Non-Predictive Analysis

In the blog 'How To: Solve Business Problems With Data' we broke non-predictive data analysis into four categories:

1.      Geospatial

2.      Segmentation

3.      Aggregation

4.      Descriptive.

Let's now consider each in turn.

Geospatial Analysis

This type of analysis uses location-based data to help drive conclusions. Examples include identifying customers by geographic region, calculating the distance between store locations or creating a trade area based upon customer locations.

Segmentation Analysis

Segmentation is the process of grouping data together. Groups can be simple, such as customers who have purchased different items, to more complex segmentation techniques where similar stores are identified based on customer demographics.

Aggregation Analysis

This methodology means calculating a value across a dimension and is commonly used in data analysis. For example, you may want to aggregate sales data for a salesperson by month. Then, you may want to aggregate across dimensions, such as sales by month per sales territory. Aggregation is often a function of reporting to "slice and dice" information to help managers make better-informed decisions and view performance.

Descriptive Analysis

Descriptive statistics provide summaries of data. Examples could be calculating the average GPA scores for applicants to a school or calculating the batting average of a professional baseball player. Some of the commonly used descriptive statistics are Mean, Median, Mode, Standard Deviation, and Interquartile range.