The ten most important things to improve complex decisions
If you’re facing a high-value decision in a complex environment, and you want to collaborate with your colleagues to maximize your chance of reaching your business objectives, here are the ten most important things to do:
[bctt tweet=”To make good decisions based on data, start by setting the data aside. Really.”]
- Set the data aside. That’s right. Ask your colleagues to ignore their spreadsheets, reports, big data, and more, for a little while. It’s going to get in the way of clear thinking at the beginning.
- Draw everything. A visual picture of how decisions lead to outcomes will make every one smarter than if it’s all in text (or, worse, just invisible words “in the air”).
- Agree to outcomes. This is harder than it sounds. What are you trying to achieve? What will you measure it to know if you have?
- Watch out for proxy outcomes. These are measurements that are easy to make, but don’t quite capture the true business objectives. A good example is “customer experience”. If we were really trying to maximize it, we’d give all of our customers a thousand dollars. When you find a proxy, draw a picture showing how it connects to the true outcome it achieves (e.g. more customers).
- Follow the “why chain”. If you think you’ve identified an outcome, check if it’s a proxy by asking “why”. Keep following the chain, drawing it as boxes and arrows or however it looks natural to you, until you can’t go any further. For a business, the usual outcome is net revenue in some year. For a sustainable business, it’s a triple bottom line of financial, social, and environmental outcomes.
- If someone names a non-outcome while talking about outcomes, write it down for later and return to outcomes. This will always happen.
- Using brainstorming techniques to ask “are there any other outcomes we’re trying to achieve?” Allow the bad ideas as well as the good to open up “divergent thinking” on your team.
- Check your outcomes that they are measurable. A good question to ask is “if you and me were to bet on whether we’d achieved this outcome or not, are we clear on how we’d determine the winner?”
- Use decision engineering techniques to model the remaining parts of the system: levers, intermediates, externals, and dependencies.
- Publish your outcomes visibly throughout your organization. Your employees make 10,000 decisions a day at work. Every one should be aligned
You’ll notice that most of the above list is around understanding your outcomes. This is because data and the mechanism for achieving those outcomes are of secondary importance: they’ll always be in service of the outcomes.
“If you don’t know where you’re going, any road will get you there”. – Lewis Carroll
Computer scientist Dr. Lorien Pratt is one of the pioneers of artificial intelligence and is credited with inventing transfer learning. Following a 36-year career delivering applied AI systems, along with leadership and program committee responsibilities at the industry-leading NeurIPS conference, Pratt is recognized alongside Marie Curie and others by the Women Innovators and Inventors Project. Today, Pratt is chief scientist at www.quantellia.com, where she continues to push the boundaries of technology as a creator of and evangelist for Decision Intelligence (DI), a cofounder of www.opendi.org, and through building Large Language Models (LLMs) integrated with DI and ML. Pratt’s The Decision Intelligence Handbook (O’Reilly, 2023) is receiving worldwide attention. With recent publications in Foresight, Futures, and Frontiers, Pratt's DI innovations connect AI to human decision making in a way that is transparent, auditable, and accessible, bringing this important technology to the masses, and reducing information inequality.
