Are you making these "data-driven blunders"🤦?
Hello,
You might have heard how Facebook has gone Meta. Despite Facebook’s severe shortcomings in the recent past, Mark Zuckerberg’s vision for the metaverse is hard to ignore. I got both excited and anxious about the metaverse, Web 3.0, and its implications.
The announcement and Zuckerberg’s chat with Gary Vee reminded me of Ready Player One, one of my favorite movies. If you haven’t watched it yet, I’d strongly recommend you check it out. The metaverse does look like the next logical step for the internet.
This newsletter will take you about 5 minutes to read.
I. Spotlight: Watch out! Are you making these "data-driven blunders"🤦?
Did the last news article you read reconfirm what you had suspected all along? No, this may not be a case of great intuition. You might just be another victim of confirmation bias.
What’s this bias? It’s the selective filtering and interpreting of information to confirm one’s preconceived notions. Simply put, it’s the cherry-picking of information to suit our assumptions conveniently.
This is a deadly bias we all succumb to every day. And, it is one of the most common reasons for bad business decisions.
Steve Ballmer said, “There’s no chance that the iPhone is going to get any significant market share. No chance.” With a firm belief in the steady rise of personal computers and the proven dominance of Microsoft in this space, any evidence to the contrary must have felt like an inconvenient outlier.
The Microsoft team chose the information they wanted to believe in until it was too late. They lost the opportunity to extend their dominance to the mobile ecosystem.
(Photo by Beatrice Formales on Unsplash)
But are data-driven decisions safe from confirmation bias?
No! If anything, people who embrace data suffer the worst consequences due to this bias. Let me explain.
In an ideal world, one uses data to evaluate assumptions and find new patterns. This helps them arrive at conclusions which can then lead to sound decisions.
But that’s not how most people operate. They do it in reverse.
People jump to conclusions based on their beliefs. They then look around for data to support their assumptions. That way, their faulty decisions are now backed up by data. I like to call these “data-driven blunders”!
You can convince an average individual to revisit their assumptions. It’s almost impossible to win over someone who has systematically arrived at the wrong decision.
“Some people use data analytics the way a drunk uses a lamp post - for support rather than illumination.” - Andrew Lang
So, how do you tackle confirmation bias with data science?
Never begin with the data. Start by critically examining your beliefs. Get into the mindset that your assumptions could be wrong. Seek out evidence based on your opinion but with the primary intent of disproving it.
As Sherlock Holmes would say, “Once you eliminate the impossible, whatever remains, no matter how improbable, must be the truth.”
Here are three steps to question your assumptions and eliminate the impossible:
1. Look for unusual data: We often ignore some data because we don’t value it. Or worse, we might avoid data because it makes us uncomfortable. Start collecting them.
2. Scout for unusual insights: This well-rounded data is useful only if you look for insights with an aim to disprove your beliefs. Look out for big, useful, and surprising insights.
3. Craft them into unusual stories: Insights alone don’t move people to action. You need visual stories. Make the correct conclusions leap out and hence hard for anyone to ignore.
The first step to fighting confirmation bias starts by knowing that it always lurks in the background. Be willing to question your assumptions.
Remember, data is a great sidekick but a poor boss! Use data the right way, and it can turn into a great ally in your fight against cognitive biases.
II. Industry Roundup:
1. Gartner’s Magic Quadrant for Data Science and ML is dead
2 minutes | Gartner | Carlie Idoine
The Gartner Magic Quadrant (MQ) is a clever market research tool that captures the attention of businesses and vendors alike. But the data science MQ rapidly lost its relevance over the years. The convergence of BI, ML, and other related segments made the comparison tough in a single view. The MQ’s exclusion of open-source platforms left it incomplete. Gartner is now replacing it with two market guides.
2. How AI helped fight storms and save livelihoods
11 minutes | Microsoft | Anam Ajmal
We see a surge in hurricanes worldwide, and they leave a trail of death and destruction. To protect vulnerable communities, SEEDS, a non-profit, turned to AI. With high-resolution satellite imagery, their model identifies homes that are at risk of getting damaged or flooded. This AI solution enabled SEEDS’ volunteers to move over 1000 families to safety days in advance during the recent Cyclone Yaas.
3. Article: The 4 types of decision-making with AI & humans
9 minutes | HBR | Michael Ross, James Taylor
Scaling AI in the enterprise needs a paradigm shift - from making decisions to making “decisions about decisions.” The authors call these micro-decisions and layout a 4-step framework to design such systems: Human in the loop, Human in the loop for exceptions, Human on the loop, and Human out of the loop. The article shares examples for each type along with caution on what can go wrong.
III. From My Desk:
1. Video Interview: OKRs and Data Culture
15 minutes | Gramener
What's common to Data Culture and OKRs (a popular goal-setting framework)? It turns out that pursuing one can jumpstart the other. In my latest chat with Kenneth Lewis, an OKR coach, we debate the synergies, learnings from experiments, and the role of leaders.
2. Talk: Cognitive biases in decision making
45 minutes | 500 Startups
What are the top 3 biases that stump leaders in decision-making? How can you apply the principles of decision intelligence to fight these cognitive biases? I use examples to explain the biases and the ways to avoid them.
Have you heard of ‘skimpflation’? That’s when companies skimp on their services amidst rising prices. Check out this brilliant Marketoonist cartoon!
Thank you for subscribing and reading the newsletter. I appreciate your attention,
Ganes.
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My Website | LinkedIn | Twitter | YouTube
I’m Ganes Kesari. I publish ‘Data-Driven Future’ to help understand how data shapes our world, explore key trends, and explain what they mean for you today. I speak and write to demystify data science for decision-makers and organizations.
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