Why model accuracy is overrated and how to convince your manager
For more than two decades, Netflix has been obsessed with machine learning models.
In 2006, the company announced a million-dollar prize to anyone who could improve its recommendation algorithm’s accuracy by 10%. Over 40,000 teams participated in the global challenge.
The competition ran for three years, and only two teams managed to exceed the accuracy threshold. Netflix rewarded a winner that delivered a 10.06% accuracy improvement.
But, they dumped the winning algorithm.
Despite its stellar accuracy, the engineering costs and complexity of this algorithm were very high — too high for its accuracy improvement. Instead, Netflix used a lower-ranked —…
Why do so many AI Projects fail, and how can Leaders avoid this?
How do most organizations begin their Artificial Intelligence (AI) journey?
Let’s look at how leaders of some large enterprises planned their foray into AI. Here are a couple of recent examples from McKinsey:
- The leader of a large organization spent two years and hundreds of millions of dollars on a company-wide data-cleansing initiative. The intent was to have one data meta-model before starting any AI initiative.
- The CEO of a large financial services firm hired 1,000 data scientists, each at an average cost of $250K, to unleash AI’s power.
And here’s an example that I witnessed first-hand.
- The CEO of…
Decision Intelligence is the Missing link in most Projects
Digital transformation is the flavor of the season. Every company has accelerated its efforts to digitize operations, gather intelligence, and rapidly respond to a changing market.
McKinsey senior partner Kate Smaje says that organizations are now accomplishing in 10 days what used to take them 10 months. With data powering better and faster decisions, she says, the road to recovery is paved with data.
As a result, most organizations are trying to adopt data-driven decision-making. They are hiring data scientists, buying the best tools, and greenlighting big-bang analytics projects.
Doubling down on data will not improve your business decisions.
Three steps to transformational communication with data
In 2012, Disney invested $350 million in a movie that seemed to have all the elements of a box office success. It was action-packed and had stunning visual effects. It was helmed by a star writer and the director of “Finding Nemo.”
When things looked very optimistic, they roped in Black Swan, a UK-based artificial intelligence (AI) firm, to predict whether the movie would be a hit. The firm’s AI algorithm warned that the movie would flop. Disney ignored it and went ahead with their release plans.
The movie, “John Carter,” tanked at the box office with estimated losses of…
I’ve been writing and speaking about data science and business applications of AI. In the pandemic year that was spent largely indoors, I spent more time writing and shooting videos at home.
As part of my yearly reflections, I took stock of my writing across channels. I had published 14 articles, 13 newsletter editions, and 274 LinkedIn posts.
I published my articles across 8 outlets such as Forbes, Entrepreneur, TechCrunch, and The Enterprisers Project.
I am grateful to the publications, the editors and everyone who helped me share the ideas. …
Here’s the listing of the 274 Data Science posts I made across 12 categories
Through 2020, I posted 5 days every week, on average. This came up to a total of 274 posts for the entire year. 😀
As part of my year-end reflection ritual, I relooked at my LinkedIn activity. I wanted to understand the past year and do some ‘analysis’ to find where I can improve. Talk about eating your own dog food! 🤓
Some quick stats: These posts were viewed over 780,000 times in all. On average, each post was viewed 2844 times, liked 36 times, and sparked 6 conversation comments. 📈
I’ll be making a separate post with my findings.
Great movies, speeches, and business presentations all share one thing: an emotional arc. How can it spice up your story?
Update: You can read this article in Ukrainian (Thanks Katerina Moshkola!)
You’re launching a digital transformation initiative in the middle of the ongoing pandemic. You are pretty excited about this big-ticket investment, which has the potential to solve remote-work challenges that your organization faces.
However, the executive board’s response to your presentation is lukewarm at best. You discover that business teams don’t share your excitement. Your technology team, burdened with multiple priorities, worries that this could lead to more work stress.
Don’t we often find ourselves in situations like these?
Even the best-laid plans falter due to ineffective communication. Industry…
COVID-19 has impacted every business. Here’s how data can help you come out stronger and thrive
The spread of coronavirus is delivering a massive blow to the global economy. The lockdown and work from home restrictions have forced thousands of startups to halt expansion plans, cancel services, and announce layoffs.
The virus is also having an impact on startup funding and deal activity, with seed-stage deals taking a serious blow this quarter. It’s clear that the startup community is now facing and will continue to confront an existential crisis in the months to come.
To stay in business, founders are looking for ways to maintain liquidity, better understand their demand-supply situation, identify operational efficiencies with a…
The pandemic isn’t over yet. How can you safely welcome employees back to the workplace?
Just imagine your first day back to the office after months of isolation: Not only are you potentially exposed to the virus on your morning commute, but you’re then presented with crowded elevators.
As you enter the floor, you notice door handles that have likely been touched by dozens of others right before you, and confined workspaces that make it too easy to breach social distancing protocols. It’s hardly a situation that would put your mind at ease, let alone one that would help you to get back into the swing of working in the office.
Learn about these biggest Challenges in the Data Science industry to avoid stagnation in your Career
Update: You can read this article in Japanese (Thanks Koki Yoshimoto!)
The challenges people encounter in a data science career are far more serious than the ones they face while getting into it.
Often, there is a big mismatch between job expectations and actual responsibilities. If you’re lucky to work in areas you aspire to, collaborating with other roles in a data science project can be a real struggle.
It might be easier to get your tooth extracted than to cope up with daily demands from your project manager. Get all of this right, and you might discover that your…
Ganes Kesari
Co-founder & Chief Decision Scientist @Gramener | TEDx Speaker | Contributor to Forbes, Entrepreneur | Publishing “Our data-driven future” gkesari.substack.com
