Ever generated a report only to realize that most of your contact information’s email addresses are not valid? That’s a failed case of data validation.
Similarly, addresses with incomplete ZIP codes, phone numbers with incomplete city codes are all examples of when input data is not valid and were not caught during a data cleanup or ETL process.
How exactly do you prevent such occurrences from happening and how do you ensure data validation to be part of your data workflow?
The answer is data validation.
What is Data Validation and Why It Matters?
Data validation is the process of ensuring that data is accurate, complete, and consistent before it is used in decision-making or analytics.
- Accurate means that all data is free from errors and reflects the true value or information it represents.
- Complete: All necessary data is present and no critical information is missing.
- Consistent: Data is uniform across all systems and does not contain conflicting or duplicate values.
When validating data, the data professional applies rules and checks to verify that it meets the required standards and is free from errors, such as incorrect formats, duplicates, or outdated information. By validating data, organizations maintain data integrity, reduce the risk of errors, and ensure that their insights and decisions are based on reliable information.
Without data validation, you will always run the risk of using flawed data, resulting in inaccurate reports, costly mistakes and potential data breaches with heavy penalties. You can prevent all this from happening if you’re careful with your input data and be aware of the potential problems that your data sets may be prone to.
Errors are bound to happen during the data input process and while data is seldom 100% perfect, data validation helps minimizes erroneous data from going undiscovered and becoming a bottleneck for your data projects.
The end goal of data validation is to ensure that you have accurate data at your disposal – whether it’s for a business case or for a migration project, data validation matters.
How Do You Validate Input Data?
There are multiple data validation software solutions available to validate input data, helping businesses profile their data to evaluate the kind of errors plaguing their data. That said, businesses must not rely entirely on software solutions to validate their data. Part of data validation also includes controlling your input data from being erroneous by implementing data validation rules on data collection points such as web and application forms.
You can ensure these errors don’t happen by implementing rules on how you want your data to be stored and maintained. Validation rules will help your company follow standards that will make it efficient to work with data. At the time of a critical report or analysis, you won’t have to worry about data is valid or not.
Some of the rules you can apply are:
- Defining the data type that your database will hold (integer, float, string etc)
- Defining the range (for example no more than 11 numbers for phone numbers)
- Uniqueness of the data
- Rejecting all null values
- Accept only work or company domain emails
- Accept only phone numbers with complete country + city codes
Instances Where Data Validity Goes Beyond Basic Flaws
The greater challenge with data lies not in countering basic flaws as typos or character mistakes – rather it is human mistakes and manipulation of data that poses the most critical challenge.
Here are common instances where data validity becomes complicated and can cause significant issues if not handled with care.
Submitting Wrong Data
Until manual data entry is a practice, data validity will remain a challenge. It’s not uncommon for users to submit the wrong files into the system. Take, for example, a user in a hospital accidentally submitting a man’s report in his wife’s patient portal or the same user submitting diabetic patients record instead of cancer patient records for reporting or analysis. Such errors can lead to potential disasters if checks are not kept in place.
Working with Outdated Records
When a data source is not regularly updated, it results in duplicates and other redundancies that prevent users from accessing updated records. For example, a bank being overwhelmed with outdated customer transaction records, having to manually verify entries every time it’s closing time.
Duplicate Data that Go Undetected
Duplicate data is a headache for most companies. There are so many factors that cause the duplication of data that preventing it is more of a challenge. From accidental user entry to system errors to disparate data sources – the causes for duplicate data are endless. What’s more concerning is that most of this data goes undetected. Even with the use of unique identifiers, data is still easily duplicated.
Take for example a restaurant that asks their customers to give feedback. One customer can be recorded multiple times based on the quality of their personal info. Some customers may write their full names the first time, the second time it may just be the first name, the third time may just be a nickname. Each time the customer changes any of their personal info – be it a phone number, an address or a name, a duplicate record is created. Basic data validity protocols, in this case, would not be applicable. Companies would need powerful data matching software solutions to overcome this challenge of duplicate records to ensure that their data remains clean and usable.
Data validation is not just fixing typos or basic errors, but also taking it to the next level, where you will ensure its reliability and its integrity.
Common Challenges with Data Validation
Common challenges associated with data validation include:
Data Inconsistency
When pulling data from different sources, formats and structures often clash. This leads to mismatched information, making it hard to validate across the board. Consistency is key, but it’s a challenge when systems don’t align.
Data validation software can automatically align data from different sources by applying consistent rules and formats, ensuring uniformity across all datasets.
Duplicate Records
Duplicates are sneaky. They creep in through errors or system quirks, cluttering your data. Spotting and merging them is a tedious but necessary task to maintain data integrity.
Advanced validation tools detect and merge duplicates quickly, maintaining a single, clean record for each entry without manual intervention.
Human Error
Manual data entry is prone to mistakes—typos, wrong formats, or misplaced numbers. These small errors can snowball into big problems if not caught early.
Data validation software reduces human error by enforcing strict input rules and providing real-time feedback, catching mistakes before they enter your database.
Outdated Information
Data doesn’t stay relevant forever. Without regular updates, you risk working with stale information. Ensuring data is current is an ongoing battle.
Data validation software can regularly update and validate data against external sources, keeping your information fresh and relevant.
Complex Validation Rules
Setting up complex rules to catch every possible error is daunting. It requires expertise and constant fine-tuning. Even the smallest oversight can let errors slip through.
Data validation software simplifies the process by automating the creation and enforcement of complex rules, ensuring thorough error checking without extensive manual effort.
How To Perform Data Validation?
There are two ways to perform data validation:
- Validation by Manually Coded Scripts: If you have great developers on board who are aware of the challenges of your data, writing a script may be a good way to perform data validation. You may have to compromise on time and accuracy though if you choose this method. Writing data validation scripts take months if not years to derive results depending on the complexity and size of your data. For enterprises and large businesses, scripting is not a viable data validation method.
- Validation by Programs: Automation is the need of the day. Validation can be achieved by using software programs that allow you to develop your own validation rules, standardize your data, remove duplicates and ensure your data is good enough for use.
Do note that data validation is not just a database process. A double layer of precaution would be to implement rules on data capture followed by data validity checks before the data is extracted for use.
Data Validation Tools
There are many tools used for validating data, let’s explore three of them:
Excel for Data Validation
Excel is a commonly used tool for data validation. One of its key functions is the ability to create rules that restrict the type of data entered into cells. For example, you can set up a dropdown list, restrict inputs to specific formats like dates or numbers, and even display custom error messages if data is entered incorrectly.
To set up data validation in Excel, follow these steps:
- Select the Cells: Highlight the cells where you want to apply data validation.
- Access Data Validation: Go to the “Data” tab on the Ribbon and click on “Data Validation.”
- Choose Validation Criteria: In the Data Validation dialog box, you can specify the type of validation you need. Options include whole numbers, decimals, dates, lists, and more.
- Set Input Messages and Error Alerts: Excel allows you to create custom input messages that guide users on what data to enter. You can also set up error alerts to notify users when they enter invalid data.
Excel’s data validation feature is particularly useful for preventing errors in large datasets, ensuring consistency in data entry, and reducing the need for manual data cleaning later. Whether you’re managing a budget, tracking project progress, or maintaining a database, Excel provides a simple yet effective way to maintain data integrity.
Google Sheets for Data Validation
Similar to Excel, Google Sheets allows you to set rules that control what type of data can be entered into cells. You can create dropdown lists, restrict data to specific ranges, and set up custom error messages to guide users during data entry.
To implement data validation in Google Sheets:
- Select the Cells: Highlight the cells where you want to apply validation.
- Open Data Validation: Go to the “Data” menu and select “Data validation.”
- Set Criteria: Choose the criteria for the type of data you want to allow, such as numbers, text, or dates. You can also create a dropdown list by selecting “List of items.”
- Show Validation Help Text: Google Sheets lets you display help text that explains the validation rule, helping users input the correct data.
- Set Error Alerts: Customize the error message that will appear when invalid data is entered.
One of the standout features of Google Sheets is its real-time collaboration. When data validation rules are set up, they apply to all users, helping teams maintain data integrity across shared documents. This is particularly useful for remote teams or any group project where multiple people are entering data. Google Sheets’ data validation is a great way to maintain accuracy and consistency in your data, even when multiple collaborators are involved.
Data Ladder for Data Validation
Data Ladder offers a comprehensive solution for data validation, particularly suited for enterprises that need to manage large and complex datasets. Unlike more basic tools, Data Ladder specializes in advanced data cleaning, matching, and standardization. The data validation software ensures that your data is valid and consistent and free from duplicates. It automates much of the data validation process, saves time, and reduces human error. With its powerful algorithms and user-friendly interface, Data Ladder helps organizations maintain high data quality standards, crucial for accurate reporting and decision-making.
For example, you can profile your data as the first step of the validation check to verify issues with the data. This would include checking for invalid, null, void data fields as well as fields with missing or incorrect, inaccurate information. Furthermore, it will also help you validate your data based on pre-defined business rules such as validating contact’s gender information by using a pre-defined gender rule on the data.
Additionally, you can also match multiple data sets to remove duplicates which we’ve seen is one of the most critical challenges with achieving data validation. You can also use address verification and validation function which validates your contact information’s postal addresses against a reliable government database. For businesses, address validation remains a crucial challenge costing them millions of dollars in revenue loss, return claims and logistic errors.
Data validation, therefore, is an all-encompassing function that you will need for every data column of your data set. From entity name to numbers, physical addresses to email addresses, every data set needs to be validated for its accuracy, completeness, and validity before it can be put to use.
This tool is particularly beneficial for companies dealing with vast amounts of data across multiple sources, as it ensures that the data is clean, accurate, and ready for use in business processes or analytics.
Want to see it in action? Download it for free and try it out.
