Data Processing


Data Processing

NSMX offers exhaustive internal data processing, cleaning, analysis and open ended coding services.

  • Data preparation includes thoroughly checking the data and inspecting each questionnaire or observation form. Verification ensures that the data from the original questionnaires have been accurately transcribed and meaningful information has been collected. We are able to accommodate imported data from nearly any source (ASCII, Binary, SPSS, Excel, Dimensions). Our centralized data refinement allows us to immaculate, validate and export the sample data for excellent reporting that will allow clients to identify emerging trends.

  • Open ended coding – We know the qualitative aspect of the research. Through Open ended coding services NSMX enable market research organizations to effectively analyse and comprehend customer responses, and draw rich and actionable insights. We leverage a proprietary suite of unique Artificial Intelligence tools to derive and analyse explicit as well as latent information within open-ended customer responses. We have extensive experience in preparing listings, code frames, verbatim typing, verbatim coding, open ended programming of ad-test, product test, and CAPI / CATI studies. Utmost care is taken to create netting or sub headings of the code frame so tables generated has decisive data.

Data processing in research is the process of collecting research data and transforming it into information usable to multiple stakeholders. While data can be looked at in numerous ways and through various lenses, data processing aids in proving or disproving theories, helping make business decisions, or even advancing enhancements in products and services. Data processing is even used in research to understand pricing sentiments, consumer behavior and preferences, and competitive analysis.

While it is a critical aspect of a business, data processing is still an underutilized process in research. With the proliferation of data and the number of research studies conducted, processing and putting the information into knowledge management repositories like InsightsHub is critical.


Data processing is the transformation of raw data into meaningful information through various techniques and methods. It involves a series of actions or steps to clean, organize, analyze, and interpret data to extract useful insights and support decision-making. Data processing is a fundamental component of data management and is essential in a wide range of fields, including business, science, research, and technology

Key Stages and Aspects of Data Processing

  • Data Collection
    Data processing begins with the collection of raw data from various sources. This data can be in the form of text, numbers, images, audio, or video. We at NSMX used to collect the data through online survays, which is one of most effective method of data collection.

  • Data Entry and Recording
    We used to store the raw data into digital formats or databases for processing. This stage involves data entry personnel manually inputting data or automated methods for data capture.

  • Data Cleaning
    The collected Raw data from the survays often contains errors, inconsistencies, duplicates, missing values, or outliers. We at NSMX used to find the accurate data for the clients. This data cleaning involves identifying and rectifying these issues.

  • Data Transformation
    Data may need to be transformed into a standardized format or structure for analysis. This includes converting units, encoding categorical data, and creating derived variables. Transformation can also involve aggregating or summarizing data.

  • Data Integration
    Combining data from multiple sources into a unified dataset is known as data integration. It can involve merging tables, databases, or files. Integration allows for a more comprehensive analysis of information.

  • Data Storage
    Processed data is typically stored in databases, data warehouses, or data lakes for easy access and retrieval. Data storage systems are designed to handle large volumes of data efficiently.

  • Data Analysis
    Data analysis involves using statistical, mathematical, or computational methods to extract insights, patterns, and trends from the processed data. Techniques may include descriptive statistics, regression analysis, machine learning, and data visualization.

  • Data Interpretation
    After analysis, the results are interpreted to derive meaningful conclusions and insights. Interpretation involves understanding the implications of the findings and how they relate to the original objectives.

  • Reporting and Visualization
    Data processing often culminates in the creation of reports, dashboards, or visual representations of the results. Visualization tools and charts make it easier to communicate findings to stakeholders.

  • Data Security and Privacy
    Data processing also involves ensuring the security and privacy of sensitive data. This includes implementing access controls, encryption, and compliance with data protection regulations.