It is often seen that projects often overshoot their normal completion data by at least three times most probably owing to shifting goals, inefficient approaches towards data collection and exploring various solution paths among others. A closer scrutiny often reveals that the delay was avoidable had there been a more disciplined decision making in place. To put it in a nutshell, there are three major principles which closely followed have reduced the entire project completion time to a considerable extent without any need for sacrificing the end results. These three principles are as follows:

**Fail Fast****40/70 Rule****Fermi Estimation**

However, before the three principles are reviewed it is important to define the overall objective in its relevant context. The way you define a problem and an objective has strong bearings on how you set the bar and the performance metrics that you use. It is of paramount importance that there is a close alignment with your problem, goals, and data metric.

It is important to quickly and accurately judge the viability of your problem-solving approach. Failing fast is a methodology that is used to determine the viability of an idea after the initial testing is done. For example, setting an initial goal and trying an approach X can let you know the feasibility of the solution. If there are any doubts, you can try other approaches.

It is often seen that people, in order to mitigate risk to the greatest extent possible, try to gather as much information as possible before making a decision. This may make them feel better, but it is no guarantor of the right outcome in the long run. Colin Powel theorized that leaders should make decisions with no less than 40% and no more than 70% of the information. Any decision with less than 40 percent data at your command can be considered hasty whereas anything above 70 implies that you have wasted too much time collecting information before making a decision.

Fermi estimation is using approximations to get a “good enough” answer to a complicated problem without wasting too much time and other precious resources.

**Data science course** from an established online training institute can equip you with knowledge and skills that could help you solve complex data science problems. Relevant knowledge and efficiency as such can help you solve problems keeping the bigger picture in mind.

© 2019 Data Science Central ® Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Statistics -- New Foundations, Toolbox, and Machine Learning Recipes
- Book: Classification and Regression In a Weekend - With Python
- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of Data Science Central to add comments!

Join Data Science Central