Types of Data Analysis

Data Analysis is categorized as below.

• Descriptive Analysis focuses on summarizing historical data, providing an overview of past events and trends.
• Diagnostic Analysis digs deeper into data to understand why specific events occurred, identifying the root causes of trends or anomalies.
• Predictive analysis involves forecasting future outcomes based on historical data and statistical models, enabling proactive decision-making.
• Prescriptive analysis goes beyond prediction to recommend actions, offering insights on the best course of action that can be taken to optimize outcomes.

Analysis Type	Business Examples
Descriptive	The daily actual production volume was 10% below the target. Summary of sales data for the period 1 Jan 2023 – 31 Dec 2023.
Diagnostic	In this month, the machine breakdown has increased by 6%. Why? Shifting to lower cost spare parts? • Investigating factors leading to a decline in customer satisfaction
Predictive	With a probability of 90%, Product xyz will receive 20+ orders in the next month. Supplier abc will 100% face delivery problems for the next 10 deliveries. Estimating equipment maintenance needs based on usage patterns
Prescriptive	A prescription to customer abc on the medicinal list of items to be consumed based on health check report. A prescription to the online maintenance team to deploy additional 5 team members online at site S1 to handle additional customer requests on the festival day Suggesting pricing strategies for improved profitability based on past sales data.

As we can see, being data-driven can help organisations in many ways. In the next section, we will look at the differences between AI, ML, Deep Learning, Data Science, Data Engineering, Data Analysis and how they can reinforce each other.