Plato Elevate Winter Summit has been announced (Dec 7th-8th)

🔥

Back to resources

AI Products Are Data Products

Product
Impact

19 October, 2020

Deepak Paramanand
Deepak Paramanand

Product Lead at Hitachi Technologies

Deepak Paramanand, Product Lead at Hitachi, shares how he built three different AI projects that all had one thing in common -- the ability to create or input data.

Problem

I started off as a SQL developer designing and creating databases. I would pull data into a spreadsheet and analyze it trying to reduce the cost and increase the revenue for the company. Understanding the impact and significance of the smart use of data led me to my first AI job.

Below, I will share three different AI projects that I worked on and that reveal the immense potential AI products have. What I came to realize is that AI products are essentially data products. If you don’t have data in your product you can’t build an AI product. That is, if you have no means to create or input data, you can’t build AI into your product.

Actions taken

I decided to single out three AI products I was working on -- one on structured data, two on unstructured data -- to highlight the immense potential of AI products and the main difficulties that come along with building them.

My first AI product was a telecom product. I had data about every call a person made for the last two years. Based on their calling behavior I had to predict who would cancel their subscription and who wouldn’t and what we could do to keep them engaged. For example, it would cost the company 5 dollars to make them customers and it could make 50 cents from them every month. That meant that a customer had to be with the company for at least 10 months to make up for the costs of acquiring them. If the company couldn’t make customers stay for two years, it could perhaps increase the revenue from them. I had to understand if and why a person would pay 2 dollars instead of 50 cents and how could I build a product out of it. I built a product based on structured data, machine learning, and logistic regression. I used data to do AI and use the output of the AI to build an AI product. I helped the company understand what was the campaign that made a customer stay (100 more minutes a month or a new phone at 50 percent less price) and how it could incentivize and retain customers.

The second product I built was based on unstructured data and was using text data and natural language processing techniques. The company I was working at was helping attorneys who would go through financial statements and make an assessment if a company was worth acquiring. To make that assessment attorneys would have to read thousands and thousands of documents. If one person would do it alone it would take them eight months and six people could do it for two months. They would read the documents, highlight the import parts, summarize that in a separate document, and repeat the process all over again. We proposed to scan the documents and present them in a readable format. I automated the process using AI and writing the code in Python, but also created a new business model. We offered attorneys to pay us only 10 percent of the money our product would save them. Not only that I built a new product for the company but I generated a new revenue stream.

The third AI product that I want to single out is an app that allows customers to create facial expressions and voice messages that resemble them. We had to teach software to recognize human emotions and expressions (winking, nodding, etc) and had to record people doing funny expressions, recognize their faces, and differentiate between genuine and fake smiles, for example. The product had to be ethical and include minorities and we didn’t have much data on minorities. Not only that building AI part itself was hard but curtailing our biases in assuming one ethnicity, gender, or age added to the problem. That included also modeling for all possible combinations of hair color, mascara, nose piercing, etc. and collecting all those data was impossible. In addition, I had to expand my knowledge on race and ethnicity, bias, and inclusion and it was entirely a new product experience for me.

Lessons learned

  • All AI projects start with data. If you don’t have data you can’t do AI.
  • Structured data is the best form of data to start your AI journey with. It’s well understood, very well documented and can work with unsophisticated AI algorithms.
  • Between lots of data and complicated algorithms choose the former. What happened in the past is a far better indicator of the future than complicated mathematical approximations.
  • If you cant get your hands on structured data try text data. We read and write every day and understand and applying AI to this data is quite intuitive to us.

Discover Plato

Scale your coaching effort for your engineering and product teams
Develop yourself to become a stronger engineering / product leader


Related stories

How to Pivot a Product Idea at the Right Time

23 November

Adi Purwanto Sujarwadi, VP of Product at Evermos, shares how he diligently managed a product in one of the biggest eCommerce companies by being an individual contributor.

Innovation / Experiment
Product Team
Product
Embracing Failures
Adi Purwanto Sujarwadi

Adi Purwanto Sujarwadi

VP of Product at Evermos

Overcoming imposter syndrome through focusing on your strengths

19 November

James Engelbert, Head of Product at BT, recalls when he had to battle imposter syndrome when managing a new team.

Product Team
Product
Health / Stress / Burn-Out
James Engelbert

James Engelbert

Head of Product at BT

The Right Way to Ship Features in a Startup

11 November

Matt Anger, Senior Staff Engineer at DoorDash, shares how he took the risk and shipped features in a startup.

Alignment
Product
Dev Processes
Matt Anger

Matt Anger

Senior Staff Engineer at DoorDash

How Data-Driven Products Help Customers and Increase Sales

11 November

Richard Maraschi, VP of Data Products & Insights at WarnerMedia, shares his insight on incorporating data science, AI, and product management to overcome slowing growth of the company.

Product
Conflict Solving
Users
Data Team
Performance
Richard Maraschi

Richard Maraschi

VP Data Product Management at WarnerMedia

How to Scale Product Teams for Empowerment & Impact?

5 November

Prasad Gupte, Director of Product at Babbel, shares his insights into the challenges behind successfully growing a team.

Customers
Product
Scaling Team
Strategy
Users
Prasad Gupte

Prasad Gupte

Director of Product at Babbel

You're a great engineer.
Become a great engineering leader.

Plato (platohq.com) is the world's biggest mentorship platform for engineering managers & product managers. We've curated a community of mentors who are the tech industry's best engineering & product leaders from companies like Facebook, Lyft, Slack, Airbnb, Gusto, and more.