Data Analysis and Data Interpretation are more important than ever with the emergence of the digital age and the sheer amount of data can be overwhelming. A study by Digital Universe stated the total data supply in 2012 to be 2.8 trillion gigabytes. 10 years later, we would leave you to guess the current data supply in 2022. Depending on the data alone, it is evident that the primary objective of any thriving business in today’s time will be to have the potential to analyze complex and critical data, create actionable findings and adapt to the evolving market requirements at will.
New age Business dashboards are the current tools for big data analytics. It has the ability to display key performance indicators (KPIs) for quantitative and qualitative data analysis. Moreover, they are perfect to make quicker and data-driven decisions that propel today’s business leaders to long term success. With the help of seamless visual communication, data dashboards allow enterprises to participate in real-time and critical decision-making and are major tools in data interpretation. But before that, let us try to understand the meaning of data interpretation.
What Is Data Interpretation?
Data Interpretation means the method of using different analytical processes to review data and come to informed conclusions. The interpretation of data allows researchers to segment, manipulate, and describe the information to answer important questions.
The significance of data interpretation is clear and this is the main reason why it must be done properly. Data is most likely to come from different sources and has an inclination to join the analysis process without any particular order. Data analysis can be completely subjective. Having said that, the nature and objective of data interpretation will change from one business to another depending on the nature of data being analyzed. There are multiple kinds of interpretation processes that are implemented based on data nature, the two most popular classifications are “qualitative analysis” and “quantitative analysis”.?
Though, before starting any data interpretation inquiry, it is important to identify that visual representation of data is pointless unless an informed decision is made about the measurement scales. It is best to decide the scale of measurement before proceeding with data analysis as it will have a long-term effect on the data interpretation ROI. The scales are:
- Nominal Scale: This includes the non-numeric segments that can’t be ranked or compared numerically. Variables are exhaustive and selective.
- Ordinal Scale: Special segments that are exhaustive yet have a logical order. A good example of ordinal scales are good, very good, excellent etc. or agree, disagree, strongly disagree etc.
- Interval: A measurement scale where data is categorized with orderly and equal distances between two consecutive categories. It will always have an arbitrary zero point.
- Ratio: This particular scale includes the features of all the above three scales.
Once the scales of measurement are chosen, it is time to decide which of the two interpretation methods will be perfect for your business data requirements. Let us try to understand particular data interpretation processes and potential Data Interpretation issues that can arise in the future.
How to Interpret Data?
While interpreting data, an analyst must look to identify the differences between correlation, causation, and coincidences, along with other biases. Further, he needs to think about all the factors responsible for producing a result. There are multiple data interpretation methods that you can use.
Data Interpretation is introduced to assist businesses to make sense of numerical data that is collected, analyzed, and presented. Having a preferred method for data interpretation will offer a solid structure and foundation to the analyst. This is because multiple teams can use different approaches to interpret the same data and end up with inconsistent results. Diverse processes will lead to double efforts, contradicting solutions, waste of time and energy. In the next section, let’s try to understand the two main processes of data interpretation: quantitative and qualitative analysis.
Qualitative Data Interpretation
Qualitative data analysis is another word for categorical analysis. In this method, data is not mentioned in numerical values or patterns, but with the use of descriptive statistics. Usually, narrative data is collected by using a number of person-to-person methods. These techniques are as under:
- Observations - Describing behavioral patterns that happen within an observation group. The patterns can include the total time spent in an event, the type of event, and the preferred communication method.
- Focus Groups - Make a group of people and ask them questions pertaining to the research topic to have a quality discussion.
- Secondary Research - Similar to patterns of behavior,various types of documentation resources can be coded and divided based on the kind of their material.
- Interviews - ?It is one of the most effective methods for narrative data. Inquiry responses can be categorized by theme, topic, or category. The interview approach enables high-quality data categorization.
A major difference between qualitative and quantitative analysis is evident in the data interpretation stage. Qualitative data is quite open to interpretation and it must be coded. This will enable the grouping and labelling of data into identifiable themes. Person-to-person data collection methods can result in disputes related to complete analysis, qualitative data analysis is often presented using the three primary principles: notice things, collect things, and think about things.?
Quantitative Data Interpretation
If you could sum up Quantitative data in a single word, it would be “numerical”. There are certain assumptions surrounding data analysis, but you can be sure that if your research includes a lot of numbers, it is not qualitative data research. Quantitative analysis means a set of methods using which numerical data is analyzed. It often includes the use of statistical modelling like standard deviation, mean and median. Now, let us go through the most widely used statistical terms:
- Mean - A mean corresponds to a numerical value for a set of responses. In terms of handling a data set or multiple data sets, a mean will comprise a central value of a particular set of numbers. It is the sum of the values divided by total values in a data set. In other words, this concept is similar to an arithmetic mean, average and mathematical expectation.
- Standard Deviation - SD is another term generally used in quantitative analysis. Standard Deviation unveils the distribution of the responses around the mean. It depicts the degree of uniformity in the responses. Also, along with the mean, it offers a detailed overview of data sets.
- Frequency Distribution - FD is a measurement that calculates the occurrence of a response in a data set. When working with a survey, frequency distribution has the ability of identifying the number of times a particular ordinal scale response comes up. In other terms, Frequency distribution is eager to identify the level of consensus among data points.
More often than not, quantitative data is calculated by visually showcasing correlation tests between two or more significant variables. Multiple methods can be used simultaneously or separately and results can be carried out to find the conclusion. Some of the other key methods of quantitative data are:
- Regression Analysis - ?Typically, regression analysis leverages historical data to identify the relation between a dependent variable and two or more independent variables. Understanding which variables are related and how they are created in the past enables you to predict potential results and make an informed decision in the future. For instance, if you wish to predict your sales for the coming month it is easy to use regression analysis to realise what factors will impact them like products on sale, the launch of a new event etc.
- Cohort Analysis - This process understands groups of users who share common features during a specific time period. In a business setup, cohort analysis is typically used to identify different kinds of customer behaviors. For instance, a cohort could be a group of users who signed up for a free trial on a particular day. Then an analysis would be undertaken to understand how the users act, the type of actions that are carried out, and how their behavior is different from other user groups.
- Predictive Analysis - Easy to understand from the name, predictive analysis is the process that looks to predict future developments by analyzing past and current data. Equipped by technologies like artificial intelligence and machine learning, predictive analytics procedures allow businesses to point trends or possible issues and plan informed strategies in advance.
- Prescriptive Analysis - Also fueled by predictions, the prescriptive analysis method uses methods like graph analysis, complex event processing, neural networks, to try and discover the impact that future choices will have in order to adjust them before they are made. This enables enterprises to build robust and practical business tactics.
- Conjoint Analysis - Usually applied to survey analysis, the conjoint analysis approach is used to study how users value different features of a product or service. This enables researchers and brands to define pricing, product features, brand packaging and more. A popular use is menu-based conjoint analysis where individuals are offered a menu of options. They can leverage the menu options to develop a product. Similarly, analysts can identify which features they would like over others and come to conclusions.
- Cluster Analysis - In the end, cluster analysis is a technique used to group specific objects into segments. As there is no target variable while using cluster analysis, it is a helpful method to determine hidden patterns and trends in the data. In a business point of view, clustering is used for user segmentation to offer targeted experiences. In market research terms, it is used to determine age groups, location, net income and more.
Conclusion
In this post, we tried to cover all the critical aspects pertaining to data interpretation and how to interpret data effectively. We hope you have a better understanding of the topic. It is evident that data interpretation is extremely crucial. What is your current understanding of data interpretation? Would you like us to make another blog consisting of Data Interpretation problems and methods for better data analysis? Let us know in the comments below or write to us at [email protected].
