What Are 3 Steps of Data Verification?
Data verification refers to auditing the accuracy, consistency and purpose of the information. It can be needed any time.
As digitalization is all around, you may require data verification services to check the quality of the process. It differs from data validation, which typically takes place at the very stage when data bases are created or updated.
It is critically important to verify information, especially during its migration or merger from resources that are from outside. Let’s say, a company is shifting to the cloud. In this case, the pan detail of its workflow, employees, customers and operations would be migrated. This process may bring in inconsistencies, which eventually impacts analytics-driven decisions. Here, verification emerges in a pivotal role.
In short, auditing of records is essential if you want accurate analysis, which leads to incredible strategies.
It is done in three steps, which are given below:
· Assess Quantity & Data Type
Assessment means measuring records, as how many records are there in a sheet and which type of information they carry.
In this stage, you need to evaluate how many records provided to check on their quality. Certainly, the customer provides it in a desirable file format, which can be a PDF, CSV, EXL or DOC file. At first, migrate it to your server so that it can be forwarded to a quality analyst.
Subsequently, the conversion happens, which means that the records will be converted into a digital copy so that one can read and understand it. Then only, mistakes and errors can be figured out.
If you have PDFs or images, the OCR technology would be really helpful. The PDF or any file would be converted into a text file or any uniform format, which quickly ensures easy assessment. It is simply because the entire information would be in the same format. The data entry experts transform it in that way, which encourages seamless removal of discrepancies, redundancies or errors.
Even if there are some critical issues, they are determined and resolved beforehand.
· Revise What Is In Database
A thorough review of the converted data is necessary if you suppose to extract something really valuable and advantageous for your business. It ensures accuracy, which signifies quality.
You can set various stages to figure out the most accurate records. Generally, this process involves defining standards, setting priorities clearly, collecting information, analyzing it and interpreting the outcome.
Upon getting deep with all these steps, you would find a number of weak spots, such as duplicity, odd entries and errors.
However, many challenges would interfere at every step. A well-defined strategy and previous hands-on experience can help in coming across them. In addition, a team of upskilled quality experts can also minimise revision time and redundancies without wasting thousands of dollars on redoing.
· Get Deep With Revised Records
Analysis is the next step, which involves getting deep with the cleansed records. It drives you to a thousand of strategies that can actually prove a breakthrough for your company.
But, it’s not like a piece of cake. You have to keep up the security of its accessibility. A little bit of ignorance or a small instance of hacking can nullify what efforts you have put in.
After that, getting deep with the infallible data resources seems easy. You can remotely access it to discover leads, or a ton of marketing schemes to better sales and hence, improve customer experience. Customer journey may achieve a breakthrough if you have consistent and error free contextual data for in-depth analysis.
This is how you can define targets, actions, strategies and many ways to quickly come on the top of the leading companies in your niche.
In the nutshell, it’s clear that verification is vital. Besides, it’s an endless process, which ensures the accuracy and consistency of information that you have over time. If you skip verifying, the database would keep on containing tons of errors, decayed data and misleading information. Needless to say, these inconsistencies won’t allow you to scale up and your strategies to prove winning at the end. The unverified datasets can only lead to bad decisions, which may prove inflexible and unreal at the end.
