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?
Here’s everything you need to know.
Let’s dive in.
What is data validation and why it matters?
The confirmation that your data is accurate, error-free, clear, and reliable is data validation.
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.
How to perform data validation?
There are two ways to perform data validation:
Number one: 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.
Why In-House Data Quality Projects Fail
Be aware of accurate data matching as a data quality challenge that cannot be managed in-house.
Number two: 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.
Using a self-service data validation tool
Data validation tools offer simple and intuitive features to automate your data quality workflows.
With such a tool, 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.
In an age when data errors can result in the loss of billions of dollars, it’s high time we implement data quality guidelines at various stages of our data workflow – after all, data integrity ensures the legitimacy of your conclusions.
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