Before you take a deep dive into a technology course or a PhD, do you understand the problems that you would like to solve?Do you want to build ovens or bake bread?You Don’t Need AIContrary to popular belief, AI is not a magic bullet.
In Harvard Business Review’s July, 2017 cover story, The Business of Artificial Intelligence Facebook’s AI boss, Joaquin Candela (speaking of Unicorns) vents his frustrations on this point:“What frustrates me,” he says, “is that everybody knows what a statistician is and what a data analyst can do.
If I want to know ‘Hey, what age segment behaves in what way?’ I get the data analyst.
“So when people skip that, and they come to us and say, ‘Hey, give me a machine learning algorithm that will do what we do,’ I’m like, ‘What is it that I look like? What problem are you trying to solve? What’s your goal? What are the trade-offs?’” Sometimes they’re surprised that there are trade-offs.
“If that person doesn’t have answers to those questions, I’m thinking, ‘What the hell are you thinking AI is?’”They are thinking it’s magic.
“But it’s not.
That’s the part where I tell people, ‘You don’t need machine learning.
You need to build a data science team that helps you think through a problem and apply the human litmus test.
Sit with them.
Look at your data.
If you can’t tell what’s going on, if you don’t have any intuition, if you can’t build a very simple, rule-based system — like, Hey, if a person is younger than 20 and living in this geography, then do this thing — if you can’t do that, then I’m extremely nervous even talking about throwing AI at your problem.
Candela’s insights are incredibly poignant in our current obsession with all things AI and ML.
The hard truth is this: you probably don’t need AI — at least not yet anyway.
Before you can get to true machine learning that’s actually having an impact on your business, you need to have a very specific, well defined business problem.
In my personal experience, most businesses haven’t defined the problem well enough to even apply a set of simple rules to it.
Candela emphatically states — if you haven’t even gotten that far how can we even begin discussing applying AI? The bottom-line is, we can’t.
Don’t Do Data Science, Solve Business ProblemsForget about Data Science for a minute and make a conceited effort to unravel problems and make plans on how to solve them.
If you do this, a funny thing will happen — the technology/algorithm/or technique you need to apply will make itself apparent.
You’ll become good or even expert at it because you won’t just be hacking or calculating, you’ll be solving a real, practical problem.
Here’s a few suggestions for Data Scientists or those on Analytics teams to apply this idea more fully:Become a scientist of the business.
Spend a little bit less time learning new algorithms and Python packages and more time learning the levers that make your specific business go up or down and the variables that impact those levers.
Identify data sources contributing to those variables — usually at the intersection you will find high value opportunities.
Be ruthless in prioritizing and accepting projects.
Prior to moving forward on a DS project, evaluate 1) The action that will be taken with the output and 2) the business value that will be created based on that action.
If both the action isn’t clear and the value is not high, don’t waste your time.
Side note: Data Science is NOT Business Intelligence, BI is an important IT function that maintains the integrity of data sources and dashboards — your job as a Data Scientist is to solve problems in the business.
Don’t expect stakeholders to always (or ever) be able to define the problem.
In my opinion, this is the number one most important skill for a Data Scientist above any technical expertise — the ability to clearly evaluate and define a problem.
Most business stakeholders have problems but haven’t thought about them long enough to be able to define the process behind them.
This is the place where you will make Machine Learning and AI work for your organization — by deciphering the needs of the business into a process where Data Science can be applied effectively.
Make yourself part of the business.
Do not under any circumstances become siloed.
Proactively get involved with the business unit as a partner, not a support function.
Good lucking solving problems.