What is data validation?

Data validation is the process of verifying and validating data that is collected before it is used. Any type of data handling task, whether it is gathering data, analyzing it, or structuring it for presentation, must include data validation to ensure accurate results. Sometimes it can be tempting to skip validation since it takes time. However, it is an essential step toward garnering the best results possible.

Several checks are built into a system which ensures the data being entered and stored has logical consistency. With the advancement in technology, data validation today is much faster. Most of the data integration platforms incorporate and automate the data validation step so that it becomes an inherent step in the entire workflow rather than an additional one. In such automated systems, there is little human intervention required. Data validation becomes essential because poor-quality data causes issues downstream, and there are higher costs attached to cleansing data if done later in the process.

The data validation process has gained significant importance within organizations involved with data and its collection, processing, and analysis. It is considered to be the foundation for efficient data management since it facilitates analytics based on meaningful and valid datasets.

How is data validation used in a business environment?

Any business, small or global, should incorporate data validation processes in their workflows. Inaccuracies in data end up causing a waterfall effect that could result in lost revenue, missed opportunities, and bad decisions.

Unless the organization has the utmost confidence in the integrity of its data, there’s no guarantee that the outcome of its analysis will be accurate. Since most conclusions are based on facts and figures, it is essential that proper data checks are in place so that they can ensure the quality and soundness of the data being gathered. The outcome is only as good as the input, hence the analytics being valid only if the data they use is validated.

The business development team of any company depends heavily on data that is accurate and reliable. Imagine the amount of wasted effort and time if the team is working with incomplete numbers or invalid email addresses. If the data goes through proper validation checks when it is being collected, the final data set will contain high quality, verified information that is valuable to the team.

Properly validated data is required at all stages of the business process. Considering the massive amounts of data an organization works with,manual processing is usually impossible. Data validation software, on the other hand, operates in the background and provides stakeholders with reliable information that can be used to make relevant, accurate decisions in the given scenario.

Along with validating the inputs and values of data, the data model too must be vetted. Data models that are built incorrectly or without a structure will create issues when applications and software try to use the data files. For any analysis to be accurate, the data files being used must be validated for both its content and structure. Cleansing the data to ensure its integrity will eventually confirm the legitimacy of the conclusions derived.

Rules for consistency

The rules for consistency ensure the integrity of the data. Some of these are often used in our daily lives, like spell check or the minimum length for a password. Organizations, however, need more stringent checks in place due to the massive amounts of data they use and the critical nature of the decision-making process that uses the same data. Therefore, they have a defined set of rules that helps in upholding the standards by ensuring data is stored and maintained in a certain way. This also makes the entire workflow efficient and effective. Here are some common data validation rules that check for data integrity and clarity.

Data type

This rule ensures the data being entered has the correct data type as required by the field, for example, text. If any other data value is entered, it should be rejected by the system.

Code check

This data validation rule checks whether the value is from a list of accepted values, like the zip code of a certain location.


This rule verifies if the data entered falls into the range specified, for example, between 20 and 30 characters. Values outside the predefined range are invalid and not accepted by the system.

Consistent expressions

It is important that the data entered makes logical sense. For instance, the date of leaving must be later than the date of joining. This type of rule ensures logical consistency of the data entered.


Several data types have a defined format, like a date. The validation rule in this case ensures every input adheres to the required format.


Some data fields need to contain unique values, if defined. For example, no two users can use the same phone number in case it is used to identify individual users. The uniqueness validation rule checks the entered data against the stored values and ensures no two records have the same value for that field.

No null values

Certain input fields must contain values and cannot be empty. This rule ensures the same.

Standards for formatting

Along with validating the data, its structure must also be validated to ensure that the data model being used is compatible with the applications that are being used to work with the data. There are many agencies—private, government, and non-profit—that help in maintaining the formats of the files and their standards. The structures of the files holding data are continuously being worked upon—defining, documenting, and developing them. It is essential to thoroughly understand the structure and standards of the data model that contains the dataset being used. Without such a validation, one might end up with data files that are not compatible with other data sets or applications that need to use them.

How to perform data validation

Data validation is used by processes like extract, transform, load where data is moved from a data source to a target warehouse. The validation certifies the accuracy of end results. The following steps are taken to perform data validation.

  1. To begin, a sample of data is determined. This is useful when the data set is massive, because it is easier to validate a part rather than the whole set. The volume of the sample needs to be in accordance with the size of the data set and an acceptable error rate must be defined before the process is initiated.
  2. Next, the dataset needs to be validated to confirm it contains all the required data.
  3. Finally, the source data’s value, structure, format etc. is matched with the schema of the destination. The data is checked for redundant, incomplete, inconsistent, or incorrect values as well.

Data validation can be carried out in one of the following ways:

  • Scripting: Scripting languages are used to create the validation process. However, since these scripts are manually written, executed, and verified, this method of data validation is time consuming.
  • Enterprise Tools: Several enterprise tools are now available in the market which validate and even repair data. Though these tools are secure and stable, they cost more and also need extra infrastructure for operating.
  • Open-Source Tools: This option is cost effective since they are cloud-based. However, they still need a level of knowledge to be used effectively.

Why do organizations need data validation?

Data validation helps organizations by eliminating the issues that could be caused due to the decay of data. Even though it is not a complete solution, it assists organizations to check for missing, incomplete, inaccurate, and inconsistent data that could inadvertently affect the final outcomes for which the data is intended. However, the validation process is only helpful at the time of input and not during the processing state since data keeps changing over time. Organizations need to employ data validation processes because:

  • It is easier for engineers to work with validated data. This way, they do not waste time on inaccurate data and are able to work faster with confidence
  • It is a proactive technique to ensure issues are identified and resolved before they find their way to the end users. Being proactive, it also promises accurate and easy-to-understand data to the entire organization
  • Cleaning data and validating after it is already in the system costs more money and time. Not only does it negatively affect the revenue of the company, but it also becomes a disruption to the regular workflow
  • By saving time and money at later stages of analysis, data validation is cost effective if used at the appropriate stage of data collection
  • It removes redundancy in data sets
  • It enhances the quality of data being collected as well as the final outcomes and thus, is pivotal to the success of the business
  • It helps in the creation of standard datasets and clean data sets, which implies efficient processes and workflows
  • A lot of time is saved when valid and accurate marketing lead lists are built and maintained since there are lesser failed attempts
  • It assures valuable and correct insights without which defects could be overlooked and errors would creep in the analytics

Challenges in data validation

Data validation process does come with a few challenges:

Siloed or outdated data

Given the distributed nature of data across an organization, it is a given that some data would be siloed or even old. Validation of such a data set is one of the major challenges organizations face.

Time consuming

Validation is a time-consuming process, especially when handling large datasets or when it is performed manually. A solution to this challenge would be to create a sample of the data and validate it for an acceptable failure rate.

Risks of error

The absence of artificial intelligence based validation tools means most of the validations are carried out manually. This leads to errors and data redundancy.

Lack of data management understanding

There is also a lack of expertise in data management processes which eventually leads to a lot of irrelevant and stale data being stored in the datasets. Without a proper understanding of the business rules, the resources end up struggling with the validation requirements.

Data validation vs. data verification

The terms data validation and data verification might sound similar, but they are two very distinct processes. Data validation processes check for the validity of the data. Using a set of rules, it checks whether the data is within the acceptable values defined for the field or not. The system ensures the inputs stick to the set rules, for instance, the type, uniqueness, format, or consistency of the data. The data validation checks are employed by declaring integrity rules through business rules. This enforcement of rules needs to be done at the start of the process to ensure the system does not accept any invalid data.

On the other hand, data verification is carried out on the current data to ensure its accuracy, consistency, and alignment to the intended purpose. While data validation typically happens when a record is being created or updated, data verification can happen at any time. It is required when data is being backed up. At the end of the backup process, data verification is performed where the copy of data is compared to its original state to ensure they are the same. In a situation where the backed-up data needs to be used, the verification ensures peace of mind by guaranteeing the match between the two sets.

The process of data verification becomes an essential step when data is migrated or merged from an outside data source. As an example, consider when company A’s dataset needs to be merged with company B’s dataset. When the data is being migrated, it is pertinent to verify the incoming data records. In simple words, the fields need to be matched correctly to ensure the correct values are being filled into the intended data fields. Even a tiny mistake of shifting a range of cells up or down could cause irreparable mistakes. This is where the data verification process helps by verifying that the information from the source system matches the information in the destination system. The process of data verification could be done manually or with the help of an automated process. Manually, the user would need to verify the accuracy of data by using samples from both systems, the source and the destination. Alternatively, an automated process can perform a complete verification of the data that is imported by matching all the records and highlighting the errors.

The difference between data validation and data verification is that while validation of data is carried out on the input or the original copy, verification is performed on all copies of data. The verification process is longer compared to the quick validation of inputs. Validation rules are in place to prohibit user errors while verification safeguards the data from issues arising from the system.

Data validation is an essential step in any workflow that involves storing, processing, or analyzing data. By ensuring the integrity of datasets, the quality of results and efficiency of the overall process is significantly enhanced. Different techniques of data validation come with their own pros and cons list and must be thoroughly examined based on requirement before the final selection. The authenticity of the final decision-making process depends heavily on the data’s validity and quality; it is vitally important for companies to invest in appropriate validation processes to ensure the best possible outcome.

data validation diagram