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Notes From Industry

The analytics leader of a US-based Fortune 200 company was under severe pressure. Her team supported 45,000 employees of the global energy company, and the business users weren’t happy. The analytics deliverables were often late and suffered from poor quality.

The analytics team was a part of the IT organization and was struggling to fill their open positions. The skills needed couldn’t be found within the IT team. Their office was a 60-mile drive up north from a large metropolitan area in the US, and it wasn’t easy to attract talent.

Training the few people they managed to hire wasn’t…

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How would you define ‘data science’? How about big data, AI, or data culture? These are just a few of the many jargons that are commonly tossed around in data speak. You might wonder what people really mean when they use one of these cool buzz words.

Just as data went mainstream, so has a dense vocabulary of jargon. It’s a real challenge to understand and agree on the definitions of these data terminologies. Perhaps, this is a tougher roadblock than even getting business value from data!

This article will demystify the 26 most frequently used terms in the data…

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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 —…

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Update: You can read this article in Japanese (Thanks to Koki Yoshimoto!)

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…

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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.


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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…

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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. …

Word cloud created using all of my 2020 LinkedIn posts’ content

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.

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Update: You can read this article in Ukrainian (Thanks to 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…

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…

Ganes Kesari

Co-founder & Chief Decision Scientist @Gramener | TEDx Speaker | Contributor to Forbes, Entrepreneur | Publishing “Our data-driven future”

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