Data standardization is the process of transforming data from disparate sources and systems into a consistent format. Standardizing data is a critical step in a data quality process because it makes it easier to identify errors, outliers and other issues within your data sets. It also makes your data easier to analyze and ensures that it is reliable.
A data standardization process plays a key role in ensuring that your organization has good data quality practices. It defines rules for how values should appear in your database and helps you quickly identify rule violations, thus establishing a consistent level of quality and consistency across your organization.
When your data is accurate, reliable, and formatted consistently, you’re able to operate efficiently and connect with customers most effectively. However, many businesses struggle to optimize the quality of their data. When the data’s format isn’t consistent, it can be difficult to find and correct errors. These errors result in wasted money, missed opportunities to connect with customers, and excess time spent fixing data issues that could have been avoided.
Transform and manipulate data with hundreds of built-in functions.
With our data standardization tool, you can customize what is right for your business.
After cleansing your data with our standardization solutions, instantly see your results.
Our data standardization tool will help improve the quality of your data by transforming and standardizing it. Data often flows into organizations from differing sources and in various formats. Experian Pandora will clean and remove incorrectly formatted data, giving you the results instantly. This tool will give you the opportunity to take this a step further and allow you to edit transformations, manipulate data and make it easier to then profile that data.
For example, a data standardization solution can format all alphabetical characters in ALL CAPS, standardize Str., Street and St. to ST or remove extraneous characters from phone numbers like parentheses and hyphens.
Hi, I’m Rishi Patel from Experian Data Quality. Today I’m going to take you through the data transformation and standardization capabilities of Experian Pandora.
So we’re looking at the interface of the tool, and we’ve already loaded some data that sits in the repository under it. We’re going to briefly show how we can improve the quality of the data by potentially transforming it and standardizing it, and instantly see the results that we get from it.
Let’s look at the previous customer table and focus in on the telephone number. Here we can see we have telephone numbers of different formats containing parenthesis and hyphens. We can start by looking at how many formats we have in this column. Right click over here, and instantly format this data. We can see that we have 80 groups or formats for this particular table.
In order to standardize this information, I’ll start off by cleaning and removing the noise. I can insert a column over here; select the data cleanse function, and remove the noise. I can see the results in here instantly, and that is part of our prototyping. I can take this a step further and edit this transformation. Go to transformations, edit…and within our expression editor we have a whole host of functions here to transform and manipulate data.
The next thing I want to do is add in some dial code information. Within this table we do have the countries, so I can search for any country function and convert that country to a dial code. I’ll take that as an input, I’ve got the country name, and I’ll select the dial code. What I can then do is concatenate the information together. Taking that dial code, add in a hyphen, and our cleansed number. Now if you go ahead and profile that transformation, we can now see that we’re left with 7 groups, the dominant being a +44, most likely in the UK.
That was a brief demonstration, but I hope it gives you a good idea of the standardization and transformation capabilities of Experian Pandora.
No matter where your organization is collecting data from, it is likely that it’s coming into your system in an endless variety of formats. Inconsistent capitalization, punctuation, obscure acronyms, values in the wrong fields or non-alphanumeric characters in places that they shouldn’t be, are just a few examples of challenges organizations can face when trying to leverage their data as a strategic asset. Standardizing data can help eliminate these challenges by organizing and formatting data in a clear, consistent manner.
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