Many businesses seek the transformation of raw data into more actionable insights to further ensure functionality in their processes. However, they fail to comprehend the A to Z of the process behind achieving this. This lack of understanding can be a significant barrier to securing successful outcomes from data verification services.
To address such a knowledge gap and equip businesses to better their data management process, we have explained each step that a professional service provider follows for ensuring data verification and validation.
We believe that by gaining a better understanding of the data verification process, businesses can make informed decisions about outsourcing data verification services and develop a more effective data-driven strategy.
Data Verification Process: Why is It Important?
Verification procedures are typically carried out to ensure that the data entered into the database is rational, logical, and acceptable.
During the verification process, the data entered into the system is compared to the data from the source document. This comparison helps to identify any errors, inconsistencies, or missing information. Any discrepancies are flagged, and the necessary corrective action is taken to ensure the accuracy and completeness of the data.
Data Verification Process: A Step-by-Step Guide
Here are the steps data services providers follow to verify and validate your data.
Step 1: Remove Irrelevant Data
When you have a large dataset, it’s common to have instances of irrelevant data for the specific project you’re working on. For example, if you’re analyzing customer data for a specific product, you may have data related to other products or services offered by your company that are not relevant to your analysis.
To identify and remove such irrelevant data, you should provide a clear understanding of the scope and objective of your analysis or project to the company. Then, they identify the specific variables or attributes important for your analysis to filter out any data that does not meet those criteria.
Removing irrelevant data can help you focus on the relevant information and reduce the size of your dataset, making it easier to analyze and draw meaningful insights from the remaining data.
Step 2: Deduplicate Data
Duplicate data can create more problems than one can imagine. Let’s say you are performing an analysis, and some entries are made twice. This can change the output of the analysis to be inaccurate.
Duplicates also increase the size of the dataset, which can slow down processing time and waste resources. Companies can check duplicates to compare each record and look for identical records. For example, if you have a customer database with multiple entries for the same customer, they will compare those records and remove duplicate entries.
By removing duplicates, the data verification service providers ensure that each record in your dataset is unique to avoid double-counting or inflating numbers in your analysis. It can also help improve the accuracy and reliability of your final analysis by providing a clean and consistent dataset.
Step 3: Deal with Missing Data
This is a common problem in datasets, and it can occur for various reasons, such as incomplete data or data entry errors. However, missing data can lead to biased or incomplete analysis if not handled properly. Therefore, dealing with missing data is important before analyzing the dataset.
Two common ways in which companies deal with missing data are to remove incomplete records or input the missing values. This entirely depends on what you demand from them.
Data is removed in cases where the missing data forms a small proportion of the total dataset. However, if the missing data is a large proportion, removal can result in a significant loss of information and create a biased analysis.
Inputting the missing values is a more common approach, and it involves estimating the missing data based on the available data. Companies use different methods for inputting missing data, such as using mean or median values, regression models, or machine learning algorithms.
By dealing with missing data, the company ensures that your dataset is complete and unbiased and helps you avoid any errors that could arise from incomplete data.
Step 4: Delete Out-dated Data
Removing outdated data can be a challenge for businesses, as they tend to accumulate unnecessary records in their CRM systems over time in the hope of sales. This can create problems in the long run, as it can increase the size of the dataset and make it difficult to perform accurate analysis.
For instance, inactive email IDs from the past 10 years would be of no use to you, if you plan to start a new campaign. Therefore, during the data verification process, service providers remove outdated data that is no longer relevant to current projects.
However, if the outdated data is still required for any future reference, companies can create a separate file or folder to maintain these records. This allows them to retain the original data without cluttering the main dataset and ensure that the dataset used for analysis is up-to-date and relevant.
Step 5: Check the Data Format
In this step, the company ensures that your data is in the correct format or structure required for further processing or analysis. It is essential to check this because data in the wrong format can result in incorrect analysis or even system failures.
For example, if you have a dataset of customer information, you might need to ensure that the phone numbers are in a specific format, such as (123) 456-7890, rather than in a different format, like 123-456-7890.
Here are some common examples of data formats that need to be checked:
- Dates need to be in a specific format, such as yyyy-mm-dd or mm/dd/yyyy.
- Numbers may need to be in a specific format, such as with or without decimal places, in scientific notation, or with leading zeros.
- Text data may need to be checked for the presence of special characters, leading or trailing spaces, or proper capitalization.
- Currency data may need to be in a specific format with proper symbols and decimal places.
- Phone numbers may need to be in a specific format with proper area codes, country codes, and hyphens.
- Email addresses may need to be in a specific format with proper syntax and domain names.
Step 6: Validate Your Data
The final steps include validating your data by checking it to ensure that it is accurate, complete, and consistent. It is an essential part of the data verification process, which aims to identify and correct errors, inconsistencies, and other issues that may compromise the quality and usefulness of your data.
Data validation typically involves several steps, such as –
Data entry verification: This involves checking that the data has been accurately entered into the system. It includes checking for spelling errors, typos, and other common mistakes that may have occurred during data entry.
Validity check: This involves checking that the data conforms to predefined standards, such as date formats or numerical ranges.
Cross-validation: This involves comparing the data to other sources of information to ensure accuracy and completeness.
Service providers ensure that data is reliable and useful for analysis, reporting, and decision-making by validating your dataset. This can help you avoid costly mistakes and improve the overall quality of your data.
Overall, a data verification service provider will help you keep your data clean, up-to-date, and organized for you to perform any kind of analysis accurately. Moreover, by outsourcing data verification services, you can potentially increase your team’s productivity by allowing them to focus on other core business activities.