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Enterprise Data Science:
Navigating the Matrix
ENDA RIDGE, PHD
ALGORITHMS LEAD, UK FMCG
#GuerrillaAnalytics http://guerril...
What You Will Learn
 Enterprise Data Science
 Reminder of how Data Science really works
 Challenges in a matrix organis...
What I’ve Learned
PhD
‘Design of
Experiments
for Tuning
Algorithms’
Boutique
Consultancy
Forensic
Analytics
Manager
Senior...
What is Data Science?
“Data Science is the discipline of discovering data and understanding data to
find opportunities, ef...
What is Data Science?
“Data Science is the discipline of discovering data and understanding data to
find opportunities, ef...
What is Data Science?
“Data Science is the discipline of discovering data and understanding data to
find opportunities, ef...
What Data Science is not…
Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge
6
https:...
What Data Science is not…
Big Data
Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge...
What Data Science is not…
Big Data
Business Intelligence &
Analytics
Copyright Enda Ridge 2016#GuerrillaAnalytics http://g...
What Data Science is not…
Big Data
Business Intelligence &
Analytics
Playing around with data
Copyright Enda Ridge 2016#Gu...
Uncertainty
 Data
 Process
 Questions
 Solutions
Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analyti...
New Data
 Disparate sources
 Surveys
 Web scrapes
 Logs
 3rd party
Copyright Enda Ridge 2016#GuerrillaAnalytics http:...
Variety
 Data joins
 Visualizations
 Algorithms
 Languages
Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerril...
High maturity Enterprise Data Science
Frame a
business
hypothesis
Gather and
generate
data
Analyse
Confirm with
experiment...
The Enterprise Matrix
Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge
14
Marketing...
The Enterprise Matrix
Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge
15
Marketing...
5 Challenges in Enterprise Data Science
Org structure & the customer
Enabling the team
Making insights actionable
Integrat...
Challenge #1: structure and customer
Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_rid...
Action #1: Chief Data Scientist (CDS)
 Chief Data Scientist in hub and spoke
 Clear Engagement Model to help those
custo...
Challenge #2: Enabling the team
Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge
19...
Action #2: build tactical environment
 Quick and simple ‘within the walls’
Copyright Enda Ridge 2016#GuerrillaAnalytics h...
Action #2: build tactical environment
 Quick and simple ‘within the walls’
 Then build out
 Scale
 Permission groups
...
Challenge #3: making insights actionable
Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda...
Action #3: Operating Model
 Worst case – rewrite code in Dev
 Best case – push algorithms into services
 Realistic midd...
Challenge #4: data community
Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge
24
Ma...
Action #4: Set out your stall
 Create Terms of Reference
 Quick wins
 Create marketing materials for Data Science
 Hav...
Challenge #5: Getting & keeping people
Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_r...
Action #5: Prioritised Hires
 Less genius, more resilience and practicality
 Prefer experience in early days
 Clear pro...
Enterprise Data Science
 Be clear on what Data Science is
 5 steps
 Establish Chief Data Scientist, Hub and Spoke
 Ena...
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Enterprise Data Science: Navigating the Matrix (Chief Data Scientist Forum Sept 2016)

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A talk on Data Science in the Enterprise. How it's different, the challenges you face and steps to overcome those challenges. Read this if you are trying to establish a Data Science capability in a large enterprise

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Enterprise Data Science: Navigating the Matrix (Chief Data Scientist Forum Sept 2016)

  1. 1. Enterprise Data Science: Navigating the Matrix ENDA RIDGE, PHD ALGORITHMS LEAD, UK FMCG #GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge
  2. 2. What You Will Learn  Enterprise Data Science  Reminder of how Data Science really works  Challenges in a matrix organisation  Navigating the matrix. 5 steps to build an Enterprise Data Science capability  How this will help you  How to fit Data Science into the enterprise  Why the Chief Data Scientist is critical  The most important decisions in year 1 Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 1
  3. 3. What I’ve Learned PhD ‘Design of Experiments for Tuning Algorithms’ Boutique Consultancy Forensic Analytics Manager Senior Manager Professional Services Head of Algorithms Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge No matter the industry, doing agile data science always faces the same challenges… 2004 2008 2010 2012 2015 Enterprises rarely have the flexibility to accommodate Data Science 2
  4. 4. What is Data Science? “Data Science is the discipline of discovering data and understanding data to find opportunities, efficiencies and improvements” Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 3
  5. 5. What is Data Science? “Data Science is the discipline of discovering data and understanding data to find opportunities, efficiencies and improvements” Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 4  Opportunities in new data sources, new products, new understanding  Efficiencies in automation, process changes, organisation change  Improvements in product features, product offerings
  6. 6. What is Data Science? “Data Science is the discipline of discovering data and understanding data to find opportunities, efficiencies and improvements” Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 5  Data Science uses the scientific method  Experiments to test hypotheses  Rigorous measurement  Making changes and observing effects
  7. 7. What Data Science is not… Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 6 https://vimeo.com/88093956
  8. 8. What Data Science is not… Big Data Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 7 https://vimeo.com/88093956
  9. 9. What Data Science is not… Big Data Business Intelligence & Analytics Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 8 https://vimeo.com/88093956
  10. 10. What Data Science is not… Big Data Business Intelligence & Analytics Playing around with data Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 9 https://vimeo.com/88093956
  11. 11. Uncertainty  Data  Process  Questions  Solutions Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 10
  12. 12. New Data  Disparate sources  Surveys  Web scrapes  Logs  3rd party Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 11
  13. 13. Variety  Data joins  Visualizations  Algorithms  Languages Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 12
  14. 14. High maturity Enterprise Data Science Frame a business hypothesis Gather and generate data Analyse Confirm with experiment Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge Data-driven operations Data-driven products 13
  15. 15. The Enterprise Matrix Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 14 Marketing Sales / Trading Logistics Other IT, InfoSec, Architecture Product Development BI & Analytics HR & Recruitment
  16. 16. The Enterprise Matrix Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 15 Marketing Sales / Trading Logistics Other IT, InfoSec, Architecture Product Development BI & Analytics HR & Recruitment Data Science
  17. 17. 5 Challenges in Enterprise Data Science Org structure & the customer Enabling the team Making insights actionable Integration with the data community Getting and keeping people Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 16
  18. 18. Challenge #1: structure and customer Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 17 Marketing Sales / Trading Logistics Other IT, InfoSec, Architecture Product Development BI & Analytics HR & Recruitment Data Science  You need to demonstrate value fast  But  All want to own ‘the sexiest job of 20th century’  Rebranding  Perhaps non-Agile ways of working  Perhaps not ready to execute
  19. 19. Action #1: Chief Data Scientist (CDS)  Chief Data Scientist in hub and spoke  Clear Engagement Model to help those customers  ‘Ready’  ‘Done’  Pipeline  Simple project artefacts  Distributed model when organisation matures Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 18 Project Project Project CDS
  20. 20. Challenge #2: Enabling the team Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 19 Marketing Sales / Trading Logistics Other IT, InfoSec, Architecture Product Development BI & Analytics HR & Recruitment Data Science  You need Data and Technology to do Data Science  But  Tradition of control  Generally slow  Incentivised to maintain status quo
  21. 21. Action #2: build tactical environment  Quick and simple ‘within the walls’ Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 20 Lab Data store Applica tions Dev tools
  22. 22. Action #2: build tactical environment  Quick and simple ‘within the walls’  Then build out  Scale  Permission groups  Proxy access  Local admin rights  Licencing  Tech Support  Data governance  Data feeds Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 21 Lab Data store Applica tions Dev tools
  23. 23. Challenge #3: making insights actionable Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 22 Marketing Sales / Trading Logistics Other IT, InfoSec, Architecture Product Development BI & Analytics HR & Recruitment Data Science  You need Data Science turned into Algorithms in products  But  Own opinions on tech  Not familiar with Data Science methods and code  Product lens
  24. 24. Action #3: Operating Model  Worst case – rewrite code in Dev  Best case – push algorithms into services  Realistic middle ground  Use version control  Create ‘integration tests cases’  Create Data Science user stories  Keep algorithms simple  See ‘Guerrilla Analytics’ Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 23 Lab Development Interfaces
  25. 25. Challenge #4: data community Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 24 Marketing Sales / Trading Logistics Other IT, InfoSec, Architecture Product Development BI & Analytics HR & Recruitment Data Science  You need access to data  You need customers  But  Gatekeepers  Perceived threat  Rebranding  Confusion with customer
  26. 26. Action #4: Set out your stall  Create Terms of Reference  Quick wins  Create marketing materials for Data Science  Have clear Engagement materials  Engage with broader data community (forums, talks etc) Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 25
  27. 27. Challenge #5: Getting & keeping people Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 26 Marketing Sales / Trading Logistics Other IT, InfoSec, Architecture Product Development BI & Analytics HR & Recruitment Data Science  You need key hires and the market is competitive  But  Existing pay structures  Existing job formats  Hiring agencies
  28. 28. Action #5: Prioritised Hires  Less genius, more resilience and practicality  Prefer experience in early days  Clear progression structures and performance management  Establish training budget and guidance Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 27 X
  29. 29. Enterprise Data Science  Be clear on what Data Science is  5 steps  Establish Chief Data Scientist, Hub and Spoke  Enable people with tactical technology  Op Model to make insights actionable  Set out your stall  Prioritised hires  Find me  on Twitter @enda_ridge  on my blog http://Guerrilla-Analytics.net Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 28

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