AI isn’t the solution for everything!
Most of the time, you don’t need AI AT ALL!
AI is merely one of the technical solutions to tackle our problems.
Problems happened not just because of lack of certain technology. A lot of times, it’s about human cooperation, mindsets, skills, process, governance, or lack of clear leadership... etc.
The very first step before you jump into the question how should I solve the problem: you should always spend a good amount of time on understanding your problem.
You need to ask yourself and the key people around you:
Are you solving the right problem?
What’s the root cause of that problem?
Then, after you have clearer picture about what’s really going wrong, then we can think about what could be the best and most sustainable way to tackle it.
Action!Action!Action! - the secret key of value generating data analytics
To create real value by using AI and big data in your decision making, the key is that you should always start with an Action hypothesis.
Instead of asking: what can we know from these datasets? You should ask: what alternatives do I have, what information do I need to have to make my choice, and what would be the consequences?
Why many data science projects fail? PART III
Collected data usually have serious quality problems.
Data science is by-product of usual business operations. Business operations are often messy. Building new projects on weak foundations is extra hard.
I don’t know shortcuts here. Companies should embrace analytical culture early on in the journey. Strong data foundation will maximise chances to excel in data science.