I've discussed some of this before, trying to define what makes a pattern a pattern. However, no one wins a Nobel Prize or gains competitive advantage by rehashing existing patterns. Some do, however, use new patterns to make novel discoveries or exploit behavioral trends; the knowledge from new patterns gives them an edge using the same data that everyone else has at hand.
A downside to pattern reliance is the human predisposition to assign meaning to events when there isn't necessarily any. Over the past 500 years, two related, but different concepts arose, to help sort out the problems that the search for an explanation can cause.
- Scientific process: The scientific process focuses on creating a hypothesis and performing experiments (or using other means) to collect data to support or refute the original hypothesis.
- Statistics: Statistics developed as a discipline to use data to combat a tendency to rely on subjective experiences (data can be thought of as a more "objective" experience, although how it's measured, collected, and recorded becomes its own can of worms).
Pattern based analytics can be thought of as the process of taking both of these disciplines and bring them together to search for patterns in "big data". Once there are too many observations or too many variables for a single person to reasonably expect to be able to synthesize or analyze on their own in a reasonable timeframe, it's time to augment the human capability with analytics software.
Using tried and tested techniques from both the scientific process and statistics, pattern based analytics software should, at a minimum, help you with the following tasks:
- Search for patterns in your dataset - find interactions between variables in your dataset that impact other variables that can be classified as patterns
- Create hypotheses or discover rules - assert the existence of a pattern and assess its strength
- Validate a pattern's existence - make sure the pattern is valid and help figure out if it's something that can be controlled (this is tricky, regardless of what you're doing. See how analytics can get in your way.)