Key Takeaways:
Grasp what data normalization and standardization are.
Discover the benefits of having clean data.
Learn practical ways to start doing data normalization and standardization.
Think of your data as your wardrobe: without organization, finding the perfect outfit can feel impossible. Normalization acts like sorting your clothes into categories (e.g. shirts, pants, jackets), so everything has its place. Standardization ensures the hangers match, and every item is clearly labeled, making it effortless to grab what you need. Join us as we break down the concepts of data normalization and standardization, explain how they work, and show you why these processes are essential for maintaining clean, efficient data and smoother operations.
What Is Data Normalization?
At its core, data normalization is about structuring your data efficiently to eliminate redundancy and inconsistency. For example, if customer addresses appear multiple times across different records with slight variations (e.g., “123 Elm St” vs. “123 Elm Street”), normalization means that these duplicates are resolved.
To get started with data normalization, review your systems. Many systems aren’t designed for normalized data, leading to inconsistent inputs. For instance, an online form might allow users to input a state name in various formats: “NY,” “New York,” or even just “NYC.” These discrepancies make analysis challenging. Larger companies often avoid this issue by not allowing freeform inputs, opting instead for dropdown menus or predefined options.
“To get started with data normalization, review your systems. Many systems aren’t designed for normalized data, leading to inconsistent inputs.”
– Technology Therapy® Group
What Is Data Standardization?
While normalization focuses on structure, standardization focuses on consistency in how data is recorded. This involves aligning data to a common format across all systems. For example, instead of allowing variations like “NY” and “New York,” standardization ensures only one format is used. Or when phone numbers are recorded in a consistent style, such as (123) 456-7890.
We recommend reviewing your forms and processes, so data is captured in a standardized way. This isn’t just about front-end design. Team members entering data on the back end also need clear guidelines to avoid inconsistencies.
Examples of Inconsistent Data Problems
Inconsistent data can wreak havoc on your business, making it difficult to analyze customer behavior or deliver effective marketing campaigns. These issues often arise from two key sources: how customers fill out forms and how employees input data.
Customer Forms
When customers fill out forms on your website, inconsistencies are common. For example, state names might appear as “New York,” “NY,” or even “NYC,” depending on how each person chooses to enter the information. Similarly, phone numbers can show up in various formats, such as (123) 456-7890, 123-456-7890, or 1234567890. These inconsistencies make it hard to group or validate data, leading to errors in marketing and customer segmentation.
Employee Input
Data entered manually by employees into your CRM or POS system can also introduce errors. For instance, duplicate records often happen when one employee enters “Jane Smith” while another inputs “J. Smith” for the same person. Product descriptions are another pain point—one employee might label an item as “Red T-Shirt,” while another uses “T-Shirt Red” or “Shirt, Red.” These variations create confusion and inefficiencies, making data less reliable for decision-making.
“When you normalize and standardize data, you eliminate errors, improve targeting, and create better experiences for your customers. And you get the bonus of making your operations more efficient.”
– Technology Therapy® Group
The Benefits of Clean Data: Examples in Action
Clean data is crucial for effective marketing and smooth operations. Imagine sending a marketing email to your New York customers. But you end up reaching only 70% of them, because some are listed as “New York,” others as “NY,” and a few as “NYC.” Without consistency, targeting becomes a challenge. By adding dropdown menus to your forms and cleaning up existing records, you can make sure that all New York customers are correctly grouped, allowing your emails to reach 100% of your audience and boosting campaign success.
Standardization solves similar issues. For example, an employee might create two accounts for the same customer, one as “John Smith” and another as “J. Smith.” This duplication wastes resources and frustrates customers. With clear entry rules and tools that automatically flag duplicates, you can merge these accounts into one. The result? A smoother customer experience and a more reliable database.
When you normalize and standardize your data, you eliminate errors, improve targeting, and create better experiences for your customers. And you get the bonus of making your operations more efficient.
Steps to Start Normalizing and Standardizing Your Data
Still feeling a bit lost when it comes to data normalization and standardization? Here are two ways to start cleaning up your data so you have a tidier “data wardrobe” for your business.
Evaluate Your Systems
Start by reviewing your current processes for capturing and storing data. Identify areas where inconsistencies or redundancies occur, such as customer intake forms or back-end setups.
Collaborate Across Teams
Inconsistent data entry often happens when team members use varying processes. Standardize workflows to ensure uniformity.
3 Tips to Guide Your “Data Wardrobe” Cleanup
Once you’ve evaluated your systems, collaborated across your teams, and tapped into automation, it’s time for the next phase of tidying up your metrics.
1. Improve Your Online Forms
- Dropdown Menus
Replace freeform text fields with dropdown options for consistent inputs (e.g., standardized state abbreviations). - Input Formatting
Use input masks for uniform formatting, such as (XXX) XXX-XXXX for phone numbers. - Mandatory Fields
Make sure that essential fields like email addresses are required to avoid incomplete data.
2. Train Your Employees and Implement Guidelines
- Clear Policies
Develop a standardized data entry process. For example, always use proper capitalization and consistent naming conventions. - Validation Rules
Configure your CRM or POS system to flag incomplete or incorrectly formatted entries.
3. Leverage Technology for Automation
- Data Cleaning Tools
Leverage software like Excel or CRM-integrated tools to identify duplicates or inconsistencies. - Real-Time Validation
Ensure forms and systems prompt corrections for errors, like invalid email formats or incomplete addresses.
Taking Small Steps Toward an Organized Data Wardrobe
Just like tidying up your wardrobe, implementing data normalization and standardization can seem daunting at first. But with a little effort, the results are transformative. By using the right tools and processes, you’ll create a streamlined, well-organized data system where everything fits and functions perfectly. This “clean data closet” will not only simplify your operations but also set your business up for long-term success.
Need Support as You Clean Up Your Data?
Work with a TTG mentor to help you learn the ropes of data normalization and standardization. Connect with us today to get started!
