Data Frenzy“Information scientists work every day on the design, delegation and choice of classification systems and standards. Each standard and each category valorizes some point of view and silences another.”
-Sorting Things Out: Classification and its Consequences
Geof Bowker & Susan Leigh Star
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Types of data
• Quantitative and Objective
- Information collected in sufficient quantity that significance can be determined numerically
• Qualitative and Subjective
- Information collected where significance is determined experientially
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Measuring of data
• Quantitative and Objective
- Instrumenting an app with tracking code, querying the database or log files
• Qualitative and Subjective
- Interacting one-on-one with users
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Big Data- AI and adaptive systems
- gross patterns in technology and users
- challenge to core biz assumptions
- dispel old fashioned market ideas
- statistical significance!
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Big Data- What signals?
- What inputs (devices)?
- Scaling issues?
- External data sources?
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New (small) Data- match to qualitative
- iterative design and data collection for formative and generative design
- summative methods (data w/in experience and context)
- high information density rather than high volume
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New (small) Data- Data with user base of 0?
- When to start?
- Mining APIs (Twitter, Google, Facebook)?
- Process?
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Good Data (data for good)
- open data
- health care, government, transportation, etc
- understandings of human psychology and/or social behavior
- harness knowledge and collective good of the crowd
- behaviors outside of market/product context
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Good Data (data for good)
- Socrata?
- health care, government, transportation, etc?
- Ethnography, research institutions?
- Surprising findings?
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Skills for data
• Quantitative and Objective
- Data scientists, researchers in labs, statisticians, mathematicians
• Qualitative and Subjective
- Cognitive psychology, anthropology, HCI experts, product managers, ethnographers
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Data Culture
• Proactive vs Reactive
• Integration into whole company
• Valued from the top down
• Budgeted for and prioritized
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Measuring Impact
• Optimizations
• Generating new ideas
• Informed recommendations
• Dispelling old fashioned ideas about markets/user behavior
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Prosper• Qual + Quant across devices
• EDD + TDD
• Continuous experiments testing aesthetics, multi-variant funnels, messaging and interaction design.
• Roughly tripled mobile conversion rates
• $354.9 Million in 11 Rounds from 25 Investors
• Crunches 50 Gb of metric data per hour
• Manage petabytes of data (1,000,000 gigabytes)
• Shows dataset growth trends, past performance to predict future needs
• Diagnose historical and real-time system needs
• Fully manage resources via quota and de-dup reports
Isilon InfoIQ
• Data from current weather, season, time of day etc
• Data from user behaviors and settings
• AI got better and better over time
• 85 utility partners + 18M customers
Opower
SimpleGov• Aggregating open data from multiple
government agencies
• Data from response rates to show most proactive agencies
• Tagging of open data to easily find issues by various metrics
Resources• UserTesting
• Heap
• Segment.io
• Optimizely Testing in iOS
• Testing with PhoneGap
• Rainbow Spreadsheet
• Socrata
• Data.gov
Tools• Proto.io (mobile/iOS)
• Flinto (iOS)
• Brief (iOS)
• Solidify (User Testing)
• Easel (Responsive Web)
• InVision (Designing+Prototyping)
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