Here we distinguish the terms “Analytics” vs “Analysis” and describe the main types of each as applied to finance.
Financial analytics is the use of processes and technology to combine datasets, perform analysis, and gain insights. Sometimes it can also trigger automated events. Here are the four main types:
- Descriptive analytics answers, “What happened?” or “What is happening?” It requires human intuition and effort to analyze and summarize historical trends in KPI dashboards, data visualizations, or reports.
- Diagnostic analytics answers, “Why did something happen?” It uses human intuition to develop hypotheses on what might be causing an issue and to explore and analyze the data to find patterns.
- Predictive analytics answers the question, “What will happen?” It uses statistical models to identify patterns in your data to project the probability of outcomes or forecast trends based on current and/or historical data.
- Prescriptive analytics answers the question, “What should we do?” It uses advanced machine learning to analyze data and recommend the optimal course of action or strategy moving forward.
Financial analysis refers to specific investigative actions to better understand your company’s past, present, or future performance. This analysis uses one of the types of analytics described above. For example, you would conduct a predictive sales analysis by using a predictive analytics correlation model. (Think of financial analysis as a subset of financial analytics.) There are many types of financial analysis you can perform but here are two illustrative examples:
Cash Flow Valuation Analysis
This dashboard visualizes cash flow-related KPIs such as internal rate of return by region against a target IRR, the number of investments by type and a detailed cash flow table. Other real-time indicators you could include are cash conversion cycle and working capital ratio. And, to help with cash flow management, you could use regression analysis to add a cash flow prediction.