Dear Advisor: How do I avoid the biggest Data/AI/ML mistakes others make?

Post was originally published in June 2020 and has been updated in August 2020, September 2020, April 2021, and April 2022 for relevancy.

You keep hearing that you need big data, the media tells you that AI can accomplish anything, and vendors tell you that things just work out-of-the-box. You don't know what you don't know -- and you don't have the data and analytics expertise. 

I've spent 10 years in the industry solving customer pain points with data and analytics to drive product market fit, and have 4 inventions of novel AI algorithms behind my belt -- inventions that brought in millions of dollars in revenue. I'm here to to tell you that developing data-products with high business impact are not trivial. And developing novel algorithms may take takes years (and a bit of luck!) to develop something that works for a very specific unique use case, when other options fail.

If you're not a data/analytics/AI expert, here are the most common recurring challenges I've seen when it comes to product scope and development of data-driven products. 

Not answering the Business Question

Missing Information -- all of the above and:

Too early for ML -- all of the above and:

2. Your MVP is under development. 

Treating ML/analytics as a Silver Bullet -- all of the above and:

Executing on ML products as if they're software engineering tasks -- all of the above and:

6. Not knowing about the Hidden Technical Debt in Machine Learning Systems

7. Not understanding what the algorithm is doing.

Not executing on (aspects of) ML products as if they're software engineering tasks -- all of the above and:

Difficulty hiring data professionals

Nobody is perfect, has clean data, or has ML running with no downtime in production. Now that you know what to focus on, start small and iterate

Do you need an expert to help you improve your product market fit and scale by leveraging data to make your customers happier? Please reach out.

Keywords: AI, ML, start-ups, data strategy, data products, customer understanding

This blog post was originally based on an office hour I hosted on the 805 Startups Discord server on June 15th, 2020. 

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