Author: Meta S. Brown
Media Source: Forbes
Originally published: 05/02/2016
Using SMART Big Data, Analytics and Metrics to Make Better Decisions and Improve Performance, predicts that “half of all big data projects will fail to deliver against their expectations.” Michael Schrage, a research fellow at MIT Sloan School’s Center for Digital Business, speaks of frustration that analytics investments are not yielding expected results.
Positive returns on analytics investment require management action. But many managers are reluctant to take action based on analytics, especially when the numbers don’t seem to match with their own gut understanding. They don’t know much about analytics and don’t really trust the process. Why trust something you don’t understand?
London-based data mining expert Tom Khabaza offers some help, in the form of a simple way to explain important analytics concepts. His “9 Laws of Data Mining” are widely accepted in the analytics community, and not only by those who see themselves as data miners. Duncan Ross, Data and Analytics Director of TES Global, states emphatically that “The 9 Laws of Data Mining are equally relevant to data science.”
As Khabaza puts it, “The 9 laws are not rules you must follow, they are descriptive statements that are always true.” And they are simple enough for analytics novices to understand. The 9 Laws help business managers better understand the analytics process, what it can and cannot do. (Khabaza was one of the first in the profession, and is an active data mining consultant today. He was also technical editor for my book, Data Mining for Dummies.)
Here, briefly explained, are Khabaza’s 9 Laws of Data Mining:
- Business objectives are the origin of every data mining solution: If you don’t know what problem you’re trying to solve, you probably won’t solve it.
- Business knowledge is central to every step of the data mining process: If you don’t have someone who knows the business on the team, you won’t get good results.
- Data preparation is more than half of every data mining process: Analytics isn’t always pretty. Most of the time and effort goes into the dirty work of cleaning data and getting it in shape for analysis.
- The right model for a given application can only be discovered by experiment: In business applications, it takes a lot of trial and error to find predictive methods that work for you. (This is different from classic scientific research processes.)
- There are always patterns: In practice, your data always holds useful information to support decision-making and action.
- Data mining amplifies perception in the business domain: Do the analysis and you’ll know and understand more than you did before.
- Prediction increases information locally by generalization: Good analytics processes provide useful predictions and a better understanding of what’s likely to happen in specific business situations.
- The value of data mining results is not determined by the accuracy or stability of predictive models: Judge results by the value they yield for the business, not by the mathematical details.
- All patterns are subject to change: What works today may not work tomorrow. You’ve got to keep investigating.
Positive returns on analytics investment are a realistic expectation when you begin with the right plan. You’ve got to understand the process, and that includes understanding that analytics is a down-to-earth, nitty gritty process that only produces results when you use the results to drive action.
Meta S. Brown is author of Data Mining for Dummies and creator of the Storytelling for Data Analysts and Storytelling for Tech workshops.
This article was first published on forbes.com. Republished with permission via Medialounge.
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