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Summary
The age of data is well underway. But using data to make better decisions is not as simple as one might hope. In this session, we'll take a look at some of the challenges that arise when we fail to build better data culture and what we can do as designers to fix it.
Key Insights
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Data challenges split into 'physics' (technical, tactical) and 'culture' (human, ethical) domains.
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Designers play a crucial role not only in creating tools but in shaping the culture and interpretation around data.
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Good data culture requires intentionality about what data is needed and why, focusing on the 'why' and 'how' more than just the 'what'.
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Data democratization is essential; easy access and usability of data empower better decision-making across the organization.
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Data quality is complex, multifaceted, and subjective; it requires human curation alongside technical solutions.
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Storytelling is inherent in how organizations use data and impacts how people interpret signals and make decisions.
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AI currently struggles with context, often producing overly certain outputs that miss nuances important to human judgment.
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Explaining AI decisions and providing data lineage increases trust and accountability, paralleling human expectations of explanation.
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There is a tension between hard (quantitative) data and soft (experiential) data, which needs cultural frameworks to reconcile.
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Designers’ unique skills as facilitators and mediators help bridge organizational and political divides in data governance and culture.
Notable Quotes
"Data problems are often people problems because we are lazy, shortsighted, territorial creatures."
"We as people are our data, so treating data with care and consideration is critical."
"Good data culture is intentional; it's about making thoughtful decisions about data use and interpretation."
"The story we tell with data shapes culture and guides how we all interact with that data."
"If people have to write SQL queries just to get simple answers, the culture is broken."
"Data quality is not just numbers; it requires human judgment and institutional knowledge."
"AI isn’t good at context yet, so it often presents a myopic point of view with false certainty."
"No black boxes: AI must be able to explain its answers like a human would."
"Designers should behave like therapists or journalists, seeking truth and bridging divides."
"Design maturity and data culture maturity might have parallels but no established standards yet."
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