Data Verification: Essential Ways to make it a Precise Process

1. Set the objective for data verification
2. Schema review
3. Use data sampling
4. Data profiling
5. Review access controls and data security aspects
6. Cleansing and standardization of data
7. Performance testing
8. Backup and recovery testing

Data Verification for your Business
Benefits of Data Verification

1. Increases Profit
2. Satisfied Customers
3. Reduces TCO

Conclusion

What do you Mean by Data Verification?

Data verification is the process of checking raw data that is sourced for accuracy. It can be fully verified or you can just get a small sample verified through email or telephone. Moreover, it is a time-consuming and tedious process. Another way of verifying the data collected is by comparing data in one system to the migrated data in the other. Automating the data verification process is another solution.

How is data verified?

Data is collected from various sources hence we are not sure as to how accurate it is or its validity and reliability. Data is recovered from public sources such as newspapers, government reports, business directories, magazines, conferences, seminars, feedback forms, etc. Therefore, it is important to verify it through email, telephone, etc. before adding it to your final database. It is verified to confirm the accuracy of the information. The Data verification process focuses on the authenticity, where is it stored, when it was published, and from which location it is uploaded.

Data Verification Steps 

Step 1: Set the objective for data verification 

The entire database cannot be verified without any objective. Understand the purpose of the data and its intended use in the future. Accordingly, set the objectives for your data verification process.

Step 2: Schema review 

Gather all the information regarding the database you need to verify. Review the schema of the database and understand the nature and structure of the database.

Step 3: Use Data Sampling

Data sampling is taking the subset of the data as a representative portion of the entire database you want to verify. Analyze the representative or sample portion of it and draw conclusions based on the characteristics of the sample taken.
Some of the common sampling methods are Random sampling, stratified sampling, systematic sampling, and cluster sampling.

Step 4: Data Profiling

Data profiling is another important aspect of the data verification process. It summarizes the structure and content of the dataset and provides valuable insights about the quality of the dataset. Datasets provide an overview of data types, value ranges, inconsistencies, inaccuracies, and missing data.

Some of the common data profiling aspects are data type analysis, data structure discovery, data content discovery, data distribution, pattern recognition, and data relationship recognition.

Step 5: Review access controls and data security aspects

It is crucial for businesses to use permission-based data that has explicit consent. Data without proper opt-in, opt-out, and access controls can lead to misuse and data breaches. Classify the data based on their sensitivity level and check authentication mechanisms. If needed, redo and reassign the authorization as per the role-based access needs.

Data collection and processing is an ongoing process and even when the data collection is at rest, it has to be secured. The other essential step in data verification is to ensure that the data is secured and encrypted in both rest and transit processes.

Step 6: Cleansing and standardization of data

Data cleansing or data scrubbing is the process of removing inaccuracies, errors, and redundancies. Some of the aspects of data cleansing that are used in the data verification process include error detection, error correction, normalization of data, data enrichment, and deduplication.

Data standardization refers to formulating a certain set of guidelines that ensures the structure, formatting, and presentation of data. Data standardization helps in easy validation and cross-referencing. It also simplifies the process of data comparison and data automation.

Step 7:Performance testing
Performance testing is another major step of data verification. After following all the above steps to verify data, use this performance testing step to see the effectiveness of your data verification process.

Leverage various performance testing techniques including testing data based on real-world scenarios, analyzing results, creating dummy performance test plans to measure the effectiveness of data, measuring the scalability of the data verification process, etc.

Step 8:Backup and recovery testing
Another essential step of the data verification process is to check if the data is safe enough to prevent data breaches and cyber-attacks. Mimic the real-world scenarios to check if the data can be recovered and stored. Analyze the effectiveness of incident response plans and ensure that the data verification process is dynamic.

Backing up data and recovery is an ongoing process that has to be integrated into the data verification and disaster recovery planning process that helps marketers build a top-quality database.

Data Verification for your Business

Data verification ensures that the information you store is accurate as it can benefit your business, especially in terms of sales and conversions. Businesses rely on their sales teams who in turn rely on datasets to create and maintain an accurate list of sales leads and to promote their products and services to existing and potential customers.

If you have accurate contact data, you can directly communicate with your target audience. This will save your time and resources. Moreover, you will not miss out on important business possibilities.

Business contact information should always go through proper data verification stages to ensure that the details in the B2B email lists are correct. Having access to verified data is essential for businesses at every stage, not just when reaching out to your business prospects. Therefore, it is important to use reliable and trustworthy data.

When you hire employees without verifying their documents, it is likely that the hired person is unfit for the job role. And this increases inefficiency and will directly impact the growth of the organization. Therefore, if you have verified contact details you will avoid such mistakes. Data needs to be managed and monitored to avoid loss of data and data theft. Businesses must have complete data backup to use in the future.

Benefits of Data Verification

#1 Increases Profit: 

Data verification is a time-consuming process and you will have to spend a lot of money. Therefore, it is best for businesses and marketers to invest in a reputed database provider like Email Data Group. Therefore, when you get a verified database for your marketing beforehand, you will be able to hire the right employees at the right time and your company will be able to increase its profits significantly.

#2 Satisfied Customers::

If you want your customers to be happy, ensure you provide verified email lists from reputed organizations. Data-driven marketing campaigns guarantee maximum response and also increase business ROI. With verified data, you can connect with responsive customers and also hire employees who are efficient, productive, and trustworthy.

#3 Reduces TCO :

Pre-packaged and customized email list are thoroughly verified before it reaches customers. You can collaborate with marketing data providers like Email Data Group and save your time and money. Moreover, you can stay assured of the credibility of the database and also reduces the total cost of ownership.

Conclusion:

Having access to verified data allows you to directly connect with your customers and build business relations. You will even be able to promote your products through various communication channels and increase sales and marketing revenue.

Using verified data enables you to achieve your business goals faster. So, if you still have not thought about it then it’s about time you invest in verified data to grow your business

Additional Read : Data Verification Best Practices