Some thoughts of a Machine Learning Practitioner on Software Development, Management, Team Building, Startups, Python, Agile Development, Data visualization... that will distract you from your end goals by making you less efficient but are critical to manage in order to succeed. Don't forget that long time adaptation to inefficient approaches can become your enemy. Let's try to empower others by sharing knowledge & personal experiences.
Showing posts with label startups. Show all posts
Showing posts with label startups. Show all posts
Sunday, November 10, 2013
Friday, September 6, 2013
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.
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.
Monday, January 2, 2012
Why startups shouldn't wast time with Canadian Innovation Commercialization Program (CICP)
Canadian Innovation Commercialization Program (CICP) is an attractive program because as it is mentioned in its name, its goal is to help companies start commercialization of their innovations. The idea is interesting, if your innovation can be beneficial to the Canadian government, they might take the risk to be the first client and will pay for it. If the government is a client, your commercialization will most likely be way easier by eliminating the egg and chicken problem of getting the first real big client and real revenues faster.
Why this program?
The government is realizing that most product fail at the commercialisation phase. SR&ED is great support to develop innovations but their is little to support commercialisation (i.e: precarn program was closed).
But seriously why this program has been put in place?
There is a rumour that it is related to the Bombardier Aerospace and Embraer government subsidy controversy. Canadian was illegally subsidizing bombardier according to the WTO anti-subsidy policy.
CICP is a legal way to subsidize companies. If you look at the pre-qualified innovations of call 001, you will find bombardier and some other mature companies who might need less commercialisation help then other ones like startups as an example:
Why startups shouldn't waste their time with this program?
According to the selected companies, its seems that Startups shouldn't wast energy on this program and even less if they are from Québec and or have nothing to do with aerospace and military related projects and are startups. If they do so they will most likely be rejected by something like "The bidder’s company does not have the appropriate management team required to move the proposed innovation into commercial markets" which will bring you back the the chicken and egg problem. Freemium is definitely a way better alternative.
Saturday, October 15, 2011
IP strategy for tech startups
Most VC & angels want IP in order to consider investing in your startups.
Why: seems a security illusion. If they were really thinking about it, they will ask for industrial secret most of the time. Let me explain:
First, look at some facts:
- 95% of patents are useless (lawyer expensive fees & precious time)
- Patents are most of the time weapons of litigation which result in licences exchanges agreements (why google bought Motorola mobility)
- 50% of patents are invalidated in litigation (thanks David)...so you need a portfolio of patents if you really want to play in that yard
- It takes on average 6 years after patent deposit before it is validated....which means your competitors can copy you easily for many years because your invention will be public ;)..
- You need deep pockets to enforce your patent rights, only big player can play that game
So, if you aren't thinking of been acquired by a very big player or founded by a very deep pocket VC, you are wasting your money, time and are helping your competitors by giving them all your secret recipes and even better, you are paying for it. Isn't that the most inefficient thing you can do?
Btw, you should know that filing is making a deal with government so don't expect efficiency. Last, lawyers are relevant in ambiguity which might explain the patent process.
Subscribe to:
Posts (Atom)

