wide errors and issues. The challenge is made clear by simple questions like this one. Do you agree or disagree that these two records are the same person?

  • Elizabeth A. Herrera, 7800 Beverly Ave Suite 300, Wilshire La Brea, CA 90037 and
  • Betty Hereira, 7800 Beverlie Blvd, Los Angeles CA 9003

Think about how you made your decision. First you’d look at the first and last names separately and together, then each individual part of the address and the address as a whole, and then need to assess the name and address together, before making your binary yes-no decision.

Sure, this may only take a second for one entry. But as author and 360Science CEO, Rob Heidenreich, points out, “Now think about repeating that decision scanning a 1,000 record spreadsheet, a 10,000 record marketing list, a 100,000 record CRM Database, or a 300 million record data store. Mind you, that even a small 1,000 record spreadsheet equates to 5-million time consuming comparisons, checking every record against the each other.”

  • Increasing globalization only exacerbates this contact data matching issue.

As we all know, different cultures do certain things differently, and this can include spelling, first and last name order, and other contact information. Because there are no global standards that dictate how a person’s contact information should be entered into a database, it’s difficult to match that contact information across systems and entries. Heidenreich states that “These variations of global names and westernization from the non-English speaking world impose major challenges for a name matching as the variations are not isolated.”

  • Address standardization simply doesn’t achieve what you think it does, making the contact matching issue even more difficult to solve.

In the white paper, Heidenreich says that “there is no such thing as achieving standardization with address data.” He uses the US Postal Services as an example of why this is true: “The CASS certified software can ‘validate addresses to the delivery point and verify that an address is deliverable’. It said nothing about making certain it is the same every time. The USPS primary concern is not about standardization – they care about deliverability.” When checking the USPS database for the address of a City Hall in a Texas suburb, for example, you’ll get 6 different city names returned as totally acceptable for that same address. Adds Heidenreich, “To be clear — address validation is a necessary component of data quality — but you have to understand it’s limitations.”

  • Despite its complexity, contact data matching is a problem that technologies must work to solve.

Heidenreich wraps up the white paper by stating that “Contact data matching is uniquely difficult, and the use of contact data is exploding. From marketing to homeland security, the underlying technologies for data matching logic must support these unique challenges in our ever evolving world.” Around the world, businesses’ reliance on accurate contact data will only increase exponentially. Technologies able to simplify matching will help alleviate a real, major pain point—transforming businesses and saving them millions in the process.

To learn more about the unique complexities of name matching, along with tips to address these challenges and clean up your database once and for all, download the full whitepaper now.


Original Post

Name matching is about more than just the person’s name. The name is a person, but the person is more than their name — just ask Taylor Swift. He’s a 29-year-old photographer from Seattle.

To effectively match contact data, you need to understand the context of the full contact record – not just a single field. We have to talk about the whole contact record.

CRM and customer data is unique – and matching people data is more complex than other form data matching. By the time a contact record is received in the database, it’s usually corrupted in numerous ways, whether by lack of standards, miskeyed data, hearing errors or lazy input. The entry of that data is a collection of disparate data sources from various systems, and data collection methods resulting in a host of errors and issues.

Read the complete white paper from 360Science…