Attracting, evaluating & criteria about Investors

How to attract good investors?

Apply the core ninja principal:

If you go out looking for cash….
You’ll find people who tell you how to improve your business….

If you go out looking for people to improve your business (real entrepreneurs)....
You will find cash! (and you will have people to help you improve your business too...cool huh?!)

How to evaluate an investor?

The real value of an investor is inversely proportional to how he will try to take advantage of you.
Basically, the more he will try to screw you, the lowest is its value. … apply the ninja rule...

What to look for in an investor?

1- Network

2- Experience

3- Money

How to select your investor?

1- Identify your need

2- Identify the best people who can help (use the beachhead model)

3- Request help (apply the ninja principle)

A simple way to identify outliers and focus on clusters

A simple way to identify outliers is to apply a 2D dimension reduction and click on the outliers to retrieve the original record as follow.

This feature is now integrated by default in mlboost. 

Now, can you zoom/focus on specific clusters? Now you can if some tagged data by excluding classes (-e) or limiting classes to specific ones (-o) like this. 

Don't have to much fun and don't forget you can specify the dimension reduction transformation used.

The big Data Dead Valley Dillemma

Here is the dump of my perception about Big Data (presented in BigData MTL 7). Basically the Big Data Dead Valley Dilemma.
Basically, where will Big Data real applications are most likely going to happen.
First, to do Big Data, your organization need be be technology mature (big limitation #1). The level of maturity required is mostly available in start-ups or big enterprises. SMB aren't considered because they have no big data problems, no long term vision and little technology maturity in general.
Second, you have to consider the risk of been able to do big data for real (big limitation #2). Most startups don't reach big data due to: founding issue, data issue (not enough) and data availability (privacy constraint).
Once you have the data (enterprise), you are stuck with IT constraints (fear of outsourcing, politics, incompetencies) and data quality (unusable data due to limited integrated QA).
Basically, big data is a dead valley. Big Data is a marketing carrot to attract SMB to invest indefinitely in project that will most likely fail, a failing project will generate more revenue to big data consultant firms. The 2 main places where big data will succeed are in Enterprise like google, amazon, yahoo, nuance and successful startups like facebook, linkedin, twitter. BigData is not a big market, there is too much barrier of entry and the benefits will be destroyed by the complexity of the integration cost. Big data can't succeed if one part of the chain is broken which is the case in 99.9% of the cases.