🔮 How will you make decisions in 2030? (Hint: Brace for impact!)
Clubhouse App is the latest talk of the town. I had no idea what it was when I signed up for it. A week later, I still have no idea! Well, it looks like phone calls, group chats, and webinars - all rolled into one. But, with total strangers.
Btw, I have 2 invites for the app. Invites are the only way you can join the club, for now. If you want one, drop me a note.
This newsletter will take you about 5 minutes to read.
I. Spotlight: 🔮 How will you make decisions in 2030? (Hint: Brace for impact!)
Let’s say you are the business manager of a retail chain. You’re facing the problem of customer churn.
How would you arrive at decisions to retain your customers?
Today, there are three common steps to get you from business problems to decisions:
Gather the data: Collect information about the problem and understand its impact. There’s no paucity of data sources, types, or ingestion tools. Enterprise data, public data, (unethical) private data - we have it all!
Perhaps, you know a lot more about those retail customers than their own family.
Analyze for insights: Find out what causes the problem and how it can be solved tomorrow. Predictive techniques and AI-driven simulations can model hundreds of scenarios to tell what could happen in each.
You can roll out personalized discounts to a hyper-targeted set of customers.
Story-tell recommendations: Rationalize the best option and build consensus among leaders. Visual storytelling of insights can not just convince people but move them to action.
Your story weaves in ROI from retained customers to secure your marketing budget.
(Photo: How we make decisions today)
Thanks to advances in data science, decision-making does seem easy today. Now, are you wondering if it could get any better in the future?
Yes! Let’s get into a time machine.
Decision making in the 2030s
Decision-makers in the future might look back at today as the middle ages of decision making - progressing, yet primitive.
Here’s how I think our three steps could evolve in the next 10 years:
Gather the data - Neural interface: You won’t need any device to gather or access your data. Thanks to neural interfaces, your brain can be plugged into the network. At will.
Data harvested from human memories will enrich signals (yes, ethics & privacy will be a bigger nightmare). All data will be hidden, like a low-level layer. Remember, your customers are part of this neural collective! You will get deeper, ready-made answers to why your customers churned.
Analyze for insights - Collective intelligence: Computing would have reached exceptional heights, at quantum scale. However, the big game-changer would be the addition of human brainpower into the computing mix.
The neural hive’s collective intelligence will make it possible to come up with the deepest insights at scale, thanks to device implants. Human intelligence will make it easier to blend insights into the business. The recommendations to control churn will be non-intuitive, actionable.. and yes, still not very explainable!
Storytell recommendations - Encoded impulses: Visual stories work best when we scan using our eyes and transfer to the brain. When you tell stories inside the human brain, will they still be visual? Why not encode them as say, neural impulses?
Information design will evolve to adapt to the brain-computer interface. When you have a connected hive of human minds (remember Avatar, the movie?), your recommendations to prevent churn will be effortless to understand. You think up decisions and the group nods in agreement!
(Photo: How we might make decisions in 2030)
What’s the biggest change in all of this? The complete cycle from problems-to-decisions may no longer play out in the physical world.
All of this could shift (back) inside our brain - thanks to neural interfaces, collective intelligence, and encoded impulses. How will this impact you?
What do you think? Hallucination🤯 or a stretch of the imagination😱? I’d love your unfiltered thoughts. And yes, until we all hook up to that neural hive, please tell me in an email!
II. Industry Roundup:
1. Building a winning analytics team - Lessons from Moneyball
7 minutes | Burtch Works | Kathleen Maley
What can we learn from how Billy Beane assembled his winning team at Oakland A’s? How do you get a competitive advantage with data? What promotes collaboration between business & analytics teams? This article provides the answers.
2. AI dubs voices to localize movies across languages, automatically
3 minutes | VentureBeat | Dean Takahashi
Tired of watching movies with subtitles? AI can now dub the movies in the actor’s own voices. Not surprisingly, the company is called Deepdub. This product can help expand the reach of entertainment studios.
3. The empty promise of data moats
This classic article underscores the importance of data, yet cautions that data is not a competitive moat for companies. It all boils down to how you use your data. The article shares 5 tips to capitalize on your data.
III. From my Desk:
1. Article: Your “Wrong” AI Model Can Transform Your Business
A lot of organizations obsess over model accuracy. In this article, I share 4 ways that an imperfect model can deliver great business outcomes.
2. Podcast: How can organizations get value from data?
27 minutes | Alldus ‘AI in Action’ | JP Valentine
What does it take to build an effective team that delivers business results? How we got started at Gramener and what we look for while hiring our team.
3. Post: What’s the most important thing in Data Science?
1 minute | LinkedIn
No, it’s not the data you can collect, the analytics insights you can find, or the stories you can tell. It’s asking the right business questions that can really move the needle.
-> View the Post (12,700 views)
Having trouble with your software upgrades? So did this train station in China, which went down along with Adobe Flash, as it rode off into sunset in 2020!
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