![]() In the example above, you can see the following types of inconsistencies: Snippet from DataMatch Enterprise Examples of Data Standardizationįor example, the same customer’s record residing at two different locations should not contain the discrepancies in first and last names, email address, phone number, and residential address: Source 1 To enable a standardized, uniform, and consistent data view across the organization, the data values must conform to the required standard – in context of the data fields they belong to. What is data transformation? Simply put, data standardization is the process of transforming data values from an incorrect format to a correct one. Let’s take a deeper look at the data standardization process: what it means, the steps it entails, and how you can achieve a standard data view in your enterprise. Such problems force businesses to introduce a data standardization mechanism – that ensures data is present in a consistent and uniform view across the organization. Teams experience delays and mistakes while carrying out their routine operations or extracting insights from datasets. Pulling data from disparate sources and getting varying formats and representations of what is supposed to be the same information – causes serious roadblocks in your data journey. While organizations shift towards establishing a data culture across the enterprise, many are still struggling to get their data right. The Piqiang Fault is a left-lateral fault.Hacker at computer data reflecting in eyeglasses The unit of rock layers that moves toward the viewer relative to the red fault line is on the left. This method always works, no matter from which direction you are looking at the boundary!įigure 2.6.8. If it is on the left side, it is left-lateral.
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