All right, welcome to your custom deep dive. It looks like someone's ready to tackle decision intelligence.
Ooh DII.
Based on all the stuff you sent our away, we we duck into the Decision Intelligence Handbook by well by DI experts, of course, and even pulled this Forbes article that really that really caught our eye.
Interesting.
No, tell me, have you ever felt like like you're just drowning in data? Right, but it's not really helping you make decisions?
You definitely not along there. That Forbes article we pulled really nailed it highlighted a big problem. It even cited a study that found that decision makers get this, they only use like twenty two percent of all the data insights they.
Get twenty two percent. What's even the point of all that data?
Then? Right, it's not just wasted resources either. Think about the missed opportunities. Imagine if we could really use all that data to make smarter choices.
Okay, so how do we fix that?
Then?
How do we bridge the gap between data overload and actually making good decisions?
That's where comes in. It's not about getting more data, it's about the right data used in the right way.
So it's a methodology it is.
It's a whole way of thinking that really focuses on actions and outcomes.
It's like that flight simulator idea from the handbook. Oh yeah, you're not actually flying, but you get to test out different scenarios and see what happens, right, and.
A safe environment. That's a great way to put it. DII gives you that safe space for decisions less you explore the impact of your choices before you actually set them in motion. I like that, and the key part of that is understanding how our actions lead to outcomes, which brings us to causal decision diagrams cdds or cdds for short.
The cdds were huge for me. It's like a flow chart but on steroids. Uh huh, visually mapping out cause and effect.
Yeah. But from what I understand, there's more to these diagrams than meets the eye. Different types of nodes and connections and all that interesting. So let's say you're decided on a new marketing campaign AD for that would include nodes for things like the cost of the campaign, the potential reach, customer conversion rates, and then of course the impact on revenue.
So you'd have nodes for each of those and then connections between them, showing how they all affect each other.
And those connections they're not just simple arrows. They show different types of causal relationships. Yeah, Like, there might be a strong positive connection between the reach of the campaign and the number of leads you get, makes sense, But the connection between those leads and actual sales might be more uncertain, influenced by your sales team or the overall market demand.
This visual element is so crucial it is. It helps everyone understand, from marketing to the CFO, even if they usually hate spreadsheets.
Absolutely, a CDD creates a shared understanding so everyone can see the big picture and how all the pieces fit together.
But it's not always straightforward cause and effect, right, you got it. Sometimes things have that we don't expect at all. Oh yeah, like that sweet potato farmer example.
From the book Aha the nematode saga. Yeah, it really shows how even good intentions.
Can backfire, So tell me about it.
This farmer was fighting these tiny pests that were messing up his crops, making them unsellable and hurting his profits. Ouch, So he decided to invest in a new irrigation system, hoping to get bigger yields.
Sounded like a solid plan, right, but it didn't quite.
Go as planned. The news system, while good in theory, accidentally created the perfect breeding ground for the pests. Oh no, he ended up with even more damaged crops and even lower profits.
So even with the best data and the best intentions, things can still go wrong.
Exactly.
This is where di I helps us see those hidden factors.
Those unknown unknowns that we might not even be aware of.
Right.
Instead of just focusing on what we think will happen, di I helps us explore all the possible outcomes, all of them, including the ones we might not have thought of.
It's like a pre mortem for decisions. I like that it captures what DII is all about, anticipating what could go wrong before it does and making smarter choices. Yeah, based on a deep understanding of the situation.
Okay, so we've got this powerful tool helps us see the whole picture, anticipate the unexpected, make more informed decisions. But how do we actually use it?
Right? How do we apply it in real life and the real world. The handbook had some interesting case studies, like that telecom company they were trying to figure out the best price for their unlimited data plans.
Oh yeah, they were facing tough competition, fierce, and.
They knew they had to make a data driven.
Decision, not just go with their gut.
Or copy what their competitors were doing.
This is where that sensitivity analysis comes in, right, Exactly. They could use di to simulate different pricing scenarios and see how those changes would impact their bottom line.
They could model all sorts of factors like customer churn, competitive responses, even work capacity.
So they could run virtual experiments.
Essentially, yes, to see which pricing strategies would.
Work best maximize profitability.
Right, it's not just picking a price and hoping for the best. Yeah, they could test different levers, see how they interact in a complex system.
That telecom example is great. But I'm wondering, does di I completely ignore human intuition.
That's a great question, because experience and gut feelings still play a role. Right, You're absolutely right. Di I isn't about replacing human judgment with algorithms. It's about empowering decision makers, giving them the insights.
They need to make better choices.
Because even the best models have limitations and there are always things we can't predict.
So it's not data versus intuition. No, it's finding the right balance exactly. The handbook mentioned a company trying to upgrade their phone systems in like fifty countries.
Oh yeah, it was a huge project, a.
Logistical nightmare, absolutely, even without the technical stuff.
They had all these old systems, hundreds of content, different needs in each location.
It was overwhelming, it was and.
Their first attempts failed because they just focused on the easiest.
Upgrades without seeing the big picture.
They missed those connections, the interdependencies.
That could make or break the project exactly.
That's where DII came in. They use it to create a CDD that mapped out everything, the complexities, the interdependencies, contract expirations, you name it.
So they could simulate different upgrade sequences.
And find the most efficient path.
They could see how actions in one country would impact other countries later on, anticipate bottlenecks, avoid those cascading failures.
The results were amazing, saved millions of euros, finished at as schedule.
Wow.
More importantly, DI changed their mindset. They started taking a more strategic approach.
Holistic the decision making.
It's not just finding the right answer, it's understanding the whole system, making choices aligned with the bigger goals.
Speaking of goals, one thing that stood out to me about DII was the focus on def objectives before diving into data analysis.
You have to know where you're going exactly.
Even with the best data, you'll get lost.
Like setting off on a journey with no destination.
You might end up somewhere interesting, but probably not where you want it to be.
So do I helps us define that destination and gives us the tools to map out the route.
But it also makes us confront those unknown unknowns.
Those factors we might not even be aware of.
That's why the decision artifacts are so important. The cdds, models, simulations.
All the stuff we create during the DI process.
It becomes a record of our thinking. Yeah, our assumptions are understanding.
At that moment in time, and they're not.
Just valuable for that specific decision, but for future decisions too.
Like a library of wisdom exactly. We can revisit them, see what worked, what didn't.
Maybe even spot patterns we missed before.
And continuously improve our decision, making.
A continuous improvement loop.
We're not just making one good decision, We're building a system for making better decisions overall.
That's a great way to put it is an ongoing process, the journey of discovery and.
Refinement, and a key part of that.
Is the decision retrospective.
We take the time to review what happened after a decision, extract valuable lessons regardless of the outcome, because sometimes a good decision can lead to.
A bad outcome, and vice versa.
It's about learning from our experiences exactly.
The retrospective is all about honesty. Did our process align with our objectives? Did we consider everything? Challenge our assumptions.
Because even when there's no right answer, we can still learn from the process.
Did we think about all the angles, identify potential risks, challenge our own thinking, the accountability even when things are out of our control.
Bringing us back to the human element of DII, balancing data with expertise and judgment, di I is really pushing us to be more deliberate, more thoughtful about our decisions.
It's a shift in how we think for sure, and not just for individuals. D I could change how entire organizations work.
Imagine every department using di to guide their choices.
From marketing to finance to operations.
Instead of all these separate decisions that often cause conflicts and inefficiencies.
Right, everyone would be on the same page, working.
From the same playbook, with the shared understanding of the big picture.
Shared understanding and alignment are key to really using di I effectively.
It's not just theory either.
The hambook mentioned companies already using di across the.
Board seeing real improvements.
In efficiency, profitability, even employee morale makes sense.
If people feel like they're part of the process, they'll be more engaged, more invested in the outcomes.
It creates ownership, accountability.
It sounds amazing, but DII isn't a magic solution, is it.
Of course not. There are going to be.
Challenges, especially in big organizations.
Change is always tough, especially when it comes to something like this.
People get comfortable with the old ways.
Some might see it as a threat to their power or expertise.
And there's a technical side.
Building the cdds, getting the data, creating the simulations.
It takes a lot of resources and expertise, it.
Does, but the potential payoff can be huge, like.
Any good investment, and you don't have to jump all in at once. Start small, maybe a pilot project in one department, prove the concept, show the results win over the skeptics exactly. Those case studies from the book are really helpful.
They are. They provide real examples of how DII is solving problems achieving.
Results in all sorts of industries.
A roadmap for the DI journey.
We can learn from others adapt their approaches to our own situations.
Which brings us back to you, the listener.
How can you use these ideas for a decision you're facing right now?
Could d I help you see hidden factors, test out different scenarios, approach decision making with more confidence. That's the question we want to think about.
That's it for this deep dive. It wasn't just about shit information. We wanted to give you a new way to think about decisions.
To make more informed choices in all parts of your life. We hope this deep dive sparked your curiosity and gave you some valuable tools and insights. Remember, better decision making is.
A journey, and DI can be a powerful ally.
Thanks for joining us.