What is quality in data entry process?

A data entry clerk, sometimes called a typist, is a member of staff employed to type data into a database using a keyboard, optical scanner, or data recorder. The keyboards used can often have specialist keys and multiple colours to help in the task and speed up the work. While requisite skills can vary depending on the nature of the data being entered, few specialized skills are usually required aside from touch typing proficiency with adequate speed and accuracy. The ability to focus for lengthy periods is necessary to eliminate or at least reduce errors. When dealing with sensitive or private information such as medical, financial or military records, a person's character and discretion becomes very relevant as well. Beyond these traits no technical knowledge is generally required and these jobs can even be worked from home.

Dear Mr. Priya John,

Quality in data - either those data are required to be entered, processed or documented – is one of the fundamentals of work progress and economic development.

However, the term "Quality in data" or even in "data input" is fully explained and precisely declared in my answers for two previously mentioned questions:

1-                 What does data governance implies for? And how can it improve quality control at work?

2-                 What are the best practices used by the data input clerk to ensure the quality of data?

And to be fair with your inquiry, I will try all my best to refine my answer – as I could – in order to explain this term and its useful application by our organizations development:

Data Quality means:

-               the perception or an assessment of data's fitness to serve its purpose in a given context, or

-               the totality of features and characteristics of data that bears on their ability to satisfy a given purpose;

-               The sum of the degrees of excellence for factors related to data.

The main aspects of data quality to makes data appropriate for a specific use are: - Accuracy- Completeness- Update status- Relevance- Consistency across data sources- Reliability- Appropriate presentation- AccessibilityWe can achieve high levels of data quality when information is valid for the use to which it is applied and when decision makers haveconfidence in and rely upon the data.

In order to increase and maintain data quality, you have to implement these steps organization-wide:-1- Data profiling: It is a statistical analysis and assessment of data availability in an existing data source (e.g. a database or a file) and collecting statistics and information about that data.

n     This can be useful in improving the ability to search the data easily, and whether the data conforms to particular standards or patterns, as well as exploring relationships that exist between value collections both within and across data sets

-2- Data standardization: It is the process of reviewing and documenting the names, meaning, and characteristics of data elements so that all users of the data have a common, shared understanding of it. A business rules engine that ensures that data conforms to quality rules can be used for.

-3- Geocoding - It is a process of assigning locations to addresses to that they can be placed as points on a map, similar to putting pins on a paper map, and analyzed with other spatial data. The process assigns geographic coordinates to the original data, hence the name geocoding. It is also called address-matching, and can be used for name and address data.

-4- Matching or Linking - a way to compare data so that similar, but slightly different records can be aligned. Matching may use "fuzzy logic" to find duplicates in the data. It often can build a 'best of breed' record, taking the best components from multiple data sources and building a single super-record.

-5- Monitoring - keeping track of data quality over time and reporting variations in the quality of data. Software can also auto-correct the variations based on pre-defined business rules.

-6- Batch and Real time - Once the data is initially cleansed (batch), companies often want to build the processes into enterprise applications to keep it clean.

-               The necessity to advance data quality in the database should be deployed weekly to ensure the identity and value of each customer is properly consolidated at any given instance.

-               However, the same process should also be applied to each direct mail campaign deployment to refresh the postal address and ensure the promotion is reaching the consumer at his current and most responsive address.

-               Otherwise, you can find DATA QUALITY SOFTWARE to ensure the process, like Talend Open Studio for Data Quality, Informatica Data Quality and Trillium Software System (or business and IT collaboration).  

= Finally, when we want to create a full system to conduct quality of data in our organizations, the use of data governance policy will confirm this. Data governance refers to high-level, corporate, or enterprise policies and strategies that define the purpose for collecting data, the ownership of data, and the intended use of data, in order get overall management of Data availability, usability, integrity, and security

n     Data (information) Governance is based on ensuring data Quality; data collection process that satisfies a given purpose and reaches a degree of excellence. Processes and technologies are involved to insure the conformance of data values to business requirements and acceptance.

n     In addition, information governance provides parameters based on organizational and compliance policies, processes, decision rights, and responsibilities. 

n     In practice of data quality, data governance is a concern of professionals involved with a wide range of information systems, ranging from data warehousing and business intelligence to customer relationship management and supply chain management.

n     To implement a data governance program, we have to implant:

-1- Assessment in defining the owners or custodians of the data assets in the enterprise 

-2- A policy must be developed that specifies who is accountable for various portions or aspects of the data, including its accuracy, accessibility, consistency, completeness, and updating. 

-3- Processes must be defined concerning how the data is to be stored, archived, backed up, and protected from mishaps, theft, or attack. 

-4- A set of standards and procedures must be developed that defines how the data is to be used by authorized personnel. 

-5- A set of controls and audit procedures must be put into place that ensures ongoing compliance with government regulations.

n     Data Governance role in maintaining data quality requires going through the data periodically and scrubbing it. Typically this involves updating it, standardizing it, and de-duplicating records to create a single view of the data, even if it is stored in multiple disparate systems. 

n     When an effective data entry and collection environment exists, staff will spend less time and money correcting errors and more time on other tasks, such as the instructional program. 

Data governance works on facilitating the culture of data quality, in order to easily meet the policy and regulatory demands of the various agencies that require information. When you have confidence in the data provided, you are more likely to survive an audit, and you will have:--- Clear standards and guidelines for data quality

--- Staff with the needed skills and information to enter data correctly.

--- Workable calendars and timelines to make sure the data are available when needed; and technology support in place to support these efforts.

GOOD LUCK

Quality in data Entry process refers to correct data appearing on the Managment reports which inturn means that the data entry should be accurate.