1. From Data to Outcomes GIGEL AVRAM SEP 2015

2. About Big Data We had “data” before “big data”, so what is different ? A wealth of ambient data : -Machine born -Crowd sources -Digital exhaust : much of digital lives generate it already -Volume, variety, velocity -Data contains experiences

3. About Insights “An actionable insight is a piece of information that enables one to make an informed decision”* “Actionable insights are typically derived by synthesizing vast amounts of data into succinct, concise statements” Examples of insights in the apps analytics world : ”What are the most used apps ?” ”What makes and models experience the most problems ?” “What features are harder to learn ?” “What features are never used ?” “What apps stopped being used ? “ * Wikipedia

5. About highly correlated insights * Mapping the customer experience – Joyce Hostyn *

6. About knowledge “The only source of knowledge is experience” – Albert Einstein -Data contains experiences and that is knowledge -Knowledge leads to action and outcomes -Representing knowledge means showing how different pieces of information relate to each other

7. The data model to tie it all together data->insights->knowledge->wisdom The model puts data in context to enable people to form their own story “A statement without a context is no statement” Measures and results should be intuitive, everything needs a reference point around Trending and past history provides critical context and need to be always shown … provides the computed impact to users or bottom line An individuals user experience is what should be measured (aka the currency) You should be able to drill down to a level where ‘root cause’ and user impact is obvious Multiple layer if semantics are needed to link the high level questions to root causes …provides multiple solutions and costs leading to action and outcomes Outcomes are happening when a discussion around options and costs is enabled Data needs to be presented with more nuance i.e. “shades of gray” to be practical to use, multiple solutions have to become visible KPI and scorecard only based results are usually presented as green, yellow, red and do not usually go beyond providing information only.

8. Why Products are part of complex and impactful ecosystems. Rapid change in the ecosystem is Inevitable. Quality is created thru through iterative improvements Continuous knowledge building thru data. A clear view of the ecosystem health. Continuous adaptation loop. Learn Adapt Measure Continuous Quality How Productizing data driven outcomes

9. Practical example “Understanding the health of the ecosystem of devices, users or apps”

10. Our approach starts with user at the center and the high level questions needing answers : What questions focusing on users experience, like : “How many users experience […] with no issues on any given day?” “What do users do the first day, week, month using […] ?” Why answers providing a computed impact to users, like : “For users who experiences problems, what are the issues they hit?” “Of those issues, how much will addressing each improve the experience?”

18. Recap Data needs to tell a story that maps to a customer end to end journey Fitting the data into a model is crucial to make sound data driven decisions. Data without context can be biased and can be misleading. The data models can answer higher level questions and lead to outcomes otherwise not possible if looking at separate insights Understand thru data if customer believes we constantly care about their needs. Understand thru data how needs are changing. Data is a powerful listening channel, but not the only listening channel The story told by the data is best validated by multiple listening channels : customer support, blogs, PR. Each channel has its strengths and can complement each other.

19. Q&A Contact : gigela@microsoft.com