Interpretation of Data: Essentials, Importance09/12/2022
Data interpretation is the process of reviewing data through some predefined processes which will help assign some meaning to the data and arrive at a relevant conclusion. It involves taking the result of data analysis, making inferences on the relations studied, and using them to conclude.
Therefore, before one can talk about interpreting data, they need to be analyzed first.
Data analysis is the process of ordering, categorizing, manipulating, and summarizing data to obtain answers to research questions. It is usually the first step taken towards data interpretation.
It is evident that the interpretation of data is very important, and as such needs to be done properly. Therefore, researchers have identified some data interpretation methods to aid this process.
Qualitative Data Interpretation Method
The qualitative data interpretation method is used to analyze qualitative data, which is also known as categorical data. This method uses texts, rather than numbers or patterns to describe data.
Qualitative data is usually gathered using a wide variety of person-to-person techniques, which may be difficult to analyze compared to the quantitative research method.
Unlike the quantitative data which can be analyzed directly after it has been collected and sorted, qualitative data needs to first be coded into numbers before it can be analyzed. This is because texts are usually cumbersome, and will take more time, and result in a lot of errors if analyzed in their original state. Coding done by the analyst should also be documented so that it can be reused by others and also analyzed.
There are 2 main types of qualitative data, namely; nominal and ordinal data. These 2 data types are both interpreted using the same method, but ordinal data interpretation is quite easier than that of nominal data.
In most cases, ordinal data is usually labeled with numbers during the process of data collection, and coding may not be required. This is different from nominal data that still needs to be coded for proper interpretation.
Observations: a description of the behavioral patterns seen in a group of people. The length of time spent on an activity, the sort of activity, and the form of communication used might all be examples of these patterns.
Groups of people: To develop a collaborative discussion about a study issue, group people and ask them pertinent questions.
Research: Similar to how patterns of behavior may be noticed, different forms of documentation resources can be classified and split into categories based on the type of information they include.
Interviews are one of the most effective ways to get narrative data. Themes, topics, and categories can be used to group inquiry replies. The interview method enables extremely targeted data segmentation.
Quantitative Data Interpretation Method
The quantitative data interpretation method is used to analyze quantitative data, which is also known as numerical data. This data type contains numbers and is therefore analyzed with the use of numbers and not texts.
Quantitative data are of 2 main types, namely; discrete and continuous data. Continuous data is further divided into interval data and ratio data, with all the data types being numeric.
Due to its natural existence as a number, analysts do not need to employ the coding technique on quantitative data before it is analyzed. The process of analyzing quantitative data involves statistical modelling techniques such as standard deviation, mean and median.
Some of the statistical methods used in analyzing quantitative data are highlighted below:
The mean is a numerical average for a set of data and is calculated by dividing the sum of the values by the number of values in a dataset. It is used to get an estimate of a large population from the dataset obtained from a sample of the population.
For example, online job boards in the US use the data collected from a group of registered users to estimate the salary paid to people of a particular profession. The estimate is usually made using the average salary submitted on their platform for each profession.
This technique is used to measure how well the responses align with or deviates from the mean. It describes the degree of consistency within the responses; together with the mean, it provides insight into data sets.
In the job board example highlighted above, if the average salary of writers in the US is $20,000 per annum, and the standard deviation is 5.0, we can easily deduce that the salaries for the professionals are far away from each other. This will birth other questions like why the salaries deviate from each other that much.
With this question, we may conclude that the sample contains people with few years of experience, which translates to a lower salary, and people with many years of experience, translating to a higher salary. However, it does not contain people with mid-level experience.
This technique is used to assess the demography of the respondents or the number of times a particular response appears in research. It is extremely keen on determining the degree of intersection between data points.
Some other interpretation processes of quantitative data include:
- Regression analysis
- Cohort analysis
- Predictive and prescriptive analysis
Identifying trends and anticipating demands
Users may employ data analysis to gain useful insights that they can use to foresee trends. It would be based on the expectations of the customers. When industry trends are detected, they may be used to benefit the whole industry.
For Example; people are more concerned about their health post covid, hence people are more likely to buy an insurance policy. The fundamental datasets for data analysis, data cycle of collection, evaluation, decision-making and monitoring should be followed by all next-gen companies.
Informed decision making
To take action and adopt new processes, the management board must evaluate the data. This underlines the need for well-evaluated data and a well-organized data collecting method. A choice is only as good as the information that went into making it. Industry leaders that make data-driven decisions have the opportunity to differentiate themselves apart from the competition. Only when a problem is recognized and a goal has been established will the most decisive steps be taken. Identification, thesis formulation, data collecting, and data communication should all be part of the data analysis process.
Many business experts do not consider data interpretation to be expenditure, despite the fact that many organisations invest money in it. Instead, this investment will assist you in lowering expenses and increasing the efficiency of your company.