Steps That Make Data Processing An Integral Part Of Market Research

Data Processing

As business starts expanding, more data flows in and this increases the demand for data entry and processing which requires hiring more employees and infrastructure investments. Businesses conduct market research to understand the latest trends and patterns in the market and identify what the customers want, analyze market size, determine which products to release, reduce the risk of product or business failure, and forecast future trends. They have to analyze and process large volumes of data accurately to derive valuable insights and make the right business decisions. Professional data processing services can help business in manage and process this data.

Data processing involves translating unorganized data into meaningful data that can be analyzed. It is a process of converting images, graphs, vector files, audio charts into valuable insights. Surveys and questionnaires are distributed among the audience to understand them, learn more about them and collect insights throughout the product or market lifecycle. The steps involved in data processing are:

  • Data editing: Data is gathered from questionnaires, surveys etc to detect errors or omissions. The whole data collection is checked and mistakes are corrected. Once it is assured that the information is accurate, consistent, uniform, complete, it can be tabulated. There are four different types of editing data.
    1. Editing for quality: This process checks whether the data forms are complete, and free of bias, whether the recording are free of errors, and if there is any inconsistencies in responses within the limits.
    2. Field editing: The answers of the respondents may have some abbreviations or illegible writing, which is rectified by the enumerator.
    3. Editing for tabulations: Here the data are modified to facilitate tabulation
    4. Central editing: This is done by the researcher where all the questionnaires or forms from the enumerators or respondents are analyzed. Inappropriate answers are removed and “no answer” is entered when reasonable attempts to get the appropriate answer fail to produce results.
  • Data coding: Coding is essential for effective analysis. In this process, answers are narrowed down to critical information that is important for the analysis. Coding decisions actually begin at the stage of designing the questionnaire. It allows pre coding the questionnaire choices which makes computer tabulation easy. In hand coding, the standard method is to code in the margin with a colored pencil, whereas in other methods, the data from the questionnaire is transcribed into a coding sheet.All these coding process are conducted to avoid coding errors. Coding is a two part operation which involves:
    1. deciding the categories to be used
    2. allocating individual answers to them

    Since coding eliminates a lot of information in the raw data, it is important that researchers design category sets carefully in order to utilize the available data effectively.

  • Classification of data: In this process the data are categorized into different homogeneous groups for better interpretation. The grouping of data is based on uniformity of attributes and similarity of data. Classification of data is important if the data collected is diverse.Good classification is characterized by clarity, homogeneity, equality of scale, purposefulness and accuracy.The process of data classification involves the following steps:
    • The complex data is organized into concise, logical and intelligible form
    • Identifies similarities and dissimilarities
    • Allows comparative studies
    • Provides better clarity on significance of the data
    • Underlying unity amongst different items is set clearly and expressed
    • Data is so arranged that analysis and generalization becomes possible.

    The data classification is done based on quantity of the data and quality of the data. Quantitative data includes quantifiable variable and qualitative includes data on the basis of attributes or qualities.

  • Data tabulation: In this process, raw data is summarized and displayed in the form of tables for easy analysis. Here the tables is divided into Frequency tables, Response tables, Contingency tables, Uni-variate tables, Bi-variate tables, Statistical tables and Time series tables.The steps in tabulation would include:
    • Title of the table
    • Columns and rows
    • Captions and stubs
    • Rulings
    • Arrangement of items
    • Deviations
    • Size of columns
    • Special emphasize
    • Unit of measurement
    • Approximation
    • Foot notes
    • Total
    • Source

    The tabular representation of the data helps the readers to quickly understand the findings of the research. Smaller and simpler tables may be presented in the text while the large and complex table should be mentioned at the end of the chapter or report.

  • Data Diagrams: In this step, the data is represented in the form of charts or graphs.
    • Charts: Charts are a diagrammatic data representation format and can be in the form of bar charts, pie charts that are uni-dimensional and square and circle charts that are two-dimensional.
    • Graphs: Graphs show the relationship between two variables by means of either a curve or a straight line. Graphs may be divided into two categories- time series and frequency distribution.

Data processing translates data into valuable information for use in business and academic research. With accurate information, companies can make the right business decisions. Organizations can rely on data entry services to organize and streamline their data and ensure accurate processing of the large volumes of information they collect.

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