Skip to content

DA3. Data Analysis Techniques

Statement

  • Given the importance of data analysis, discuss three possible data analysis techniques you can use. What does each technique bring to the analysis of data?
  • Provide a discussion on the three different techniques selected, and include both the advantages and disadvantages of each

Answer

Introduction

Data analysis is the process of digesting raw data into useful information that helps in diagnosing issues, predicting trends, and making informed decisions. The process starts by defining the problem and the objectives of the analysis; then collecting, cleaning, and transforming the data; and finally analyzing the data to extract results, and then communicating the results using a suitable method (Grant, 2020).

Multiple data analysis techniques can be used to analyze data, such as descriptive, diagnostic, predictive, and prescriptive analysis. The simplest one is descriptive analysis and it answers the question of “What happened?”.

Diagnostic Analysis

Diagnostic analytics examines data to understand the root causes of events, behaviors, and outcomes (Amplitude, 2023). It answers questions like “Why did an event happen?” and “How did changes impact the outcome?”. It is a deeper form of descriptive analysis that goes into the details of the data.

The advantages of Diagnostic Analysis include its ability to detect anomalies and enhance visibility over events in the business; the results are also helpful in solving problems and avoiding future issues. The disadvantages include the difficulty of identifying causation vs correlation, that is, two events may seem connected but none is the cause of the other; it also depends on the quality of the data and the tools used; and finally, it depends on the skills of the analyst to look for into the right subset of data.

Predictive Analysis

Predictive analysis examines data to predict future outcomes based on historical data. It answers questions like “What is likely to happen?” and “What is the probability of an event happening?”. It uses both descriptive and diagnostic analysis as part of its process (Shaw, 2020).

The advantages of predictive analysis include its ability to give an insight into the future which helps executives make better informed decisions about products and services. The disadvantages include that it can only give a probability or an estimate and the accuracy of the results may vary; it also uses statistical algorithms and machine learning techniques which require specialized people to work on; and finally, it is generally hard to implement a future predictor as it requires a lot of data to start with.

Prescriptive Analysis

Prescriptive analysis is the most advanced form of data analysis. It examines data to determine the best course of action to take in a given situation. It answers questions like “What should we do?” and “What is the best course of action?”. It uses descriptive, diagnostic, and predictive analysis as part of its process (Segal, 2022).

The advantages of prescriptive analysis include its importance in improving processes, mitigating risks, and presenting insights about alternative scenarios. The disadvantages include that its results are only reliable for short-term planning and are less useful for long-term predictions; it is the most complex, time, and resource-consuming form of data analysis; and finally, it uses AI and machine learning which requires highly skilled people to get good results.

Conclusion

The text discussed three data analysis techniques: Diagnostic, Predictive, and Prescriptive analysis. Diagnostic analysis is the simplest form of data analysis and it helps in understanding the root causes of events. Predictive analysis helps in predicting future outcomes based on historical data. Prescriptive analysis is the most advanced form of data analysis and it helps in determining the best course of action to take in a given situation.

References