The power of knowledge is apparent especially with the growing availability of information. This has led firms to turn to the scientific approach as the surest path towards financial success. Such an approach, as everyone across the industries from investors to all types of business professionals knows, means looking at the data to find answers. But just looking is not enough, as the answers will only show up after an interpretation process. Therefore, it is crucial to understand different data interpretation techniques to be able to methodically turn data resources into insights.
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Contents
The steps of data interpretation
A methodical approach that is taken by such processes as data interpretation means that it involves rules guiding conduct step by step. Of course, there is always room to improvise and innovate, that is how progress happens. But in order to improve the steps of the process, one first has to understand them.
There are five steps generally recognized as the stages of data interpretation. The first step, simply put, is deciding what you want to achieve with data interpretation, including the type of data that is to be analyzed. Then comes the collection of data itself. The third step is the most important as it is interpreted strictly speaking or data analysis. The process concludes with visualization of results so that the findings are presented clearly and, finally, reflection, which means reviewing the entire process to identify potential errors or discrepancies that may have affected the findings.
After reviewing the results and the course of the procedure, analysts may conclude that the goals established at the initial stage have been advanced enough at least for the time being. Alternatively, they may choose to start over, utilizing what they have learned about the possible improvements in the procedure.
All stages of data interpretation are important and should be done with utmost care if one wants to fully benefit from data assets. But the main engine of the process is the data interpretation techniques used for analysis in the third stage. Choosing the method that best fits the purposes at hand is a key factor in determining financial success that can be brought about.
The primary and most fundamental choice is between quantitative and qualitative analysis. Let us look at these data interpretation techniques one by one.
Quantitative analysis
As the name indicates, quantitative analysis is concerned with quantities or quantifiable information. This means that if we choose this technique for data interpretation, we commit ourselves to collect data that can be expressed in numerical values. Of course, there is plenty of business-relevant information that either originates in the form of numbers or can be translated to them.
Another benefit of quantitative analysis is that mathematics is as exact a science as it gets. This means that as long as we are asking precise “how much” type of questions, we are going to get very precise answers to them. Furthermore, mathematical calculations are objective thus if done correctly, they can be repeated by different people, and all should be getting the same results. This means that there is no place for personal bias at this stage. Of course, bias can still come in when we start to discuss what the numbers imply for us, but at least such discussions stand on a solid foundation of objectivity.
There are many different methods of quantitative analysis that are applicable for business and financial data. Let us look at some of these methods that have been successfully used for financial benefits.
- Regression analysis is used in project management for efficient resource allocation as it predicts outcomes by identifying relationships between variables.
- Cohort analysis is used in digital marketing to identify best marketing strategies by comparing how different cohorts of web users are affected by them.
- Trend forecasting is used in investing to predict future growth or decline of companies as well as other upward or downward trends.
Qualitative analysis
The other of the two main data interpretation techniques deal with the data that cannot be quantified appropriately without losing important features. This means textual data, like various documents, interview answers, or evaluations.
Qualitative methods are useful for utilizing various narratives and points of view as a source of knowledge. Their benefit is versatility and depth of explanations as texts can potentially express more than numbers and have more variations in describing phenomena. However, this type of information is more subjective thus it cannot escape bias and the analysis is inevitably less precise.
Financial firms and businesses often use qualitative methods to figure out what people think, want, or expect. This helps to predict future market shifts and improve products and services as well as internal operations thus leading to increased profit. Companies and investors utilize the following data interpretation techniques that are based on qualitative analysis.
- Content analysis is used in investment, for example, to measure the popularity of a product or brand by how often they are mentioned on social media.
- Discourse analysis is used to analyze internal communications to promote efficient cooperation, improve workspace climate and boost talent retention.
- Thematic Analysis is used in marketing and product research, for example, to analyze information from focus groups or interviews and identify customer expectations regarding particular types of products.
The importance of harnessing data interpretation techniques
Thus, there are very many data interpretation techniques under two major heading of qualitative and quantitative methods. The key to financial success is figuring out which methods to use for what kinds of questions.
But many problems in business and finance are too complex to be solved by just one method. Therefore, it is crucial to utilize different techniques for varying aspects of the problem at hand. Quantitative methods can give exact answers to very specific questions thus grounding interpretation of data in objectivity. But businesses and investment funds deal with people and their worldview and people see the world not only through numbers. Thus, qualitative data interpretation techniques provide the context of human preference to explain the deeper meaning of numbers.
Whether investing or allocating resources, combining the two major methods of analysis allows to make the right financial moves and lead to success.