In the epoch of data analytics, the use of analytics is no longer reserved for massive, well-funded businesses. Data and analytics are now widely used, with 94 % of business and enterprise analytics experts stating that they are essential to their organization’s digital transformation initiatives. There is most definitely no lack of information available. However, there is still significant room for improvement in terms of the data’s quality. The cost of faulty data to American businesses and organizations, according to an IBM estimate from 2016, was more than $3 trillion annually. This cost was attributed to duties like finding and repairing problems as well as validating and verifying data sources. Four years later, a study revealed that poor data quality costs businesses $12.8 million annually. Needless to say, in 2022, this number has massively increased.

This is problematic considering how poor data performance has an adverse effect on multiple company performance areas. Insufficient customer or prospect data, squandered marketing and communications endeavors increased spending, and generally poorer decision-making are some of the consequences. As a corollary, all firms should place a high priority on increasing data quality. When discussing data quality, we often use the terms data verification and data validation synonymously. These two terms, however, are different. The focus of the piece is to compare and contrast the two and explore a few different usage techniques.

Also read: 5 Tips to avoid data breaches in the healthcare industry


Whether you’re gathering information on the ground, evaluating the data, or getting ready to present the data to stakeholders, data validation is a crucial component of every data handling process. One’s results won’t be accurate if their data is inaccurate from the start. For the same reason, data must be verified and validated prior to application. Although it’s an essential stage in every data workflow, data validation is frequently overlooked. Data validation may appear to be a phase that slows down your workflow, but it is pivotal since it will enable you to produce the maximum results. Presently, data validation can be completed considerably more promptly than you could have anticipated. Data validation can now be perceived as an essential ingredient of your workflow rather than as an extra step thanks to the development of data integration platforms that can incorporate and automate validation processes.


To mitigate any project defects, data must be validated for quality, clarity, and specificity. Without data validation, you run the risk of making judgments based on faulty data that is not accurately representative of the situation at hand.  While it’s crucial to confirm data inputs and values, the data model itself also has to be validated. When attempting to use data files in different programs and software, you will encounter problems if the data model is improperly designed or formatted. What you can do with data will depend on the format and content of the data files. Garbage-in and garbage-out situations can be curtailed by using validation procedures to cleanse data before application. Ensuring the accuracy of the data contributes to the credibility of your judgments. 


In a fairly short period of time, analytics has advanced dramatically. It can potentially help with many different aspects of operations and can change the game for many firms. However, in order to achieve the best outcomes, businesses must understand how to make the best use of this technology, enhance the quality of their data, and efficiently manage it. Here are a few techniques for data validation to assist you to guarantee the accuracy of your data.


With this approach, you review your subject areas on an aggregate basis to make sure the data matches the original data source. Although it appears to be a simple validation to carry out, businesses rarely employ it. It guarantees that the appropriate data is available for data validation in excel sheets, VBA sheets, or any other sort of data source. The method is among the finest approaches to guarantee data completeness.


By employing this technique, you can compare analogous information at various stages of the life cycle of your business and have an approximative verification across multiple source systems. It is possible to compare two data sources by merging the data together and examining discrepancies using code, such as SQL data validation.


This technique enables you to keep a record of all of your challenges, including redundancy, inaccurate data, duplication, and incomplete information, in one place with the aid of an automated data tracking tool. This allows you to identify recurring problems, pinpoint subject areas that pose a higher risk, and ensure that the appropriate precautionary measures have been taken.


Data verification is the process of examining various types of data for consistency and accuracy after data migration. It is known as Source Data Verification (SDV) in some fields, such as clinical studies. Data must be verified to assure accuracy before being transferred from a data warehouse for usage in a big data processing system. A large data project could be in jeopardy from everything from misspelling to inaccurate numbers to data loss. When data is imported from one source to another, is comprehensive, and supports procedures in the new system, data verification helps to ascertain whether the information was accurately translated. A parallel run of both systems may be required during verification to spot areas of discrepancy and prevent data loss.


Data verification guarantees the accuracy of the data you store in the system. This has numerous advantages for your company, particularly in terms of sales. For building and maintaining an accurate list of leads and prospecting for sales, your sales force counts on dependable data. Your sales charges cannot be accurate if you continually phone a disconnected line or send an email to an invalid address. Every second used to update files and call invalid numbers is time that could be used to make sales. Bad data can cost your company time and lead to missed opportunities. 


Validation processes can verify that the data is reasonable, logical, and acceptable. Having as much precise data as you can in your database is certainly preferable. Here are a few techniques for data verification to assist you to guarantee the accuracy of your data.


The term “double-entry” implies entering the data twice and comparing the results. A prime example that demonstrated this is making a new password. Most often you are prompted to enter the password twice when creating a password. This enables the computer to confirm that the data entry was accurate and error-free in both cases. By matching them, the first and second entries are checked against one another. Despite the fact that this may be helpful in locating several errors, it is impractical for vast amounts of data.


Reading written work and identifying any errors is known as proofreading.  This process calls for a thorough inspection of the data entry to make sure there are no errors and that everything is accurate.

Data continues to grow in importance for businesses as data management methods and technology advance. It is being used by an increasing number of businesses to make decisions related to marketing, product development, finance, and other areas. Leveraging data is increasingly becoming a question of staying competitive as more businesses benefit from it and businesses that ignore data and related technology run the danger of falling behind. The prompt and proactive detection and remediation of possible problems are the most important elements of efficient data quality management. Those who can do this will be far ahead of the competition and well-positioned to prosper in 2022 and beyond.


Making accurate, well-informed decisions requires access to high-quality data. While all data has some degree of “quality,” the level of data quality is determined by a number of qualities and factors.  A single erroneous data point can create mayhem throughout the entire system since data accuracy is a crucial component of high-quality data. Executives cannot trust the data or make informed judgments without accuracy and dependability in data quality. This could lead to an increase in operating costs and disruption for users further down the supply chain.

We at Intone are committed to providing you with the best data integration service possible, tailored to your needs and preferences. We offer you:

  • Knowledge graph for all data integrations 
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  • Data encryption at every stage
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  • Real-time, streaming & batch processing of data
  • Supports unlimited heterogeneous data source combinations
  • Eye-catching monitoring module that gives real-time updates

Know more about RPA in banking and RPA in healthcare.

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