Data interpretation: Overview, analysis, types and advanced methodologies
Introduction
In the present digital age, data has occupied the centre stage when it comes to analysis as well as interpretation. Analysis and interpretation of data is important because data is deemed as the most valuable currency for businesses in the present times. The value of data would continue to increase further in the coming time as it becomes the prime commodity of exchange between companies.
When we look at data analytics, there are many processes associated with it. Some of the processes include processing, interpretation and visualization. In order to understand the crux of all these processes, it is important to get acquainted with data analytics and the best means to achieve this objective is to enrol in the best data analyst course online. This type, of course, serves as a passport for entry into the data analytics market. Despite a large number of processes associated with data analytics, the two most important ones include data interpretation and visualisation. In this article, we understand the dynamics of these two processes in much more detail.
Data interpretation: Overview and analysis
Data interpretation is a process that makes use of different types of analytical methods to derive relevant conclusions from different data sets and portray the findings in a simplified manner. In simple terms, data interpretation is the process that summarizes information which has been gathered from different sources and finally helps in the communication of these findings in a very lucid way. The importance of interpretation of data is as important as the process of analytics itself.
No matter how much time is devoted to the process of analytics, it is important that the findings need to be reported in a short span of time. This is only possible when a data analyst makes use of both the qualitative as well as quantitative aspects of the information. While the critical enquiry is important, it is the visual representation of data that mentions and highlights the depth of this critical enquiry.
The art of interpretation
Data interpretation is both a science as well as an art. This means that it is a blend of qualitative as well as quantitative methodologies. Data interpretation involves the correlation between various variables as well as the understanding of causative factors associated with them. After understanding these baseline details, an analyst is able to interpret data with a lot of ease.
Let us now understand the two main methods of data interpretation, the first being qualitative data interpretation and the second being quantitative data interpretation.
Qualitative data interpretation
Qualitative data interpretation answers the context of the information and narrates the entire process of data analysis in a descriptive format.
Observation
The first technique associated with qualitative data interpretation is observation. As the name indicates, observation involves the detailed study of activity for a long period of time from a research perspective. The subject is studied in detail and various activities related to it are interpreted by other stakeholders.
Focus Group discussion
Focus Group discussion is another qualitative technique that is employed for the process of data analytics and insights. Focus Group discussion allows all the stakeholders to participate in a holistic discussion related to the research subject. Consequently, the findings of the discussion are communicated in such a manner that the concerns of all the stakeholders are taken into account.
Usually, the methods that are applied in qualitative analytics are a precursor to the interpretation of results in an effective manner. Another important technique that is employed in qualitative analysis is secondary research. Secondary research involves the study and observation of documentation resources so that detailed knowledge is attained about the research subject before the findings are communicated.
Quantitative data interpretation
When it comes to quantitative data interpretation, we generally refer to the communication of findings in the form of numerical results. The advantage of quantitative data interpretation over qualitative analytics is that the former gives a discrete picture of the findings. The quantitative findings can be interpreted in statistical terms. For instance, after recording a large number of responses, we may categorize data sets into different types based on the mean, mode or standard deviation.
The method to be chosen completely depends upon the demand of the research subject and the need of the stakeholders. In addition to this, one of the most widely used methods for communication of findings and data interpretation is the frequency distribution tables. After conducting a particular survey, data is interpreted in the form of frequency distribution tables so that the stakeholders understand the responses at the first instance itself without going into much detail.
Advanced methodologies and techniques
We can also use advanced methodologies and techniques for data interpretation including regression analysis and cohort analysis.
Regression analysis
In regression analysis, a historical data set is used as a test subject and a relationship is established between two variables. While the values of the independent variable are known, the values of the dependent variable are unknown. Based on historical data, the values of the independent variable are substituted and the values of the dependent variable are obtained.
In simple terms, the values of the independent variable are controlled. After these values are obtained, a graph is plotted based on historical data sets and this graph is extrapolated to predict outcomes in the future. This is one of the most important and most commonly used data interpretation techniques.
Cohort analysis
Cohort analysis establishes a relationship between stakeholders who share common characteristics for a period of time. For instance, we can use this technique to understand the behaviour of customers who are interested in a particular range of products on an E-Commerce platform. Consequently, the data related to these customers is analysed as a whole and is interpreted as finding for that particular group of customers. The advantage of using this methodology is that it helps in targeting a wide range of customers at a time rather than targeting them individually for the purpose of marketing.
Conclusion
We can use other methods of data interpretation ranging from predictive analytics to prescriptive analytics. The techniques and methodologies of data analytics and interpretation are numerous. However, the choice of the appropriate data interpretation technique ensures that the end results are aligned with the goals and objectives that we had set at the onset of the project.
