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Building a Successful
Enterprise Data Science Capability
ENDA RIDGE, PHD
HEAD OF DATA SCIENCE & ALGORITHMS, UK SUPERMARKET...
“Data is the new oil”
“The sexiest job of the century”
Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analy...
What I’ve Learned
PhD
‘Design of
Experiments
for Tuning
Algorithms’
Data mining
Software
pre-sales
Forensic Data
Analytics...
Common pitfalls with Enterprise Data Science
No understanding of Data Science
• Business cannot engage with data science, ...
What you will learn today
How to define Data Science
• So you can talk about it, influence stakeholders and management exp...
We know what Science is
What is Data Science for the Enterprise?
“Data Science is the discipline of understanding processes
described by data for ...
What is Data Science?
 Opportunities - in new data sources, new products, new customer understanding
 Efficiencies - in ...
What is Data Science?
“Data Science is the discipline of understanding processes
described by data for the benefit of the ...
Analogies help differentiate Data Science from other teams
 Data Science
 The physicists
 Finding the equations and
ass...
Mature Data Science in the Enterprise
Frame a
business
hypothesis
Gather and
generate
data
Analyse
Confirm
with
experiment...
Data Science Involves Uncertainty
Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge
...
Data Science Involves New Data
Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge
 S...
Data Science involves Varied Activities
Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_...
Data Science involves Experiments
Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge
Navigating the Enterprise Matrix
Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge
M...
Navigating the Enterprise Matrix
Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge
M...
5 Challenges for Data Science
Org structure & the customer
Enabling the team
Making insights actionable
Integration with y...
Challenge #1: Org structure and the customer
Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @...
Action #1: Strong Leader in a Central Data Science Team
 Central Hub
 A Senior Advocate
 Business side, not IT side
 C...
Challenge #2: Enabling the team
Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge
Ma...
Action #2: build tactical environment for insights
 Reduce IT complexity
 Scale
 Permission groups
 Proxy access
 Loc...
Challenge #3: making insights actionable
Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda...
Action #3: Focus on the easy opportunities
 Avoid big complex product development programmes
 Prefer projects that are a...
Challenge #4: Distinction from data community
Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net ...
 Create Terms of Reference
 Quick wins
 Create marketing materials for Data Science
 Have clear Engagement materials
...
Challenge #5: Hiring & keeping people
Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ri...
Action #5: Negotiate Prioritised Hires from Day 1
 You need 1 or 2 data scientists who
can do science and communicate
 L...
5 Challenges for Data Science
• Strong leader
#1 Org structure & the customer
• Tactical environment
#2 Enabling the team
...
Building a Successful Enterprise Data Science Capability
 Questions, training?
 Find me
 on Twitter @enda_ridge #Guerri...
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Building a successful enterprise Data Science capability (CX Network October 2017)

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Many data science initiatives fail to launch because organisations do not understand the dependencies, people, process and technologies needed to make data science work for them. This is all the more challenging in large enterprises with legacy systems, technology constraints and a business culture that is not data driven. Find out the steps you need to take to successfully leverage data science in your enterprise.

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Building a successful enterprise Data Science capability (CX Network October 2017)

  1. 1. Building a Successful Enterprise Data Science Capability ENDA RIDGE, PHD HEAD OF DATA SCIENCE & ALGORITHMS, UK SUPERMARKET AUTHOR OF “GUERRILLA ANALYTICS – A PRACTICAL APPROACH TO WORKING WITH DATA”
  2. 2. “Data is the new oil” “The sexiest job of the century” Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge
  3. 3. What I’ve Learned PhD ‘Design of Experiments for Tuning Algorithms’ Data mining Software pre-sales Forensic Data Analytics Senior Manager Professional Services Head of Data Science & Algorithms Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 2004 2008 2010 2012 2015 #1 Challenge to doing Enterprise Data Science successfully: Organisations do not have the right focus and flexibility to accommodate Data Science
  4. 4. Common pitfalls with Enterprise Data Science No understanding of Data Science • Business cannot engage with data science, won’t accept its recommendations Hiring a team without business objectives & sponsorship • No measure of success, no support from leadership Hiring a team and not enabling them • Team without technology, data, supporting teams to do their job Not working closely with business customers • Irrelevant solutions, results never used Forcing Data Science into a delivery methodology e.g. scrum • Scientific enquiry constrained Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge
  5. 5. What you will learn today How to define Data Science • So you can talk about it, influence stakeholders and management expectations The typical challenges and pitfalls you will encounter in an enterprise • So you can make the right decisions in Year 1 and create a successful capability Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge
  6. 6. We know what Science is
  7. 7. What is Data Science for the Enterprise? “Data Science is the discipline of understanding processes described by data for the benefit of the business” Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge
  8. 8. What is Data Science?  Opportunities - in new data sources, new products, new customer understanding  Efficiencies - in automation, process changes, organisation change  Improvements - in product features, product offerings Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge “Data Science is the discipline of understanding processes described by data for the benefit of the business”
  9. 9. What is Data Science? “Data Science is the discipline of understanding processes described by data for the benefit of the business” Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge  Data Science uses the scientific method  Trying to model our businesses and our customers  Experiments to test hypotheses  Making changes, measuring and observing effects
  10. 10. Analogies help differentiate Data Science from other teams  Data Science  The physicists  Finding the equations and assumptions that explain the movements of the planets and stars  Making predictions of where the planets will be next  Testing those theories with experiments  Analytics  The astronomers  Observing the sky  Mapping the planets and other bodies  Summarising observations and trending behaviours  Big Data  Hubble telescope  Modern telescopes orbiting Earch  Radio wave collectors and other signals about planets and stars
  11. 11. Mature Data Science in the Enterprise Frame a business hypothesis Gather and generate data Analyse Confirm with experiment Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge Business change
  12. 12. Data Science Involves Uncertainty Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge  Data  Processes  Questions  Solutions
  13. 13. Data Science Involves New Data Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge  Surveys  Web scrapes  Systems  Logs  3rd party
  14. 14. Data Science involves Varied Activities Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge  Data joins  Visualizations  Algorithm automation  Programming languages
  15. 15. Data Science involves Experiments Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge
  16. 16. Navigating the Enterprise Matrix Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge Marketing Sales / Trading Logistics Other IT, InfoSec, Architecture Product Development BI & Analytics HR & Recruitment
  17. 17. Navigating the Enterprise Matrix Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge Marketing Sales Logistics Other IT, Information Security, Architecture Product Development BI & Analytics HR & Recruitment Data Science
  18. 18. 5 Challenges for Data Science Org structure & the customer Enabling the team Making insights actionable Integration with your technology and business dependencies Getting and keeping people Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge
  19. 19. Challenge #1: Org structure and the customer Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge Marketing Sales Logistics Other IT, InfoSec, Architecture Product Development BI & Analytics HR & Recruitment Data Science  All want to own ‘the sexiest job of 20th century’  Rebranding of teams  Perhaps non-Agile ways of working  Perhaps not ready to execute your recommendations
  20. 20. Action #1: Strong Leader in a Central Data Science Team  Central Hub  A Senior Advocate  Business side, not IT side  Clear Engagement Model  Clear Pipeline and Priorities Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge Project Project Project Data Science Hub  Avoid these pitfalls:  Hire a couple of ‘clever scientists’, leave them in a room and wait for magic  Land data scientists in an existing business function, pulled into operational roles  Fail to prioritise projects, overwhelmed with demand
  21. 21. Challenge #2: Enabling the team Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge Marketing Sales / Trading Logistics Other IT, InfoSec, Architecture Product Development BI & Analytics HR & Recruitment Data Science  Avoid pitfalls:  Building your own shadow IT  Building complex Data Science infrastructure  Waiting until the data is ‘perfect’  Waiting until the data is in a warehouse
  22. 22. Action #2: build tactical environment for insights  Reduce IT complexity  Scale  Permission groups  Proxy access  Local admin rights  Licencing  Tech Support  Data feeds  Create insights instead of maintained products  Use tactical as a design pattern for strategic Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge ‘Lab’ Data store Server Dev tools
  23. 23. Challenge #3: making insights actionable Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge Marketing Sales / Trading Logistics Other IT, InfoSec, Architecture Product Development BI & Analytics HR & Recruitment Data Science You will need Data Science turned into business change  Development teams  Have own opinions on tech  Are not familiar with Data Science methods and code  Take Product lens, not a data lens  Pitfalls  Picking large, complex products  Distracted with Operating Models, delivery panaceas like Agile
  24. 24. Action #3: Focus on the easy opportunities  Avoid big complex product development programmes  Prefer projects that are a decision rather than an automation e.g. stop doing that, start doing this  If you do build a product, keep the team small  Prefer projects where you can insert data science in a light-weight way  Replacing/intercepting a spreadsheet process  Monthly calculation to support high value business decisions e.g. pricing, segmentation  Hold the customer to account with an engagement model  A.R.C.I to call out accountability and reduce interference  Project brief and schedule that you stick to  ‘Marketing collateral’ when the job is completed Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge
  25. 25. Challenge #4: Distinction from data community Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge Marketing Sales / Trading Logistics Other IT, InfoSec, Architecture Product Development BI & Analytics HR & Recruitment Data Science You need access to data You need internal customers  But  Gatekeepers  See you as a threat  Rebranding  Confusion for customer  Pitfalls  Competing on analytics  Engaging in complex reorganisations and role definitions
  26. 26.  Create Terms of Reference  Quick wins  Create marketing materials for Data Science  Have clear Engagement materials  Engage with broader data community (forums, talks etc) Action #4: Set out your stall Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge • Relentlessly communicate what Data Science is • Have worked examples to bring it to life • Pick early ‘wins’ that other data teams could not or will not attempt • Communicate success as collaboration and opportunity • Stamp out Data Science elitism
  27. 27. Challenge #5: Hiring & keeping people Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 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 and grades  Hiring agency relationships  A difficult journey if starting from scratch  Pitfalls  Accept the status quo  Inheriting people who are not the right fit  Not paying enough attention to your new team
  28. 28. Action #5: Negotiate Prioritised Hires from Day 1  You need 1 or 2 data scientists who can do science and communicate  Less genius, more resilience and practicality  Begin the HR conversations early  Interview process  Progression paths  Head count, salary budget  Training budget  Networking opportunities Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge
  29. 29. 5 Challenges for Data Science • Strong leader #1 Org structure & the customer • Tactical environment #2 Enabling the team • Focus on easy opportunities #3 Making insights actionable • Set out your stall #4 Integration with the data community • Key hires and take care of them #5 Getting and keeping people Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge
  30. 30. Building a Successful Enterprise Data Science Capability  Questions, training?  Find me  on Twitter @enda_ridge #GuerrillaAnalytics  on my blog http://guerrilla-analytics.net Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge  Guerrilla Analytics gives you a simple Op Model for analytics and data science teams  Help managers support your team building  A practical definition of Data Science  Operational and organisational challenges and conflicts  Get started! 5 ways to ensure success

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