Embedded in nearly everything we do is the process of gathering information, and processing it in some sort of way in order to reach conclusions about the things around us. Whether you call it statistical analysis, analytics, data science, "big data", or anything else, it all boils down to the same general process of gathering data and using it to greater our understanding of what is being observed around us.
The terms above typically refer to quantitative, or number based, analysis. An example of this is deciding what firm to invest money in based on their average annual rate of return. However, some analysis is qualitative, or non-numerical. An example of this is deciding which social event to attend based on which one seems like it will be more enjoyable. I firmly believe that, for significant analyses, there is both a quantitative and a qualitative component
Most data analyses, from simple totaling or averaging to complex numerical modeling are motivated by one of the following three desires, and fall into one of the categories below.
Managing Expectations:
- Risk Assessments
- Predictive Analyses
- Investigations (Cause and Effect, Scientific Studies, etc.)
Assessing Impact:
- Performance Management
- Customer Experience
- Assessing Secondary and Large Scale Impacts
Managing Resources:
- Data driven Decision Making
- Strategic Planning
- Resource Allocation and Prioritization
Information about each of these processes forthcoming .....