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Text Matching and Analytics

When we join two data sources using text there is often not a perfect match. Being able to match the two sources is vital to good analysis and we have a proven track record and methodology. We apply statistical and machine learning techniques to textual data to extract information and sentiment from the data.

A Typical Business Problem

It is very common for businesses to have many versions of the same data. For example, a business may have customer records in systems for contact management, licensing, sales and finance. The situation is often made worse when a business wants to access external data for its customer from a third party.

It is rare to find that all the systems have the same name for each customer. This happens naturally as it is common to abbreviate many parts of a company’s name. These differences in description can also prevent sales processes from operating correctly and will impact revenue generation.

JTA Solutions

We help our clients to improve their business performance by leveraging superior data and insights across their organization, growing their knowledge and developing impactful go-to-market strategies, audience targeting and nurturing of prospects.

We can deploy a variety of fuzzy matching and direct matching techniques to solve these issues. On a recent sales matching project, we were able to increase the match rate from 18% to 92%.

Read our whitepaper on text matching

The Secret Recipe to Success

Text matching can be fraught with problems. Here are some techniques recommended by JTA to make your text matching exercise a success:

  • Good text matching starts with data cleaning. Matches might fail because of differences in case, extra white space or use of accents with Latin languages. Data cleaning can help to minimise these problems.
  • Understand how languages work. A matching algorithm may need to apply different techniques when matching different languages. Chinese is very different to Italian.
  • Keep a database of common abbreviations and apply them consistently to the input data. If you have records for “ACME Incorporated” and “ACME inc.” then change them to both have a preferred consistent format.  Doing this will allow you to get a lot more direct matches without resorting to fuzzy matching algorithms.

    • Maximize the number of direct matches that you make in order to minimize the number of fuzzy matches required.
    • Never accept the results from matching in a business process without a validation process to test that the match is correct. Only accept matches which pass the test with a defined level of confidence.
    • Be careful when using fuzzy matching to build algorithms that are efficient as they can require a lot of computing resources. Run times must be realistic in the context of the business process as a whole.
    • Use machine learning techniques so that your matching engine will train itself over time and remember matches. You want the business process to get ever more accurate and faster and also reduce the amount of manual time needed.

     

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