What are the best techniques for data analysis to evaluate past decisions?

1 Descriptive analysis

Descriptive analysis is the simplest and most common technique for data analysis, as it summarizes the main characteristics and trends of a data set. It can help you answer questions such as what happened, when, where, and how often. Descriptive analysis can use tools such as tables, charts, graphs, or dashboards to present the data in a clear and concise way. However, descriptive analysis does not explain why something happened, or what will happen next, so you need to combine it with other techniques for deeper insights.

2 Diagnostic analysis

Diagnostic analysis is the next step after descriptive analysis, as it helps you identify the causes and factors behind the patterns and anomalies in your data. It can help you answer questions such as why something happened, what influenced it, and how it relates to other variables. Diagnostic analysis can use tools such as correlation, regression, or hypothesis testing to explore the relationships and effects among the data. However, diagnostic analysis does not predict what will happen in the future, or what actions you should take, so you need to consider other techniques for more guidance.

3 Predictive analysis

Predictive analysis is a more advanced and complex technique for data analysis, as it uses statistical models and algorithms to forecast what will happen in the future based on your past data. It can help you answer questions such as what is likely to happen, how confident are you about it, and what are the possible scenarios. Predictive analysis can use tools such as machine learning, artificial neural networks, or decision trees to generate predictions and probabilities. However, predictive analysis does not guarantee what will happen, or what is the best decision, so you need to evaluate the accuracy and validity of your models and assumptions.

4 Prescriptive analysis

Prescriptive analysis is the most sophisticated and innovative technique for data analysis, as it uses optimization and simulation techniques to recommend the best course of action for a given situation based on your past data. It can help you answer questions such as what should you do, how will it affect your goals, and what are the trade-offs and risks. Prescriptive analysis can use tools such as linear programming, genetic algorithms, or Monte Carlo methods to generate optimal solutions and alternatives. However, prescriptive analysis does not account for all the uncertainties and complexities of the real world, or the human factors and preferences, so you need to apply your judgment and experience to make the final decision.

5 Comparative analysis

Comparative analysis is a useful technique for data analysis, as it helps you compare and contrast your past decisions with other alternatives or benchmarks. It can help you answer questions such as how did you perform, what were the differences and similarities, and what can you learn from them. Comparative analysis can use tools such as ratio analysis, variance analysis, or benchmarking to measure and evaluate your performance and outcomes. However, comparative analysis does not provide a complete picture of your decision-making process, or the context and constraints of your situation, so you need to supplement it with other techniques for more insights.

6 Reflective analysis

Reflective analysis is a vital technique for data analysis, as it helps you review and learn from your past decisions and their consequences. It can help you answer questions such as what did you do well, what did you do poorly, what did you learn, and what can you improve. Reflective analysis can use tools such as feedback, debriefing, or self-assessment to collect and analyze your experiences and perceptions. However, reflective analysis does not guarantee that you will apply your learning and improvement to your future decisions, or that you will avoid the same mistakes, so you need to commit to your action plan and monitor your progress.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?