Data Processing

A working definition of data processing usually includes all operations performed on data disclosure, management, use, and collection of data are four examples of business data processing within a company. The strategic goal of data processing is to convert raw data into meaningful information that improves a current situation or resolves an existing problem. Data processing outputs often take various forms such as reports, diagrams, and graphics that make the data easier to understand and analyze.

The three types of data processing manual, mechanical and electronic — are summarized below.

• Manual Data Processing   This is the “old-fashioned” way of doing it before the invention of calculators. When data processing is done manually (“by hand”), the process is slow and all mistakes are due to “human error.”
• Mechanical Data Processing   Data processing results improved dramatically with the addition of typewriters and calculators. However, speed and errors were still very much a function of “human operators.”
• Electronic Data Processing  Modern data processing uses computers to facilitate processing requirements. Based on data processing instructions provided by human users and programmers, computers now handle an expanding part of the data processing operation. While processing data electronically has become the accepted norm, the underlying technology continues to evolve. For example, replacing “paper forms” is still a work in progress for many organizations.

The data processing cycle is a sequential one that starts with inputs and often ends with the interpretation of results however, many organizations add two stages for feedback and storage:

1. Input  The first part of the data processing cycle involves collecting data as well as entering it and then preparing it for the next part of the cycle.
2. Processing  During the second part of the cycle, data is manipulated according to instructions and parameters programmed into the processing application.
3. Output  The form of outputs includes common variations such as results that are printed or displayed on a computer monitor.
4. Interpretation  Assessing and analyzing results: What does the data mean?
5. Feedback  Comparing output with desired results: How can data be processed better?
6. Storage Archiving the data (either physically or electronically) for future use.

In a complete data processing operation, you should pay attention to what is happening in five distinct business data processing steps:

• Editing   What data do you really need? Extracting and editing relevant data is the critical first step on your way to useful results.
• Coding  This step is also known as bucketing or netting and aligns the data in a systematic arrangement that can be understood by computer systems.
• Data Entry  Entering the data into software is a step that can be performed efficiently by data entry professionals.
• Validation   After a “cleansing” phase, validating the data involves checking (and preferably double-checking) for desired quality levels.
• Tabulation   Arranging data in a form that facilitates further use and analysis.