Before we talk about the types of data validation techniques, it is important to understand what data validation is. It is the process of ensuring data accuracy and quality before using, importing or processing them. It is important because the inaccuracy of data can lead to producing inaccurate results, thus, stressing the need for validation and verification of data.
Data validation is a critical step in any data workflow, but it is often skipped over as it usually seems to slow the pace of work and is given the least priority because loss is not seen sooner. However, it is important to realize that this process helps produce the best results possible and avoids system breaks in the future because of data quality issues that lead to high maintenance costs. And a good data integration platform that incorporates and automates the process of validation, enables the inclusion of data validation as a core part of the workflow. In this article, we will discuss the various data validation techniques.
Why Do You Need Data Validation?
There are many reasons why you might need to validate data. Perhaps you’re working with a legacy system that doesn’t have extensive validation checks in place, and you need to make sure the data is clean before you can work with it. Alternatively, you may be building a new system and want to make sure that the data entering your database is of the highest quality. You might also be seeking to include continuous control monitoring and robotic process automation as integral systems to your business operations as well, all of which require data validation.
Data validation is also important for security reasons. If you don’t validate user input, someone can enter malicious data that could compromise your system. By validating input, you can help protect your system from attack. Finally, data validation can help improve the usability of your application. If the data is clean and well-formatted, it will be easier for users to input and work with. This can lead to a better user experience overall.
You might also like to read about Data Integration Solutions: Benefits And Key Features.
Key Data Validation Techniques to Improve Your Data Quality
Here are a few data validation techniques that are key to improving the quality of your data
Source System Loopback Verification
The task of performing aggregate-based verifications of subject areas and ensuring that they match the source of origin is key here. Despite how easy this technique seems, enterprises don’t often use it. This can be one of the best techniques to ensure the completeness of data.
Learning more about Data Verification and Data Validation Techniques can prove incredibly useful too.
Ongoing Source-To-Source Verification
An effective way to detect problems before they mature is by having approximate verification across multiple source systems. This can also be done by comparing similar information at several different stages of a business life cycle. This can prove to be a great way to catch non-data warehouse issues and protect the integrity of the information and the data of the warehouse.
Tracking all issues centrally in one place can help identify recurring issues, reveal riskier subjects, and ensure the application of proper preventive measures. This makes it easy for businesses to input and report issues to be tracked effectively.
Data certification includes performing data validation up-front before adding it to data warehouses. This is an important technique that also includes the use of data profiling tools. The process can add some delay when integrating new data sources into warehouses, but the long-term benefits of this technique hugely enhance the value of a data warehouse.
Learning about How The Benefits Of a Data Warehouse Give Businesses A Competitive Edge may prove very useful for you.
Ensuring the maintenance of statistics for the full life cycle of data gives access to meaningful information that can create alarms for unexpected results. Thus, it becomes important for businesses to ensure that they set alarms based on constant updates in trends. This also helps detect difficult-to-catch situations on an automated basis.
Giving proper thought to data quality while designing data integration flows and overall workflows allow for catching issues quickly and efficiently. And performing checks along the way also provides more advanced options for making quick resolutions.
Why Choose IntoneSwift?
A study published in the Harvard Business Review found that data quality is far worse than most companies realize. They reported that a mere 3% of the data quality scores in the study were rated as “acceptable.”
The IDC also reported how poor data quality costs businesses anywhere between $9.7-$14.2 million each year. Their usage of such data leads to a significant reduction in productivity and mistrust between customers and brands.
The growing demand for data and the path that modern business is set on calls for the need for businesses to adapt to this growing trend well. Quality data is a big part of it. IntoneSwfit’s data management services are a top-of-the-line data integration solution that can is tried and tested by industry experts and leaders alike. We offer,
- Generates knowledge graph for all data integrations done
- 600+ Data, Application, and device connectors
- A graphical no-code low-code platform.
- Distributed In-memory operations that give 10X speed in data operations
- Attribute level lineage capturing at every data integration map
- Data encryption at every stage
- Centralized password and connection management
- Real-time, streaming & batch processing of data
- Supports unlimited heterogeneous data source combinations
- Eye-catching monitoring module that gives real-time updates