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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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Good data culture requires intentional decisions about what data is collected and why.
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Democratizing data access speeds up decision-making and reveals cultural issues when too difficult.
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Human curation remains essential for data quality as AI can’t yet fully replace manual intervention.
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Data storytelling shapes organizational culture and helps unify diverse interpretations of the same data.
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Designers play a critical role beyond aesthetics by mediating between teams and aligning data practices.
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AI’s current lack of context understanding leads to overconfidence and risk of missing nuance.
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Transparency via data lineage tools is crucial to avoid black-box models and build trust in AI.
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There is often tension between 'hard' quantitative data and 'soft' experiential insights in decision-making.
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A mature data culture integrates data quality metrics with organizational norms and responsibilities.
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Information architecture and shared terminology are vital to reduce confusion and promote healthy data culture.
Notable Quotes
"Data problems often end up being people problems because we're lazy, shortsighted, territorial creatures."
"We as people are our data, which means we have to treat data problems with care and consideration."
"Good data culture is intentional about the decisions we want to make and why."
"If people have to file requests and learn SQL just to get simple data, you have a culture problem."
"Data quality is multifaceted, subjective, and can't be reduced to simple profiles alone."
"AI isn't good at context yet; it may insist it's a B or a 13, but not that it could be both depending on view."
"No black boxes: AI needs to explain its answers just like people do to build accountability and trust."
"Most people will make the right decisions with data if they know the appropriate guidelines."
"Designers should act like therapists or journalists when negotiating political divides around data governance."
"The Power BI team succeeds with data because everyone on the team is obsessed with understanding customer behavior."
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