Data processing is often misunderstood as data manipulation or data analysis, but it is much more than that. Multiple decisions are taken based on the accurate processing of data, and brands and researchers rely on data to make actionable decisions. The processing of data in research is one of the most critical components of the research process and can be the difference between brands being successful or not.
While market research data can be processed in multiple ways, it all boils down to what sort of insights you elicit from the collected data and the impact it makes on your decision-making processes.
What is data processing in research?
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.
Through this process, research stakeholders turn qualitative data and quantitative data from a research study into a readable format in the form of graphs, reports, or anything else that business stakeholders resonate with. The process also provides context to the data that has been collected and helps with strategic business decisions.
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.
Discover a multitude of examples of qualitative data in education, unveiling how it enriches our understanding of learning dynamics and student experiences.
Data Processing Steps in Research
The data processing cycle in research has six steps. Let’s look at these steps and why they are an imperative component of the research design.
-
Collection of research data
Data collection is the primary stage in the research process. This process could be through various online and offline research techniques and could be a mix of primary and secondary research methods. The most commonly used form of data collection is research surveys. However, with a mature market research platform, you can collect qualitative data through focus groups, discussion modules, and more.
LEARN ABOUT: Best Data Collection Tools
-
Preparing research data
The second step in research data management is preparing the data to eliminate inconsistencies, remove bad or incomplete survey data, and clean the data to maintain consensus. This step is critical since insufficient data could render research studies wholly useless and could be a waste of time and effort.
LEARN ABOUT: Customer data management
-
Inputting research data
The next step is putting the cleaned-up data into a digitally readable format consistent with organizational policies, research needs, and more. This step is critical since the data is then put into online systems compatible with managing research data.
-
Processing research data
Once the data is input into systems, it is critical to process this data to make sense of it. The information is processed basis on needs, types of data collected, time available to process data, and multiple other factors. This is one of the most critical components of the research process.
-
Output of research data
This stage of research data processing is where it gets turned into insights. This stage allows business owners, stakeholders, and other personnel to look at data in graphs, charts, reports, and other easy-to-consume formats.
-
Storage of the processed research data
The final stage of the steps of data processing is the storage. Keeping the data in a format that is indexable, searchable, and creates a single source of truth is essential. Knowledge management platforms are most commonly used for storage of processed research data.
Benefits of data processing in research
Data processing can differentiate between actionable insights and their lack of existince in the research process. However, there are some distinct benefits and advantages of processing research data. They are:
-
Streamlined processing and management:
When research data is processed, there is a high probability that this data is going to be used for multiple purposes at this moment and in the future. Accurate data processing helps streamline how research data is handled and managed.
-
Better decision making:
With accurate data processing, the probability of making sense of the data to get to decisions faster and better becomes possible. Decisions then are taken based on data that tells stories instead of on a whim.
-
Democratization of insights:
Processing data allows raw data to be turned into a format that works for multiple teams and personnel. Easy-to-consume data allows for the data democratization of insights.
-
Reduced costs and high ROI:
Data-backed decisions aid brands and organizations in making decisions based on evidence-backed data from credible sources. This helps to reduce costs since decisions are linked to data. The process also helps to maintain a very high ROI on business decisions.
-
Easy to store, report and distribute:
Processed data is easier to store and manage since there is a structure to raw data. This data is then referenceable and accessible in the future and can be called upon when required.
Data processing in research Examples
Now that you know the finer nuances of data processing in research let us look at specific examples to help you make sense of its importance.
Example at a global SaaS brand
Software-as-a-service (Saas) brands have a global footprint and have a plethora of customers – multiple times both B2B and B2C customers. Each brand and customer has different problems they hope to solve using the SaaS platform and hence have distinct needs. While conducting consumer research, the SaaS brand can understand consumer expectations, purchase and buying behaviors, and more. This also helps to profile customers, align product or service enhancements, manage marketing spending, and more basis on the processed research data.
Other examples of this data processing include retail brands with a global United States footprint with customers across various demographics vehicle manufacturers and distributors with multiple dealerships, and more. Everyone conducting market research needs to leverage data processing to make sense of the data.
LEARN ABOUT: Dealership Marketing Strategies & Tips
Process your research data with QuestionPro
Collecting research data, including survey research and other qualitative data, is possible with an enterprise-grade research platform like QuestionPro. Due to the nature of the tool, there is also the distinct possibility of processing data and making decisions that matter. The platform also allows you to process and store data for easy access. Get started now!
