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Discussion

4.1.1 Data Processing

After collecting data, raw data is converted into meaningful
statements through the data processing, data analysis and data
interpretation and presentation. Data reduction or processing
mainly involves various manipulations necessary for preparing
the data for analysis. The process (of manipulation) could be ◆ Data reduction
manual or electronic. It involves editing, categorising the open-
ended questions, coding, computerisation and preparation of
tables and diagrams. Data processing is concerned with editing,
coding, classifying, tabulating and diagramming research data.
The essence of data processing in research is data reduction which
is explained in detail in this unit.
Data processing implies editing, coding, classification and
tabulation of collected data so that they are amenable to analysis.
Data processing occurs when data is collected and translated into
usable information. Usually performed by data scientists, it is ◆ Data into
important for data processing to be done correctly so as not to information
negatively affect the end product or data output. Data processing
starts with data in its raw form and converts it into a more readable
format (graphs, documents, etc.) giving it the form and context
necessary to be interpreted by computers and utilised by employees
throughout an organization.
4.1.1.1 Six Stages of Data Processing
a. Data collection

Collecting data is the first step in data processing. Data is pulled
from available sources, including data lakes and data warehouses. ◆ Acquisition of data
It is important that the data sources available are trustworthy and
well-built so the data collected (and later used as information) is
of the highest possible quality.
b. Data preparation

Once the data is collected, it then enters the data preparation
stage. Data preparation, often referred to as “pre-processing” is
the stage at which raw data is cleaned up and organised for the
following stage of data processing. During preparation, raw data ◆ Pre-processing stage
is diligently checked for any errors. The purpose of this step is
to eliminate mistakes in data (redundant, incomplete, or incorrect
data) and begin to create high quality data for the best business
intelligence

SGOU - SLM - MCom Research Methodology 143