Data standardization guide: Types, benefits, and process

When you pull data from various applications fitted across the enterprise, you expect to receive a consistent definition and format of the same information. But in reality, this is rarely the case. The variations present in datasets – across applications and even within the same application – make it almost impossible to use data for any purpose – from routine operations to business intelligence.

Today, an average enterprise uses multiple SaaS and in-house applications. Every system comes with its own set of requirements, restrictions, and limitations. This is why data hosted across applications is bound to contain discrepancies. And if we take misspellings, abbreviations, nicknames, and typing errors into consideration, we realize the same values can have hundreds of different representations. This is where it becomes imperative to standardize data in order to make it usable for any intended purpose.

In this blog, we will learn everything about data standardization: what it is, why and when you need it, and how you can do it. Let’s get started.

What is data standardization?

In the data world, a standard refers to a format or representation that every value of a certain domain must conform to. Hence, standardizing data means:

The process of transforming an incorrect or unacceptable representation of data into an acceptable form.

The easiest way to know what is ‘acceptable’ is to understand your business requirements. Ideally, organizations must ensure that the data model utilized by most – if not all – applications should conform to their business needs. The best way to achieve standardization of data is to align your data representation, structure, and definition to organizational requirements.

Types and examples of data standardization errors

A few examples of how unstandardized data may end up in the system are given below:

  1. Customer Phone Number is saved as a string in one system while it is only permissible to be a number containing 8 digits in another system – leading to data type inconsistency.
  2. Customer Name is saved as a single field in one system while the same is covered as three separate fields in another system for first, middle, and last names – leading to structural inconsistency.
  3. Customer DOB has the format MM/DD/YYY in one system, while the same has the format Month Day, Year in another system – leading to format inconsistency.
  4. Customer Gender is saved as Female or Male in one system, while the same is saved as F or M in another system – leading to domain value inconsistency.

Apart from these common scenarios, misspellings, transcribing errors, and lack of validation constraints can increase data standardization errors in your datasets.

Why do you need to standardize data?

Every system has its own set of limitations and restrictions, leading to unique data models and their definitions. For this reason, you may need to transform data before it can be correctly consumed by any business process.

Usually, you know it is time to standardize data when you want to:

1. Conform incoming or outgoing data

An organization has many interfaces that exchange data points from external stakeholders, such as vendors or partners. Whenever data enters an enterprise or is exported out, it becomes necessary to conform data to the required standard, otherwise the unstandardized data mess just gets bigger and bigger.

2. Prepare data for BI or analytics

Same data can be represented in multiple ways, but most BI tools are not specialized to process every possible representation of data values and may end up treating the same meaning data differently. This can lead to biased or inaccurate BI results. Therefore, before you can feed data into your BI systems, it must be cleaned, standardized, and deduplicated, so that you can attain correct, valuable insights.

3. Consolidate entities to eliminate duplicates

Data duplication is one of the biggest data quality hazards businesses deal with. For efficient and error-free business operations, you must eliminate duplicate records that belong to the same entity (whether for a customer, product, location, or employee), and an effective data deduplication process requires you to comply with data quality standards.

4. Share data between departments

For data to be interoperable between departments, it has to be in a format that is understandable by everyone. Mostly, organizations have customer information in CRMs that is understood by the sales and marketing folks. This can introduce delays in task completion and roadblocks in team productivity.

Data cleansing versus data standardization

The terminologies data cleansing and data standardization are usually used interchangeably. But there is a slight difference between the two.

Data cleansing is the process of identifying incorrect or dirty data and replacing it with correct values, while data standardization is the process of transforming data values from an unacceptable format to acceptable format.

The purpose and outcome of both these processes is similar – you want to eliminate inaccuracy and inconsistency from your datasets. Both processes are vital to your data quality management initiative and must go hand-in-hand.

How to standardize data?

A data standardization process has four simple steps: define, test, transform, and retest. Let’s go over each step in a bit more detail.

1. Define a standard

In the first step, you must identify what standard meets your organizational needs. The best way to define a standard is by designing a data model for your enterprise. This data model will represent the most ideal state that data values for a certain entity must conform to. A data model can be designed as:

  1. Identify the data assets crucial to your business operation. For example, most enterprises capture and manage data for customers, products, employees, locations, etc.
  2. Define the data fields of each asset identified and decide on the structural details as well. For example, you may want to store a customer’s Name, Address, Email, and Phone Number – where the Name field spans over three fields and Address field spans over two.
  3. Assign a data type to every field identified in the asset. For example, the Name field is a string value, Phone Number is an integer value, and so on.
  4. Define character limits (minimum and maximum) for each field. For example, a Name cannot be longer than 15 characters and Phone Number cannot be more than 8 digits, etc.
  5. Define the pattern that fields must adhere to – this may not be applicable to all fields. For example, every customer’s Email Address should adhere to the regex: [chars]@[chars].[chars].
  6. Define the format in which certain data elements must be placed within a field. For example, a customer’s DOB should be specified as MM/DD/YYYY.
  7. Define the measuring unit for numeric values (if applicable). For example, customer’s Age is measured by Years.
  8. Define the value domain for fields that must be derived from a certain set of values. For example, customer Age must be a digit within 18 and 50, Gender must be Male or Female, and so on.

A designed data model can then be placed in an ERD class diagram to help visualize the defined standard for every data asset and how they relate to each other. An example data model for a retail company is shown below:

2. Test for standard

Data standardization techniques start at the second step, since the first step focuses on defining what should be – something that is done once or incrementally reviewed and updated every once in a while.

You have defined the standard and now it’s time to see how well the current data conforms to it. Below, we go through a number of techniques that test data values for standardization errors and build a standardization report that can be used for fixing the issues.

a. Parsing records and attributes

Designing a data model is the most crucial part of data management. But unfortunately, many organizations do not design data models and set common data standards in time or the applications they use do not have customizable data models – leading them to capture data in varying field names and structure.

When you query information from different systems, you might notice that some records return a customer’s name as a single field while others return three or even four fields covering a customer’s name. For this reason, before any dataset can be screened for errors, you must start by parsing records and fields to attain the components that needed to be tested for standardization.

b. Building data profile report

The next step is to run the parsed components through a profiling system. A data profiling tool reports on different statistics about data attributes, such as

  • How many values in a column follow the required data type, format, and pattern?
  • What is the average number of characters present in a column?
  • What are the most minimum and maximum values present in a numeric column?
  • What are the most common values present in a column and how many times do they appear?

c. Matching and validating patterns

Although data profiling tools do report on pattern matches, since it is an important part of data standardization testing, we will discuss it in a little more depth. To match patterns, you need to first define a standard regular expression for a field, for example, a regular expression for email addresses can be: ^[a-zA-Z0-9+_.-]+@[a-zA-Z0-9.-]+$. All email addresses that do not follow the given pattern must be flagged during testing.

d. Using dictionaries

Certain data fields can be tested for standardization by running values against dictionaries or knowledge bases. You can also run them against custom-created dictionaries. This is often done to match misspellings, abbreviations, or shortened names. For example, company names usually include terms like LLC, Inc, Ltd., and Corp., etc. Running them against a dictionary full of such standard terms can help identify which ones do not follow the required standard or are spelled incorrectly.

Read more about Using wordsmith to remove noise and standardize data in bulk.

e. Testing addresses for standardization

While testing data for standardization, you may need to test specialized fields, such as locations or addresses. Address standardization is the process of checking the format of addresses against an authoritative database – such as USPS in the US – and converting address information into acceptable, standardized format.

A standardized address should be correctly spelled out, formatted, abbreviated, geocoded, as well as appended with accurate ZIP+4 values. All addresses that do not conform to the required standard (especially addresses that are supposed to receive deliveries and shipments) must be flagged so that they can be transformed as needed.

Read more: A quick guide to address standardization and verification.

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3. Transform

In the third step of the data standardization process, it is finally time to convert the non-conforming values into a standardized format. This can include:

  • Transforming the field data types, such as converting Phone Number from string to an integer data type and eliminating any characters or symbols present in phone numbers to attain the 8-digit number.
  • Transforming patterns and formats, such as converting dates present in the dataset to the format MM/DD/YYYY.
  • Transforming measurement units, such as converting product prices to USD.
  • Expanding abbreviated values to complete forms, such as replacing the abbreviated U.S. states: NY to New York, NJ to New Jersey, and so on.
  • Removing noise present in data values to attain more meaningful information, such as removing LLC, Inc., and Corp. from company names to get the actual names without any noise.
  • Reconstructing the values in a standardized format in case they need to be mapped to a new application or a data hub, like a master data management system.

All these transformations can be done manually – which can be time consuming and unproductive – or you can use automated tools that can help clean data by automating the standard testing and transforming phases for you.

4. Retest for standard

Once the transformation process is over, it is a good practice to retest the dataset for standardization errors. The pre and post standardization reports can be compared to understand the extent to which data errors were fixed by the configured processes and how they can be improved to reach better results.

Using self-service data standardization tools

Nowadays, data is manually entered, as well as automatically captured and generated. In the midst of handling large volumes of data, organizations are stuck with millions of records containing inconsistent patterns, data types, and formats. And whenever they want to use this data, teams are bombarded with hours of manual format checking and correcting every little detail before the information can be deemed useful.

Many businesses are realizing the importance of providing their teams with self-service data standardization tools that come with inbuilt data cleansing features too. Adopting such tools can help your team execute complex data cleansing and standardizing techniques on millions of records in a matter of minutes.

DataMatch Enterprise is one such tool that facilitates data teams in rectifying data standardization errors with speed and accuracy, and allows them to focus on more important tasks. To know more about how DataMatch Enterprise can help, you can download a free trial today or book a demo with an expert.

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