Descriptive analytics is a metric to summarize current and historical data based on certain attributes to identify trends and relationships. Descriptive tax analytics is a metric that provides summarize transactions by jurisdictions and calculation of existing processes and policies associated with a company's tax liability which helps the company assess how well the tax function is performing. An example of descriptive tax analytics is determining how much federal income taxes the company paid last year. The data that can be used to perform this analysis are the payroll summary, employees list, and federal income tax rate.

Predictive analytics uses data, statistical algorithms, and machine learning techniques to identify common attributes or patterns that may be used to forecast similar activity based on historical data. For example, predictive tax analytics combine new information and historical data to determine tax liabilities, for instance, to use it to figure out expected taxes to be paid over the next five years. Another example of predictive analytics in tax would be to identify the total cost of research and development or R&D tax credit that the company might qualify for the next five years. The data that the accountant can use for this analysis are the employee timesheet, the total amount of expenses related to R&D, and the project name.