The goal of comparative analysis is to search for similarity and variance among units of analysis.
Conventionally, comparative analysis emphasized on the “explanation of differences, and the explanation of similarities.”
This helps to establish relationships between two or more phenomena and provide valid reasons.
Definition of Comparison Analysis
Comparisons are now carried out on various dimensions and levels, for example, geographical factors, time, age groups, etc.
For instance, you could use comparative analysis to investigate how your product features measure up to the competition.
For instance, if your business compares the cost of producing several existing products relative to which ones have historically sold well, that should provide helpful information once you’re ready to look at developing new products or features.
Comparative analysis is generally divided into three subtypes
Using quantitative or qualitative data and then extending the findings to a larger group.
These include:
Pattern analysis—identifying patterns or recurrences of trends and behavior across large data sets.
Data filtering—analyzing large data sets to extract an underlying subset of information. It may involve rearranging, excluding, and apportioning comparative data to fit different criteria.
Decision tree—flowcharting to visually map and assess potential outcomes, costs, and consequences.
What analysis to do with data?
Industry / Gender / Age / Size of Company / Tools Position / Salary/Hire Year/ Location/Department
Data Analysis and Business Insights
What is the business problem?
What data to analyze?
How to analyze the data?
Business case 1: More customers apply for loan in August than July
Business case 2: Internet service demand increased at beginning of COVID
Data Analysis and Business Insights
Five Big customer satisfaction: static/ dynamic/disassemble
Data Analysis and Business Insights
Data Analysis and Business Insights
Data Analysis and Business Insights
Workflow
What data to analyze? Exploring data sources:
Industry documents and reports
Defined business KPI
Related data files
How to analyze your data
Commonly used statistic analysis:
Find the pattern: mean, median, mode
Maximum and minimum
Percentage and ratio
How to analyze your data
Mean: average of numbers
Median: middle number in sorted, ascending or descending, list of numbers-more descriptive than average
Mode: value that appears most frequently in data set
Which to choose from the above three statistics:
Mean: number values are close
Median or mode: difference between number values are too big or extreme values
Not sure: use all – more statistics provides more insights
