Data Standardization: Define, Test, and Transform

As organizations shift towards creating a data culture across the enterprise, many are still struggling to get their data right. Pulling data from disparate sources and having different formats and representations of what the same information is supposed to be creates serious hurdles in your data journey. Teams encounter delays and errors as they perform their routine operations or extract insights from data sets. Such issues are forcing companies to introduce a data standardization mechanism – which ensures that data is present in a consistent and uniform presentation across the enterprise.

Let’s take a deeper look at the data standardization process: what it means, the steps involved, and how you can achieve a standardized data display in your organization.

What is data consolidation?

Simply put, data standardization is the process of converting data values ​​from an incorrect format to a valid format. To enable a unified, unified and consistent display of data across the organization, the data values ​​must conform to the required standard – in the context of the data fields to which they belong.

Example of data standardization errors

For example, the same customer record located in two different locations should not contain discrepancies in first and last name, email address, phone number and residential address:

Source 1
Noun E-mail address Telephone number DOB sex home adress
John O’Neill [email protected] 5164659494 14/2/1987 M 11400 Watts Olympic BL # 200
Source 2
First Name nickname E-mail address Telephone number DOB sex home adress
Jonathan O’Neill john.neal_gmail.com +1 516-465-9494 14/2/1987 AD Mention 11400 Watts Olympic 200

In the example above, you can see the following types of inconsistencies:

  1. structural: The first source covers the name of the customer as one field, the second source stores it as two fields – first name and last name.
  1. pattern: The first source has a valid email pattern superimposed on the email address field, while the second source is conspicuously missing @ Code.
  1. data type: The first source only allows numbers in the phone number field, while the second source has a string type field that contains symbols and spaces as well.
  1. appearance: The first source contains the date of birth in the format MM / DD / YYYY, and the second source has the format DD / MM / YYYY.
  1. field value: The first source allows storing the gender value as M or F, and the second source stores the full form – male or female.

These data inconsistencies lead you to make major mistakes that can waste your business a lot of time, cost, and effort. For this reason, a comprehensive mechanism is implemented for data standardization It is critical to keep your data clean.

How to consolidate data?

Data consolidation is a simple four-step process. But depending on the nature of the inconsistencies in your data and what you’re trying to achieve, the methods and techniques used for standardization can vary. Here, we provide a general rule of thumb that any organization can use to work around their standardization errors.

  1. Determine what is the standard

To get to any country, you must first determine what the country actually is. The first step in any data consolidation process is to determine what is required to achieve it. The best way to find out what you need is to understand the business requirements. You need to examine your business processes to see what data is needed and in what format. This will help you set the basis for your data requirements.

Defining a data standard helps define:

  • Data assets critical to your business process,
  • data fields needed for those assets,
  • The data type, format, and style whose values ​​must correspond to,
  • The range of acceptable values ​​for these fields, etc.
  1. Testing data sets against the specified criterion

Once you have a standard definition, the next step is to test how well your data sets perform against it. One way to evaluate this is to use data profiling Tools that generate comprehensive reports and find information such as the percentage of values ​​that match data field requirements, such as:

  • Do the values ​​follow the required data type and format?
  • Do the values ​​fall outside the acceptable range?
  • Do the values ​​use abbreviated forms, such as abbreviations and nicknames?
  • be uniform addresses As needed – like USPS standardization for US addresses?
  1. Converting non-matching values

Now it’s finally time to convert the values ​​that don’t match the specified standard. Let’s take a look at the common data transformation techniques used.

Some data fields must first be analyzed to obtain the necessary data components. For example, parsing the name field to separate first, middle, and last names, as well as any prefixes or suffixes present in the value.

  • Data type and format conversion

You may need to remove non-matching characters during conversion, for example, remove symbols and alphabets from a number-only phone number.

  • Pattern matching and validation

Pattern transformation is performed by creating a pattern regular expression. For example, an email address can be validated using regex: ^[a-zA-Z0-9+_.-][email protected][a-zA-Z0-9.-]+ dollars. For values ​​that do not conform to the regex, they must be parsed and converted to the specified pattern.

Company names, addresses, and people’s names often contain short forms that can cause your data set to contain different representations of the same information. For example, you may have to expand states’ states, such as turning New York into New York.

  • Noise removal and spelling correction

Certain words don’t add any meaning to the value, and instead introduce a lot of noise into the data set. These values ​​can be identified in a dataset by running them against a dictionary containing these words, flagging them, and deciding which ones to be permanently removed. The same process can be performed to find spelling and typing errors.

  1. Retest the data set against the specified criterion

In the final step, the transformed data set is retested against the specified criterion for the percentage of data standardization errors that are fixed. For errors still in your dataset, you can adjust or reconfigure your methods and run the data through the process again.

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The amount of data being created today—and the variety of tools and techniques used to capture that data—lead companies to confront the horrific data chaos. They have everything they need, but they’re not quite sure why the data isn’t there in an acceptable, usable form. Adopting data standardization tools can help correct such inconsistencies and enable a much-needed data culture across your organization.

Originally Posted in Martech.zone

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