Blog: Don’t Convince Your Boss To Use Machine Learning Until You Have Done This…
“We have to do AI stuff!”
“How can we implement AI in our company to bring more profits?”
“What machine learning models can we use to solve this problem?”
Maybe you’ve heard of one of these statements or questions from your upper management side (aka your boss).
Maybe you’ve faced these questions on a daily basis with your boss asking you — as a data scientist — how to use machine learning models or how to leverage on the power of AI to bring values to the company (or make more money for the company).
Maybe you’ve not heard this before from your boss as the company still doesn’t see the needs of AI.
Or maybe you want to convince your boss to use machine learning to add values to the business.
Whatever the case it is…There are reasons why AI has become most company’s goals. And there is one thing for sure.
A study done by Statista shows that global revenues from AI for enterprise applications is projected to grow from $1.62B in 2018 to $31.2B in 2025 attaining a 52.59% CAGR in the forecast period.
With the popularity of AI, the focus on AI might be an overkill as I believe there are lots of low-hanging fruit in data science for business to pluck and relish.
“Everyone’s rushing to double down on AI when there’s still so much low-hanging fruit elsewhere.”
So many companies started their AI projects with excitement and high expectation but turned out to be another failed AI projects due to the lack of dataset, broken data pipelines etc.
And these companies may eventually think that AI is a hype and data science is nothing but a fancy department for companies in vanity.
All these are some of the major factors that stop companies from achieving successful digital transformation in this era where data is the new gold mine.
So… Before you get too excited to convince your boss to use machine learning next time, make sure you’ve already done some projects that have delivered tangible business values by tackling the low-hanging fruit first.
In the following section, I’ll briefly talk about what low-hanging fruit means and why tackling the low-hanging fruit in data science for business is important before embarking any machine learning (or AI) initiatives.
Let’s get started!
What is low-hanging fruit?
The original meaning of low-hanging fruit means the sweet, easy-to-reach fruit at the lower end of a tree’s branches.
Orchard workers and homeowners appreciate the ease with which this fruit can be picked, in contrast to the effort required to reach the fruit found higher in the tree.
In the context of business, “low-hanging fruit” refers to easy-to-accomplish tasks or easy-to-solve problems in a particular situation.
In other words, low-hanging fruit could be to automate mundane day-to-day tasks and improve employees’ efficiency to do work.
Or it could be to use the existing data from your company’s websites to obtain any meaningful and actionable insights that would otherwise be hidden.
The fact is that most companies have a lot of these low-hanging fruit (improving data cleaning and data pipeline to extracting insights from existing data etc.) which don’t need complex ML models to be solved.
The sad news is that most data scientist want to use complex ML models to solve simple problems that could be tackled using simple statistical modeling, analytics or visualization.
“Simple can be harder than complex: You have to work hard to get your thinking clean to make it simple. But it’s worth it in the end because once you get there, you can move mountains.”
— Steve Jobs
Why tackle low-hanging fruit in data science for business first before any ML/AI projects?
This is not to encourage that you should always set lower targets and goals just because they are easy to achieve.
The whole point of tackling low-hanging fruit in data science for business first before any ML/AI projects is this — Momentum and Confidence.
Momentum. By achieving big wins in tackling low-hanging fruit for business (provided the big wins help companies gain immediate benefits), your team will gain momentum to solve more difficult problems in future.
Most importantly, your boss will be able to see the tangible results and actionable insights obtained from data by just doing simple analysis without much complex ML models.
As a result, your team’s effort to convince your boss to use ML in future would be much more easier as your boss (or the company) has seen the importance of data to deliver tangible business values.
Achieve big wins in small problems first, then achieving big wins in big problems would not be too far away.
Confidence. At the end of the day — monetarily speaking — business is all about making profits. If your projects prove to be able to bring profits to the company, I believe that’ll be too damn hard for your boss to deny the importance of the projects.
Your boss might be sceptical of what data science can bring values to business at first. And this is perfectly fine and understandable.
To prove that your data science projects are able to deliver real business values, you have to boost their confidence to harness the power of data. This is where tackling low-hanging fruits comes in before doing any ML projects that would potentially take more time and resources to prove its values to the company.
Once the confidence is built, once the trust is built, it’ll be much easier for them to move to other ML/AI projects in the pursuit of digital transformation.
Thank you for reading.
I hope that by now you’ve understood what low-hanging fruit means and why tackling the low-hanging fruit in data science for business is important before embarking any machine learning (or AI) initiatives
At the end of the day, achieving low-hanging fruit in nothing but a stepping stone to solve other more complex problems (ML/AI) that would ultimately make digital transformation a success for a company.
As always, if you have any questions or comments feel free to leave your feedback below or you can always reach me on LinkedIn. Till then, see you in the next post! 😄
About the Author
Admond Lee is a Big Data Engineer at work, Data Scientist in action. He is known as one of the highly sought-after data scientists and consultants in helping start-up founders and various companies tackle their problems through business and data strategy with deep data science and industry expertise.
He has been guiding aspiring data scientists from various background to learn data science skills effectively to ultimately land a job in data science through one-to-one mentorship and career coaching.