Introduction

In South Africa, the issue of one street having two names presents a complex challenge for automated matching systems. This phenomenon occurs with both English and Afrikaans street names, like Church Street vs. Kerkstraat, and is exacerbated by inconsistent abbreviations, such as 4th Avenue vs. Fourth Avenue.

Finding the right automated match solution becomes crucial for effective data management and avoiding false positive matches.

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The Complexity of Identical Street Names

It is not uncommon for two streets to share the same name, as seen with Nelson Mandela Drive, a frequently used name for major roads across the country. Typically, these streets are distinct enough from each other due to other address elements. However, when human errors come into play, addressing data becomes much more intricate, and simplistic matching strategies prove inadequate.

Human Error and Its Implications

One common issue is that people might remember the street name but not the specific street type. For instance, someone might mention “Osbourne Road” instead of “Osbourne Street” when providing an unfamiliar address. When such errors find their way into corporate data, they can lead to significant challenges.

Another challenging scenario arises when addressing a location as the “corner of” two streets. For example, someone might say, “Our offices are on the corner of 1st and Main Street.” In geographies where multiple street options exist, this creates ambiguity and results in multiple streets sharing the same name.

The Importance of Effective Automated Match Solutions

As mentioned previously, if simplistic, statistical matches are now applied to these data sets where may have a  scenario where “1st Street” is a better match to “1st Crescent” than “Church Street” is to “Kerkstraat”. Similar examples could easily be applied to Name, and other data elements.

Implementing an automated match solution is a critical component of any master data management technology stack. However, choosing the right solution is essential to avoid exacerbating existing complexities. Some solutions may lack the necessary granularity to handle such intricate situations, leading to overwhelming exceptions that demand manual verification.

Choosing the Right Solution

To ensure a successful automated matching process, it is vital to partner with experienced suppliers who can deliver the expected results. The ideal solution should create accurate links between actual matches, avoiding false positives, and minimize the dependence on manual validation. Avoid generic statistical matching approaches and opt for a solution that can be finely tuned to meet your specific data management needs.

The bottom line – make sure that your supplier has the experience necessary to ensure that your automated matching solution does what you expect – creates the links between actual matches without creating false positive matches, and without depending on manual validation.

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And make sure that the tool you use does not rely on generic statistical matching but can be tuned to your specific needs.

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