When pulling data from various applications across the enterprise, we want to receive consistent definitions and formats for the same data. But in practice, this rarely happens. The differences that exist in data sets, especially across applications and even within the same application, make it nearly impossible to use the data for any purpose, from daily operations to business intelligence.
Today, it’s common for enterprises to use multiple SaaS and on-premises applications. Each system has its own requirements, constraints, and limitations. This is why data across applications necessarily contains differences. If we take into account misspellings, abbreviations, nicknames, and typos, we realize that the same value can be represented in hundreds of different ways. This is where the data must be standardized to make it usable for any intended purpose.
In this article, we’ll learn about data standardization: what it is, why you need it, when you need it, and how to do it.
What is data standardization?
In the world of data, a standard refers to a format or representation that every value of a domain must conform to. Therefore, standardizing data means: The process of converting incorrect or unacceptable data representations into an acceptable form.
The easiest way to understand what is “acceptable” is to understand the business requirements. Ideally, organizations must ensure that the data model used by most applications should match their business needs. The best way to standardize data is to align data representation, structure, and definitions with organizational requirements.
Types and examples of data standardization errors
Some examples of how non-standardized data ends up in the system are given below:
- Customer phone numbers are saved as strings in one system, while only numbers containing 8 digits are allowed in another system, resulting in inconsistent data types.
- Customer names were saved as a single field in one system and overwritten as three separate fields for first, middle and last name in another system, resulting in inconsistent structures.
- The customer’s date of birth is in the format of MM/DD/YYY in one system, but in the format of month, day, and year in another system – resulting in format inconsistency.
- Customer gender is saved as Female or Male in one system and as F or M in another system – resulting in inconsistent field values.
In addition to these common cases, spelling errors, conversion errors, and lack of validation constraints can also increase data standardization errors in a dataset.
Why is standardized data needed?
Each system has its own set of specifications and limitations, resulting in unique data models and their definitions. Therefore, the data needs to be transformed before it can be used correctly by any business process.
Typically, you know it’s time to normalize your data when you:
1. Confirm input or output data
Organizations have many interfaces to exchange data from external stakeholders, such as suppliers or partners. Whenever data enters the enterprise or is exported, it must be brought into compliance with the required standards, otherwise the mess of unstandardized data will only grow.
2. Prepare data for BI or analysis
The same data can be represented in many ways, but most BI tools are not designed to handle every possible representation of data values, and may end up processing the same meaning of data in different ways. This can lead to biased or inaccurate BI results. Therefore, before data is entered into a BI system, it must be cleaned, standardized, and deduplicated so that correct and valuable insights can be obtained.
3. Consolidate entities to eliminate duplication
Data duplication is one of the biggest data quality hazards businesses face. For efficient and error-free business operations, duplicate records belonging to the same entity (whether a customer, product, location, or employee) must be eliminated, and an effective deduplication process requires compliance with data quality standards.
4. Inter-organizational data sharing
In order for data to be interoperable between departments, it must be in a format that everyone can understand. In most cases, organizations have customer information in a CRM that sales and marketing people can understand. This can cause delays in task completion and hinder team productivity.
Data Cleaning and Data Standardization
The terms data cleaning and data standardization are often used interchangeably. But there is a subtle difference between the two.
Data cleaning is the process of identifying incorrect or dirty data and replacing it with correct values, while data standardization is the process of converting data values from an unacceptable format to an acceptable format.
The purpose and results of both processes are similar: to eliminate inaccuracies and inconsistencies in the data set. Both processes are critical to a data quality management program and must go hand in hand.
Five steps to standardize data
The data normalization process has four simple steps: define, test, transform, and retest. Let’s look at each step in more detail.
As a first step, it is necessary to determine what standards will meet the needs of the organization. The best way to define standards is to design a data model for the enterprise. The data model will represent the most ideal state that an entity’s data values must conform to. The data model can be designed as:
- Identify data assets critical to business operations. For example, most businesses capture and manage data about customers, products, employees, locations, and more.
- Define the data fields for each asset identified and determine the structural details. For example, you might want to store a customer’s name, address, email, and phone number, where the Name field spans three fields and the Address field spans two fields.
- Assign a data type to each field identified in the asset. For example, the “name” field is a string value, the “phone number” is an integer value, and so on.
- Define character limits (minimum and maximum) for each field. For example, the name cannot exceed 15 characters, the phone number cannot exceed 8 digits, etc.
- Define the pattern that fields must follow – this may not apply to all fields. For example, each customer’s email address should obey the regular expression: [chars]@[chars].[chars].
- Define the format in which certain data elements must be placed in fields. For example, the customer’s date of birth should be specified as MM/DD/YYYY.
- Define the unit of measurement for the value. For example, a customer’s age is measured in years.
- A value range that defines a field that must be derived from a specific set of values. For example, customer age must be a number between 18 and 50, gender must be male or female, etc.
The designed data model can then be placed in an ERD class diagram to help visualize the definition criteria of each data asset and their interrelationships. A sample data model for a retail company looks like this:
Data standardization techniques begin with the second step because the focus of the first step is on defining what should be done—either done all at once or reviewed and updated incrementally at intervals.
Now that the standard has been defined, it’s time to see how well your current data conforms to that standard. Below, we’ll describe several techniques for testing data values for normalization errors and building a normalization report that can be used to troubleshoot the problem.
Parsing records and attributes
Designing a data model is the most critical part of data management. But unfortunately, many organizations fail to design data models and set common data standards in time, or the applications they use do not have customizable data models – causing them to capture data with different field names and structures.
When querying information from different systems, you may notice that some records return the customer’s name as a single field, while other records return three or even four fields covering the customer’s name. Therefore, before filtering any dataset for errors, the records and fields must first be parsed to obtain the components that require test standardization.
Build data analysis report
The next step is to run the parsed component through the analysis system. Data analysis tools report different statistics about data properties, e.g.
- How many values in a column match the required data type, format, and schema?
- What is the average number of characters present in a column?
- What are the minimum and maximum values in the numeric column?
- What are the most common values in the column and how many times do they occur?
Matching and verification modes
Although data analysis tools do report pattern matching, since it is an important part of data standardization testing, we will discuss it in more depth. In order to match the pattern, you need to first define a standard regular expression for the field. For example, the regular expression for an email address 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.
Use data dictionary
You can test the normalization of certain data fields by running the values against a dictionary or knowledge base. They can also be run against custom created dictionaries. This is usually done to match misspellings, abbreviations, or abbreviated names. For example, company names often include terms such as LLC, Inc, Ltd., and Corp. Running them against a dictionary that contains such standard terms can help identify which terms do not meet the required standards or are misspelled.
Standardized test address
When testing data normalization, you may need to test specific fields, such as location or address. Address standardization is the process of checking address formats against authoritative databases (such as transportation industry address specifications) and converting address information into an acceptable standardized format.
Standardized addresses should be correctly spelled, formatted, abbreviated, geocoded, and appended with accurate ZIP+4 values. All addresses that do not meet the required criteria (especially addresses that are supposed to receive deliveries and shipments) must be flagged so that they can be converted if necessary.
In the third step of the data normalization process, the final step is to convert the unqualified values into a standardized format. This can include:
- Convert field data types, such as converting a phone number from a string to an integer data type and eliminating any characters or symbols present in the phone number to obtain an 8-digit number.
- Convert modes and formats, such as converting dates present in a data set to MM/DD/YYYY format.
- Convert units of measurement, such as converting product prices to US dollars.
- Expand abbreviated values into the full form, such as replacing abbreviated US states: NY to New York, NJ to New Jersey, etc.
- Eliminate the noise present in the data values to get more meaningful information, such as removing LLC, Inc. and Corp. from the company name to get the actual name without any noise.
- Reconstruct values in a standardized format in case they need to be mapped to new applications or data centers (e.g. master data management systems).
All of these transformations can be done manually (which can be time-consuming and inefficient), or you can use automated tools to help clean the data by automating standard testing and transformation stages.
Retest and meet standards
After the transformation process is complete, it is a good idea to retest the data set for normalization errors. Pre- and post-standardization reports can be compared to understand how well the configured process fixed data errors and how to improve those errors for better results.
Digital transformation often begins with data standardization, or the process of converting all various data sets into a consistent format. It’s not exciting, but it’s true. Want to learn more about your organization? Standardized data. Do you consider it an innovation to project a holographic digital twin of a machine anywhere in real time? Standardized data. Need to reduce costs across your entire supply chain and maintenance cycle without time-consuming audits? you understood.
Information is power, but without the right context, that power can be illusory. For example, suppose you are overseeing the global rollout of a new car. The car sold 5 million units in the first 12 months. Sounds awesome, right? So what if every other active vehicle in your organization (including older models) sold 7 million units during the same period? Also, what if 93% of those 5 million units come from just one region? 54% of these were returned and refunded within the first three months of sale. Scenario is everything and a clear scenario is only possible when the data is ready, available and reliable.