![]() In particular, I consent to the transfer of my personal information to other countries, including the United States, for the purpose of hosting and processing the information as set forth in the Privacy Statement. I agree to the Privacy Statement and to the handling of my personal information. By submitting this form, you confirm that you agree to the storing and processing of your personal data by Salesforce as described in the Privacy Statement. By submitting this form, you acknowledge and agree that your personal data may be transferred to, stored, and processed on servers located outside of the People's Republic of China and that your personal data will be processed by Salesforce in accordance with the Privacy Statement. Users can use it on any field with a Data Role applied to map invalid values to valid ones.īy registering, you confirm that you agree to the processing of your personal data by Salesforce as described in the Privacy Statement. The Hybrid method is available as “Pronunciation + Spelling” in Tableau Prep Builder 2019.2.3 and later releases. Use the Similarity threshold (default 0.85 based on internal experimentation) to get the final groups.For each key, compute Levenshtein distance and Similarity between values mapped to the key.The two keys produce two groups of data values: and For example, the data values on the left are associated with the keys on the right. Transform data values to keys using Metaphone3.Resulting groups are determined based on a similarity threshold. The hybrid approach transforms data values into their associated keys, then for each key it groups values that are most similar based on distance. The charts show the results of our experiment comparing them using a test dataset. We measured accuracy as follows:Īnd, we developed a hybrid approach that combined the two methods to produce accuracy like that of Levenshtein distance but at a fraction of the cost (time). We experimented with using only key-based or distance-based methods to group data where we knew the expected groups (using the geographic data available in Tableau Desktop), and used computation time and accuracy to compare them. So, it’s important to provide users an efficient grouping method that offers good results. Tableau Prep Builder allows users to directly interact with their data, transform it, get immediate feedback, and confidently prepare data for analysis. When working with large sets of data values, distance-based grouping methods are slow, as they compare all pairs of values. The two keys generate two groups of data values: Before generating the key, each string is first transformed into lowercase letters and all special characters including whitespaces, punctuation, and control characters are removed.įor example, the values on the left are associated with the keys on the right. In Tableau Prep Builder, this method is case insensitive and only applies to numbers and letters. It tokenizes the value into a character set and sorts the characters to generate a key, known as a 1-gram. Ĭommon Characters: This method is useful to fix capitalization or formatting issues. The two keys produce two groups of data values: For example, the values on the left are associated with the keys on the right. It uses the Metaphone3 algorithm to generate keys based on the value’s English pronunciation. Pronunciation: This method is useful for fixing data entry errors where words sound similar. ![]() In key-based methods like Pronunciation and Common Characters, each value is transformed to a key, or token, and all values with the same key are grouped together. Wouldn't it be great if a data preparation tool could help automate this task? Updating this script is still tedious as he works backwards from errors in his analysis. ![]() After spending a lot of time manually fixing the city names, he converted that work to a Python script as he found he has to repeat the standardization with every campaign. He finds that users misspell several cities, which leads to errors in his analysis as data is not correctly reported. John, a Tableau customer, analyzes marketing call data where agents manually enter responses across the US. To correctly analyze this data, users must manually reconcile data values, which can be error-prone and time-consuming. For example, a city field with “Seattle” spelled as “Seattel” an address field with two variations of 5th street as "5th St" and "St, 5th" or a customer name represented as "First name Last name" and "Last name, First name". Text fields in data tables often have data with misspelled values or multiple alternatives of the same concept. Reference Materials Toggle sub-navigation.Teams and Organizations Toggle sub-navigation.
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