Is it unscientific to be data-driven?
How to make more scientific decisions
Let’s talk about science
In theory, data-driven decision-making is the most rational, scientific approach to decision-making.
In practice, however, it has become unscientific.
The scientific method is:
Asking questions
Forming hypotheses
Experimenting
Analyzing the results
Forming new hypotheses or drawing a conclusion
*Throughout the process, data is collected to inform the analysis and conclusion.
The prevailing process for organizational decision-making today has instead become:
Asking a question
Forming hypotheses
Gathering data from past experiments and experiments others have conducted (competitive analysis, target addressable market, etc.)
Analyzing the results
Drawing a conclusion (the investment decision)
Experimenting—but rather than calling it an experiment, it’s tied to specific metrics that impact the careers of the team assigned to execute the “ROI-proven-in-advance initiative.”
Expertise & Reason > Numbers
In a data-driven context, the most basic element is a set of numbers, not human reasoning or expertise. The person or team forming the proposal only serves in a data-gathering and computational capacity. The decision is then based on the mathematics, and not the opinions or reasoning of the people who researched the options or created the proposal.
In practice, many leaders will pause after reviewing the numbers and ask their team members what they think. If they agree with the team’s logic, but do not feel that the logic is substantiated by data in the proposal, they may ask their teams to update the proposal in specific ways they feel they will be able to justify to their leadership, but the process itself does not support decisions that must be justified based on something other than data.
Leaders, especially those in pursuit of innovation, have struggled against this paradigm for decades. In a surprising twist, investments in artificial intelligence, coming in at an 87% failure rate, have shined a spotlight on an important truth for leaders today: this process is not only unscientific, it’s not working, and it’s time for a new approach—one that is more scientific and rehumanizes decision-making—fit-for-purpose in the 21st Century.
Organizational Empiricism
Empiricism is a philosophical theory that all knowledge originates in experience. Organizations around the world, in the process of embedding data-driven principles into every aspect of decision-making, have developed a form of organizational empiricism, in which the only way a new product, service line, or initiative can be funded is if it the future value can be empirically proven.
But there’s no data about the future.
This is a logical fallacy, as you cannot have empirical proof of something that has yet to occur, and it is self-preserving in the case that the initiative is not successful. The leader can justify that he or she approved the initiative based on the empirically-sound proposal, and the logical end is that the blame lay on the person or team who formed the proposal, either through failure to adequately gather and analyze the necessary data or failure in execution.
Leaders who only understand and are able to reward data-driven decisions will curate their organizations to optimize their existing core value propositions. Data-driven decision-making is a foundational capability for any effective leader or manager, but it must be extended and augmented with reason.
Organizational Reasoning (From Data-Driven to Reason-Driven)
Reason-driven decision-making (or Organizational Reasoning) places human reasoning at the top of the hierarchy of decision-making, on the foundation of data-driven methodologies.
The Reason-Driven Framework, which I developed for Chapter 15 in my book, Autonomous Transformation, creates a foundation on which creative discussion can be bridged with rigor and documentation, and pivots the conversation from solely discussing the validity of conclusions to a more scientific process, accounting for the unknowable in the documentation of theories and hypotheses.
Forming strategies through this approach rehumanizes decision-making by bringing others into the entire thread of reasoning, which is much more difficult to do verbally than visually, and can be shared with stakeholders and team members to generate consensus, which will improve the likelihood of positive collaboration and follow-through.
This changes the conversation. When someone disagrees with an investment you want to make, instead of going back and proving them right or wrong with numbers, the Reason-Driven Framework enables you to ask: “What line of reasoning do you disagree with? The future we’re trying to create? The theories of what would have to be true?”
They would need to refute the whole line of reasoning, and if they can’t, the only logical option is to brainstorm a new hypothesis with you or propose another way to overcome whatever obstacles they raised.
This presents a foundation for a new bridge between the centuries of reasoning theory and methodological development and the practical environment of organizations, creating a dynamic representation of the organization’s strategy, assumptions, knowns, and unknowns as the organization progresses boldly into a future driven by reason.
Imagine joining an organization today: "These are three initiatives we are working on, these are the targeted outcomes, the next milestones, and your workstream or tasks."
Imagine instead: "This is the future we are working to create. These are the things we believe would have to be true in order to reach that future. You're joining the team working on this hypothesis, to prove or disprove it to determine whether this broader theory is possible."
Where would you rather work?
Several Fortune 500 companies have reported back that they’ve begun using the framework—today can be the day your organization does as well. Reach out if you could use my support or to share how it’s working for you!
Thanks for reading,
Brian
Whenever you're ready, here are 3 ways I can help you:
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If you want to go deeper as an individual, you can sign up for my live course Future Solving Fundamentals. My flagship live course on how to position yourself in the future in the era of AI. I share over a decade of AI strategy expertise, proven methods, and actionable strategies. This course sets the stage for a new era of value creation with artificial intelligence. Join leaders from Microsoft, Accenture, Amazon, Disney, Mastercard, IKEA, Oracle, Intel, and more.
If you want to go deeper as a team or organization, I’ve partnered with LinkedIn Learning, Emeritus, and Duarte to create tailored Future Solving™️ programs that leaders at Fortune 500 companies are implementing to help reembrace science in their decision-making and meet the complexities of the 21st century with a system of leadership, strategy, and decision-making that is built-for-purpose for era of artificial intelligence. You can reach out to connect and learn more here.