Thriving companies worldwide depend on trustworthy data to keep their growth constant. To rely on data, you must first be sure of how trustworthy the data is. Data entry cleaning, validation, and transformation are some of the most crucial aspects of using data in an organization. 

Data entry cleaning refers to removing any unnecessary aspect that does not belong in your dataset. Data validation checks the reliability and cleanliness of your data. Likewise, data transformation means organizing data to make it more convenient to understand and use by both humans and computers. 

What are data entry cleaning, validation, and transformation?

Data cleaning is a crucial step in making the data efficient and workable. Raw data is never free from errors and inefficiencies. It is rarely easy to spot these errors unless you conduct a proper data entry cleaning. 

This step is important before there is no way to avoid mistakes and inconsistencies while deriving data from multiple sources. Avoiding mistakes is not possible in data entry processes. Hence, data entry cleaning is the foremost step to ensure the data is ready for decision-making. 

Data validation, on the other hand, comes second to data cleaning. Once the data are cleaned, data validation helps ensure that the data is also accurate and reliable for further steps in data-driven decision-making. The data input is checked several times until the consistency and accuracy of the data is guaranteed. 

Data transformation helps in converting the validated data for organization and management. Pattern detection and improving business strategies become simpler when data is transformed. This process removes improper indexing, null values, unexpected duplicates, and incompatible formats from the data set. 

How do data entry cleaning, validation, and transformation work?

Proper data entry cleaning, validation, and transformation are critical in creating organizational data value. Almost every organization in Nepal works with data and must ensure that the data they are getting are not misleading.

Knowing how data entry cleaning, validation, and transformation work is just as vital for anyone owning or working in an organization. The following steps will guide you through how these three processes work:

Duplicate data removal: Removing duplicate data helps ensure no error in the data entry, as double data entry can result in faulty conclusions during decision-making. 

Filtering unwanted errors and outliers: Structural errors are common, and data entry cleaning helps reduce such errors. Removing irrelevant and useless outliers is also done during data entry cleaning. 

Finding missing data: Data entry cleaning helps find missing values in a dataset. 

Quality assurance: Maintaining data quality is done within data entry cleaning and validation steps. 

Detail plan: Data validation helps create a roadmap for what steps must be taken to proceed. Hence, it serves as a detailed plan. 

Database validation and formatting:  The size of the data, the number of records, and an assessment of the inputs and objectives based on the data categories are all be determined and accomplished during data validation. 

Sampling: Sampling helps ensure that the larger set of data works as per the goal of an organization by first sampling and testing a smaller set of data. 

Generation of code: Transformation tools help in code generation used in the transformation process. 

Execution: With the help of the code, data is changed into a desired format. 

What KPIs are measured in data entry cleaning, validation, and transformation?

Key performance indicators or KPIs refer to measuring performance through a given period to reach an objective. KPIs help make advisable decisions while also considering the objectives one should accomplish. KPIs are critical elements of knowing how accurate and complete your data is. 

The following KPIs are measured in data entry cleaning, validation, and transformation:

Customer Experience KPIs: This KPI helps measure customer satisfaction when they purchase a product from the company with the help of metrics like Customer Effort Score (CES) and Net Promoter Score (NPS).

Employee Experience KPIs: Valuable metrics like Employee Net Promoter Score and Employee Productivity help measure employee satisfaction in the company. 

Process, Technology, And Data KPIs: New Business Process Adoption, Cloud Adoption, and Data Quality are used as process, technology, and data KPIs during data transformation. 

Why Nepal for data entry cleaning, validation, and transformation?

Anyone willing to work remotely can do data entry cleaning, validation, and transformation, given they know the measures and tools used during these processes. There has been a growth in the number of young people interested in computers and information technology in the past few decades. This has also increased their desire to work remotely while providing the best possible datasets for organizations. 

Fresh graduates and college students are looking for computer-related jobs, making it easier to find workers in Nepal. Quality work is guaranteed when fresh minds want to enhance their careers and grow as professionals. 

Another major attraction for clients is that the labor cost in Nepal is less costly than that of other parts of the world. For companies looking to contact their clients in real-time, the time zone of Nepal works perfectly in favor of both the companies and the clients. Outsourcing work, as it is, is quite cheap, and finding workers in Nepal through outsourcing is just as simple.  

Getting a trustworthy, clean, validated, and organized set of data is not as close to being hard in Nepal as the workers are dedicated to providing the clients with only the most effective and useful datasets. 

Why Kantipur Management for data entry cleaning, validation, and transformation?

Kantipur Management provides various services related to maintaining and securing large databases with a history of working with different organizations throughout different industries in Nepal. Data entry cleaning, validation, and transformation are included in the major operations of Kathmandu Management. 

Kathmandu Management helps outsource and find the best worker to ensure your data entry is cleaned, validated, and transformed. With this expertise, your company will be guaranteed to work with efficient data with low to zero errors. 

Moreover, Kantipur Management is your perfect outsourcing associate to develop reliable strategies that best work to create a ground for database management. Making informed and data-driven decisions is likely when you partner with Kantipur Management.