The integrity of data becomes increasingly more important as more B2B firms use data-driven techniques to enhance revenue and improve operational efficiencies. The inability to trust business data gathered from a variety of sources can sabotage an organization’s efforts to fulfill critical business objectives. The sheer volume of data can be overwhelming for businesses. Data standards, heterogeneous data systems, a lack of data governance, manual processes, and so on are all issues they encounter.
Businesses acquire data about their customers through internal processes as well as external interactions, including demographic, technographic, firmographic, and financial information. However, the information gathered is frequently unprocessed and rife with mistakes, making it difficult to draw conclusions. As a result of this inability to trust data, data validation is required. Data validation allows businesses to have more confidence in their data.
In this article, you will gain information about Data Validation. You will also gain a holistic understanding of the importance of Data Validation, the types and methods of Data Validation, the steps to perform Data Validation, and its benefits and limitations. Read along to find out in-depth information about Data Validation.
Table of Contents
What is Data Validation?
Data Validation is the process of ensuring that source data is accurate and of high quality before using, importing, or otherwise processing it. Depending on the destination constraints or objectives, different types of validation can be performed. Validation is a type of data cleansing.
When migrating and merging data, it is critical to ensure that data from various sources and repositories conforms to business rules and does not become corrupted due to inconsistencies in type or context. The goal is to generate data that is consistent, accurate, and complete in order to avoid data loss and errors during the move.
Why Data Validation is Important?
Image Source
Validating the accuracy, clarity, and specificity of data is essential for mitigating any project flaws. Without validating data, you risk making decisions based on imperfect data that is not accurately representative of the situation at hand. Structures and content in datasets determine the results of the process, and validation techniques cleanse and eliminate unnecessary files from it, as well as provide an appropriate structure to the dataset for the best results.
Data Validation is used in Data Warehousing as well as the ETL (Extraction, Translation, and Load) process. It makes it easier for an analyst to gain insight into the scope of data conflicts. While it is critical to validate data inputs and values, it is also necessary to validate the data model itself. If the data model is not properly structured or built, you will encounter problems when attempting to use data files in various applications and software.
Data Validation can also be performed on any data, including data in a single application, such as MS Excel, or simple data mixed together in a single data store.
Hevo Data, a Fully-managed Data Pipeline platform, can help you automate, simplify & enrich your data replication process in a few clicks. With Hevo’s wide variety of connectors and blazing-fast Data Pipelines, you can extract & load data from 100+ Data Sources straight into your Data Warehouse or any Databases. To further streamline and prepare your data for analysis, you can process and enrich raw granular data using Hevo’s robust & built-in Transformation Layer without writing a single line of code!
Get Started with Hevo for Free
Hevo is the fastest, easiest, and most reliable data replication platform that will save your engineering bandwidth and time multifold. Try our 14-day full access free trial today to experience an entirely automated hassle-free Data Replication!
What are the Types of Data Validation?
Every organization will have its own set of rules for storing and maintaining data. Setting basic data validation rules will assist your company in maintaining organized standards that will make working with data more efficient. Most Data Validation procedures will run one or more of these checks to ensure that the data is correct before it is stored in the database.
The following are the common Data Validation Types:
1) Data Type Check
A Data Type check ensures that data entered into a field is of the correct data type. A field, for example, may only accept numeric data. The system should then reject any data containing other characters, such as letters or special symbols, and an error message should be displayed.
2) Code Check
A Code Check ensures that a field is chosen from a valid list of values or that certain formatting rules are followed. For example, it is easier to verify the validity of a postal code by comparing it to a list of valid codes. Other items, such as country codes and NAICS industry codes, can be approached in the same way.
3) Range Check
A Range Check will determine whether the input data falls within a given range. Latitude and longitude, for example, are frequently used in geographic data. Latitude should be between -90 and 90, and longitude should be between -180 and 180. Any values outside of this range are considered invalid.
4) Format Check
Many data types have a predefined format. A Format Check will ensure that the data is in the correct format. Date fields, for example, are stored in a fixed format such as “YYYY-MM-DD” or “DD-MM-YYYY.” If the date is entered in any other format, it will be rejected. A National Insurance number looks like this: LL 99 99 99 L, where L can be any letter and 9 can be any number.
5) Consistency Check
A Consistency Check is a type of logical check that ensures data is entered in a logically consistent manner. Checking if the delivery date for a parcel is after the shipping date is one example.
6) Uniqueness Check
Some data, such as IDs or e-mail addresses, are inherently unique. These fields in a database should most likely have unique entries. A Uniqueness Check ensures that an item is not entered into a database more than once.
7) Presence Check
A Presence Check ensures that all mandatory fields are not left blank. If someone tries to leave the field blank, an error message will be displayed, and they will be unable to proceed to the next step or save any other data that they have entered. A key field, for example, cannot be left blank in most databases.
8) Length Check
A Length Check ensures that the appropriate number of characters are entered into the field. It verifies that the entered character string is neither too short nor too long. Consider a password that must be at least 8 characters long. The Length Check ensures that the field is filled with exactly 8 characters.
9) Look Up
Look Up assists in reducing errors in a field with a limited set of values. It consults a table to find acceptable values. The fact that there are only 7 possible days in a week, for example, ensures that the list of possible values is limited.
Providing a high-quality ETL solution can be a difficult task if you have a large volume of data. Hevo’s automated, No-code platform empowers you with everything you need to have for a smooth data replication experience.
Check out what makes Hevo amazing:
- Fully Managed: Hevo requires no management and maintenance as it is a fully automated platform.
- Data Transformation: Hevo provides a simple interface to perfect, modify, and enrich the data you want to transfer.
- Faster Insight Generation: Hevo offers near real-time data replication so you have access to real-time insight generation and faster decision-making.
- Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
- Scalable Infrastructure: Hevo has in-built integrations for 100+ sources (with 40+ free sources) that can help you scale your data infrastructure as required.
- Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
Sign up here for a 14-Day Free Trial!
What are the Methods to Perform Data Validation?
There are various methods for Data Validation available, and each method includes specific features for the best Data Validation process.
These methods to perform Data Validation are as follows:
1) Validation by Scripts
In this method, the validation process is carried out using a scripting language such as Python, which is used to write the entire script for the validation process. To ensure that all necessary information is within the required quality parameters, you can compare data values and structure to your defined rules. This method of Data Validation can be time-consuming depending on the complexity and size of the data set you are validating.
2) Validation by Programs
Many software programs are available to help you validate data. Because these programs have been developed to understand your rules and the file structures you are working with, this method of validation is very simple. The ideal tool will allow you to incorporate validation into every step of your workflow without requiring a deep understanding of the underlying format.
The different programs that can be used are:
A) Open Source Tools
Because open-source options are cost-effective, developers can save money if they are cloud-based. However, in order to complete the process effectively, this method necessitates extensive knowledge and hand-coding. OpenRefine and SourceForge are two excellent examples of open-source tools.
B) Enterprise Tools
For the Data Validation process, various enterprise tools are available. Enterprise tools are secure and stable, but they require infrastructure and are more expensive than open-source tools. For instance, the FME tool area is used to repair and validate data.
What are the Steps to perform Data Validation?
The steps carried out to perform Data Validation are as follows:
Step 1: Determine Data Sample
If you have a large amount of data to validate, you will need a sample rather than the entire dataset. To ensure the project’s success, you must first understand and decide on the volume of the data sample, as well as the error rate.
Step 2: Database Validation
You must ensure that all requirements are met with the existing database during the Database Validation process. To compare source and target data fields, unique IDs and the number of records must be determined.
Step 3: Data Format Validation
Determine the overall data capability and the variation that requires source data for the targeted validation, and then search for inconsistencies, duplicate data, incorrect formats, and null field values.
What are the Benefits of Data Validation?
Some of the benefits of Data Validation are as follows:
- It is cost-effective because it saves the appropriate amount of time and money through dataset collection.
- Because it removes duplication from the entire dataset, it is simple to use and is compatible with other processes.
- With improved information collection, data validation can directly help to improve the business.
- It is made up of a data-efficient structure that provides a standard database and cleaned dataset information.
What are the Limitations of Data Validation?
Some of the limitations of Data Validation are as follows:
- Because of the organization’s multiple databases, there may be some disruption. As a result, data may be out of date, which can cause issues when validating the data.
- When you have a large database, the process of data validation can be time-consuming because you have to perform the validation manually.
Conclusion
In this article, you have learned about Data Validation. This article also provided information on Data Validation, the types and methods of Data Validation, the steps to perform Data Validation, and its benefits and limitations.
Hevo Data, a No-code Data Pipeline provides you with a consistent and reliable solution to manage data transfer between a variety of sources and a wide variety of Desired Destinations with a few clicks.
Visit our Website to Explore Hevo
Hevo Data with its strong integration with 100+ Data Sources (including 40+ Free Sources) allows you to not only export data from your desired data sources & load it to the destination of your choice but also transform & enrich your data to make it analysis-ready. Hevo also allows integrating data from non-native sources using Hevo’s in-built REST API & Webhooks Connector. You can then focus on your key business needs and perform insightful analysis.
Want to give Hevo a try? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You may also have a look at the amazing price, which will assist you in selecting the best plan for your requirements.
Share your experience of understanding Data Validation in the comment section below! We would love to hear your thoughts.