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Data Update - 01/27/2016vsco.co/blevishkin
Data Update - 03/17/17vsco.co/prazakj
04 SEP ...
VSCO→CONFIDENTIAL→DONOTDISTRIBUTEVSCO→CONFIDENTIAL→DONOTDISTRIBUTE
Everybody’s confused on
what *is* Data Science
→ Execs ...
VSCO→CONFIDENTIAL→DONOTDISTRIBUTEVSCO→CONFIDENTIAL→DONOTDISTRIBUTE
The confusion is on “data”
and who should analyze it
→L...
VSCO→CONFIDENTIAL→DONOTDISTRIBUTEVSCO→CONFIDENTIAL→DONOTDISTRIBUTE
“Data Science” really is one
of two functions / skillse...
VSCO→CONFIDENTIAL→DONOTDISTRIBUTE
Analytics Customers
→Product
→Marketing
→Revenue
→Other: People Ops, Support, etc.
VSCO→CONFIDENTIAL→DONOTDISTRIBUTE
Analytics Mission
Provide relevant, reliable and timely data insights to inform how the
...
VSCO→CONFIDENTIAL→DONOTDISTRIBUTE
How Analytics Succeed - at *relevant*
Every analysis has a business counterpart who can ...
VSCO→CONFIDENTIAL→DONOTDISTRIBUTE
How Analytics Succeed - at *timely* & *reliable*
Invest in systems & processes to preven...
VSCO→CONFIDENTIAL→DONOTDISTRIBUTE
How Data Analysts Succeed (individual level)
→Curious, persistent, & thorough mindset
→R...
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The Role(s) of Data Science in Modern Organizations

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There is a lot of confusion around the role of Data Science, what skills it requires, and what career paths it can lead to. This deck attempts to clear up some misconceptions, speaks to the different roles within data science, and discuss how individuals and data orgs can succeed in this nebulous environment.

Publicada em: Tecnologia
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The Role(s) of Data Science in Modern Organizations

  1. 1. VSCO→CONFIDENTIAL→DONOTDISTRIBUTE Data Update - 01/27/2016vsco.co/blevishkin Data Update - 03/17/17vsco.co/prazakj 04 SEP 2018 RUBEN KOGEL ( VSCO ) The Role(s) of Data Science in Modern Organizations
  2. 2. VSCO→CONFIDENTIAL→DONOTDISTRIBUTEVSCO→CONFIDENTIAL→DONOTDISTRIBUTE Everybody’s confused on what *is* Data Science → Execs (who “hire” the DS function) want “insights” and tracking for everything → Orgs need a business analytics function, a personalization function, or both → Candidates think it’s mostly about fancy machine learning techniques → Practitioners think it’s mostly about counting things properly vsco.co/evanhundelt
  3. 3. VSCO→CONFIDENTIAL→DONOTDISTRIBUTEVSCO→CONFIDENTIAL→DONOTDISTRIBUTE The confusion is on “data” and who should analyze it →Lots of types of data • events / clickstream • production data • financials • qualitative insights / feedback • competitive intelligence →Should every data collection, reporting, or analysis go through Data Science? vsco.co/evanhundelt
  4. 4. VSCO→CONFIDENTIAL→DONOTDISTRIBUTEVSCO→CONFIDENTIAL→DONOTDISTRIBUTE “Data Science” really is one of two functions / skillsets → (Business) Analytics • serves internal customers • data insights on how the business is performing and how to improve it • requires data analysis & business sense → Personalization • serves external customers (via Product) • optimizes user experience and LTV via personalized results (using user data) • requires data engineering skills (ML) & experimental design vsco.co/evanhundelt
  5. 5. VSCO→CONFIDENTIAL→DONOTDISTRIBUTE Analytics Customers →Product →Marketing →Revenue →Other: People Ops, Support, etc.
  6. 6. VSCO→CONFIDENTIAL→DONOTDISTRIBUTE Analytics Mission Provide relevant, reliable and timely data insights to inform how the business is performing and how to improve it, via: • instrumentation • ad-hoc analyses • advisory Goal: accelerate the feedback loop & drive faster, better decisions that will yield better business outcomes
  7. 7. VSCO→CONFIDENTIAL→DONOTDISTRIBUTE How Analytics Succeed - at *relevant* Every analysis has a business counterpart who can ask questions and act on the findings →collaboration - on the question, the findings - is key →success is tied to the business counterpart success →analytics org mirrors counterparts (product, marketing, revenue)
  8. 8. VSCO→CONFIDENTIAL→DONOTDISTRIBUTE How Analytics Succeed - at *timely* & *reliable* Invest in systems & processes to prevent mistakes and increase productivity →standardize methods, data sources, metrics, reporting →invest in clean, robust, and scalable data infrastructures →document all important findings →develop & promote self serve tools whenever possible →hybrid centralized / de-centralized model is optimal
  9. 9. VSCO→CONFIDENTIAL→DONOTDISTRIBUTE How Data Analysts Succeed (individual level) →Curious, persistent, & thorough mindset →Rigorous analytical thinking →Interpersonal & communication skills →Coding skills (SQL + data manipulation language) →Statistics and data intuition →Business sense

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