Data StrategyHow & When to Invest

@chemphill @carbonfive #sxswV2V

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

@chemphill @carbonfive #sxswV2V

@chemphill @carbonfive #sxswV2V

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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Solving Big ProblemS.

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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Solving New ProblemS.

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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solving Good ProblemS.

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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How we work with data

Design & BuildCollect & Analyze

Define goals & Craft hypotheses

How we communicate data

How we communicate data

How we communicate data

Prosper

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

Isilon InfoIQ

• 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

Opower

• 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

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)