Introducing algorithm-driven decision making - a paradigm shift in business strategy
Introducing algorithm-driven decision making - a paradigm shift in business strategy
"Data-driven decision-making" has become such a prevalent concept that it's often misunderstood or poorly implemented.
Many businesses, overwhelmed by the idea, choose not to adopt it rather than figure out how to do it effectively.
But when we look at great companies, we see that their success isn't just about being "data-driven." It's about something more fundamental: their focus on processes and algorithms (in the sense of a series of steps, not the computerized version of it).
So, instead of wrestling with the baggage that comes with "data-driven decision-making," let's introduce a more precise concept: algorithm-driven decision-making.
What's Wrong with Data-Driven Decision-Making?
By definition, data-driven decision-making is "an approach to making business decisions that rely heavily on data and analysis rather than solely on intuition or personal experience."
However, this definition leads to two major misconceptions:
Data must come first before decision-making.
Data equals numbers, often leading to an overreliance on dashboards and BI tools.
Defining Algorithm-Driven Decision-Making
Algorithm-driven decision-making is an approach to business decisions that uses explicitly defined and potentially automated algorithms to drive most or all decisions.
This doesn't mean AI runs your business. It means you document your thinking processes and, step by step, transform them into mostly automatic processes fueled by data.
Levels of Algorithm-Driven Decision-Making
Write it down - nothing more; just write down a decision (or have data systems write it down).
Automate parts of it. Have a computer assistant.
Create automatic decision systems (or parts of them).
Level 3 is what most modern algorithmic trading hedge funds do. It’s what Amazon does when it preships items to warehouses that are closer to you, even though you haven’t ordered any of those items, just based on good predictive models.
It’s also a simple version of the AI overlord ruling us all and scary for most of us.
And it is not where most of the differential value of these two approaches lies! Most value is added in levels 1 and 2, not in 3. 3 is just about scale, and 1 and 2 are about creating value.
The Algorithm-Driven Advantage
By putting algorithms at the forefront, you:
Prioritize the goal of the algorithm (because you can't have an algorithm without a goal).
Design the algorithm second.
Determine the necessary data input last.
Consider Ray Dalio's approach - founder of the biggest hedge fund in the world. He didn't start with, "I have a huge amount of data and want to do machine learning." Instead, he began with his business goals and investments, gradually automating decision after decision until he arrived at machine learning and big data.
This sequence change matters enormously.
Benefits of Algorithm-Driven Decision-Making
Accelerated Growth: Algorithms are inherently analyzable, leading to faster improvements. As Jeff Bezos said, "Amazon's success is a function of the number of experiments we can run."
Automatic Goal Focus: Every algorithm has a defined purpose, ensuring alignment with overall objectives.
Enhanced Scalability: Bridgewater Associates' success is a testament to this benefit.
Improved Decision Quality: As you progress through the three levels, decisions become more information-based and less emotional.
Real-World Success Stories
Amazon: From day one, Bezos analyzed data to make decisions, continuously turning insights into algorithms. This approach has contributed to Amazon's staggering 27.7% average annual revenue growth over 20 years.
Bridgewater Associates: Ray Dalio wrote down every investment rule from the start, automating them over time. This systematic approach helped Bridgewater grow to manage $140 billion in assets as of 2021.
UPS: Their ORION system, an algorithm-driven approach to route planning, saves about 100 million miles driven annually, reducing fuel consumption by 10 million gallons and cutting costs by $300-$400 million per year.
Implementing Algorithm-Driven Decision-Making
Start by writing down your decision processes for one key area of your business. Analyze the outcomes and learn from them! Writing down decision processes is weird, trust me, but the learnings will be big.
Look for opportunities to partially automate these processes.
Gradually move towards fully automated decisions where appropriate.
Remember, the goal isn't to replace human judgment entirely but to create more consistent, scalable, and effective decision-making systems.
The Algorithm-Driven Future
By shifting focus from data to algorithms, businesses can create more robust, scalable, and effective decision-making systems. This approach doesn't discard data – it puts data in its proper place as an input to well-defined decision processes.
Are you ready to revolutionize your decision-making approach? Start by examining your most crucial business decisions and turn them into explicit algorithms. The results might surprise you.
Generated thanks to the MemeAlchemist.com.
Want to discuss this approach further? Reach out! I'd love to advance my thinking on this topic and hear about your experiences implementing algorithm-driven decision-making in your business.