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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 often reflects human behavior, making intentions crucial in its interpretation.
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Questions surrounding the 'why' behind data are often as important as the data itself.
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Good data culture requires intentionality in decision-making and the use of data.
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Democratizing data access is critical for fostering a proactive culture.
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Storytelling with data helps contextualize insights and engage stakeholders.
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Data quality is multifaceted and subjective, requiring ongoing attention and care.
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Designers must balance practical data usage with creative interpretations and storytelling.
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AI presents challenges in providing context and understanding nuances in data.
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Cultural norms within an organization deeply affect how data is utilized and interpreted.
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Strong data cultures incorporate cross-functional collaboration and insights from multiple disciplines.
Notable Quotes
"Data problems often end up being people problems."
"We need to be clear on the problem before we try and solve it."
"Words matter and language is foundational to culture."
"Data is more than just a metric; it's about customer behavior."
"Human curation is still vital in assessing data quality."
"Good data culture uses signal appropriately and defines its use clearly."
"It's essential to disambiguate what data means in the context of organizational goals."
"Designers should own the responsibility of bridging cultural divides in data usage."
"AI struggles with context, often missing nuances that are critical for interpretation."
"Understanding an organization’s data culture is vital before joining a new team."
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