September 19, 2024

The ultimate goal of data science can be reduced to just two words: Decide better. But a decision without action is merely a dream. How do we turn information into action—and perhaps more importantly, what prevents us from doing so? - Alistair Croll

In the late 1800s, Elias St. Elmo Lewis proposed the AIDA model (Attention, Information, Desire, Action) to explain how consumers get from data to action:

1. Something captures our attention.
2. We collect information about it.
3. We discover a gap between our current state and an imagined, better state, triggering desire.
4. We act.

In today’s data-rich environment, this journey is far more complex and uncertain. Even with perfect information, organizations are often paralyzed, unable to turn data-driven insights into meaningful action.

There are four big obstacles preventing groups and organizations from acting on what they know.

Every individual brings their own, undisclosed agenda to the table. Our personal experiences, biases, and contexts shape how we interpret data. Someone fearing job loss may prioritize safety, while an exhausted team member might just want the meeting to end. These hidden motivations can lead a team starting with the same data to reach vastly different conclusions.

We’re remarkably poor at understanding what others truly think, even when hard data is available. A 2022 study showed that while 73% of Americans support meaningful action on climate change, they believe that only 40% of their fellow citizens feel the same way. This misperception is often fueled by the disproportionate attention given to vocal minorities, amplified by media and social platforms.

Humans are inherently social creatures, and the fear of ostracism can be paralyzing. As 2024 Data Universe keynote speaker David McRaney noted, we often fear social death more than death itself.  This fear often leads us to remain silent even when we know a decision is wrong, prioritizing social acceptance over speaking truth to power.

Game theory illustrates how competing interests can lead to suboptimal outcomes, even when cooperation would benefit everyone. Trust becomes crucial in these scenarios. However, unexpected side effects of decisions often become clear only long after action has been taken, leading to complex regulatory environments and perverse incentives that make everyone act against their own best interests.

The antidote to coordination problems is common knowledge. When everyone knows that everyone knows something, our social dynamics can propel us forward rather than hold us back. This principle is illustrated in the tale of the Emperor's New Clothes. It’s only when a child blurts out the obvious truth that everyone acknowledges the emperor’s nakedness. Studies show that as few as 25% of a group putting forward a change can make the rest of the group quickly coalesce around that change.

While data helps us decide better, our ultimate goal must be to produce better outcomes. To achieve this, we must:

• Align teams with clear messages, exposing hidden motivations.
• Better understand opposing viewpoints and their popularity.
• Encourage plain-spoken criticism without fear of ostracism.
• Turn data into common knowledge, ensuring everyone knows that everyone knows the facts.

Data must not only help us decide better but also make us act better. It should be the child at the Emperor’s parade, willing to speak truths so self-evident that suddenly, we all know that we all know—and can now act together.

That’s why Data Universe 2025 not only explores where the rapid pace of innovation is leading us, but also asks how best to harness that change in service of what truly matters: Better outcomes.