building a data science capability in retail
TRANSCRIPT
Guerrilla Analytics: Building a Data Science Capability in RetailENDA RIDGE, PHD#GuerrillaAnalytics http://guerrilla-analytics.net
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2What You Will Learn A Data Science Capability
Why do this? The strategic advantage of a Data Science capability in Retail What do you need? The 3 components of a capability Where do you start? 5 steps to build a capability
How this will help you C-Suite, Directors, Heads:
Understand the vision you’re setting out Know the obstacles you will have to smash down Define milestones and measures of success
Data Scientists: The support you must lobby for Your focus in year 1
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3What I’ve Learned
PhD‘Design of Experime
nts for Tuning
Algorithms’
Boutique Consultanc
y
Forensic Data
Analytics
Senior Manager
Professional
Services
Head of Algorith
ms
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No matter the industry, doing agile data science always faces the same challenge…
2004 2008 2010 2012 2015
Organisations do not have the flexibility to accommodate data science
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4
The Strategic Advantage of Data Science
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5ChallengesHave we changed customer buying behaviour?
Could we tell when our plant will fail?
Can we improve getting stock on shelves?
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6ChallengesWhat are sensible product groupings?
Where do we next locate a store?
What factors really influence dwell time?
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7Problem characteristics
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Complex, interrelated, living systems
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8Problem characteristics Uncertainty
Data Process Questions Solutions
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9Problem characteristics New data, ‘informal’ data sources
Disparate sources Surveys Web scrapes Logs 3rd party
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10Problem characteristics Huge variety of solutions to try out
Data joins Visualizations Algorithms
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11You’re not ready for the factory line
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12What is Data Science?“Data Science is the discipline of understanding and using data
to improve your business”
MathematicsStatistics
Machine learningVisualization
- Enhance products- Find opportunities- Increase efficiency
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13Strategic advantage?Have we changed customer buying behaviour?
Could we tell when our plant will fail?
How do we make our warehouse more efficient?
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Experiment design
Predictive modelling
Operations Research
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14What Data Science is not…
Big Data
traditional Business Intelligence
creating beautiful visualizations just because we can
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https://vimeo.com/88093956
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15Are you doing Data Science?
Frame a business problem
Gather and generate data
AnalyseConfirm with experiment
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Business operations
Data-driven products
Best in class organisations integrate Data Science into everything they do
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16
3 Components of a Data Science Capability
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17Typical mistakes Not knowing how Data Science really
works in the trenches
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18Typical mistakes Not knowing how Data Science really
works in the trenches Expecting magic
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19Typical mistakes Not knowing how Data Science really
works in the trenches Expecting magic Bundling with IT
or isolating from IT
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20Typical mistakes Not knowing how Data Science really
works in the trenches Expecting magic Bundling with IT
or isolating from IT Too much structure / bureaucracy
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http://workplacereport.com/
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213 Components of a Capability
Data Science
Leadership
DataPeople and Technology
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22Component 1: Leadership Set the direction and support it
Changes to BAU Inefficiencies exposed Opportunities to capitalise on
Pitfall: Data Science very difficult Results don’t get used
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Frame a business problem
Gather and generate data
Analyse
Confirm with experiment
Business operation
sData-driven
products
#GuerrillaAnalytics http://guerrilla-analytics.net
23Component 1: Leadership Set targets and measure progress What’s a Data Science KPI?
# of Algorithms in products? Improvements to bottom line? # of Experiments completed? How to cost?
Pitfalls: Whimsical projects Losing business focus
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24Component 1: Leadership Prioritise the pipeline
Pitfalls: No strategic focus
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25Component 2: People & Technology Hype says you need geniuses Reality:
Communication Consulting and Influencing Tenacity Passion
Pitfalls Failure to understand business context Disillusionment at obstacles Cannot answer the ‘so what’?
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26Component 2: People & Technology
What you need Pitfall
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27Component 2: People & Technology Data Science needs technology flexibility Faced with
Overwhelming firewalls Irrational fear of Open Source IT SLAs for server builds Ad-hoc IT support
Pitfalls Premature tech governance Technology dictated from above
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28Component 3: Data Data Scientists need access to your data In the early days
Focus on blockers to access, storage Let the Data Scientists work the data
Pitfalls: Not taking a strategic view on your data Making a data dictionary a pre-requisite Letting security perceptions be an excuse Sticking to outmoded ideas of ‘production
data’
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293 Components of a Capability
Data Science
Leadership
DataPeople and Technology
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• Vision• Smash barriers• Priority targets
• Access• Security• Service Ops
• Coal face• Soft skills• Flexible tech
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30
5 Steps to Build a Capability
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315 Steps
Build a customer base
Assemble the right people
Enable them
Engage and Operate
Work with product development
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32Step 1: Build a customer base Find the low hanging fruit Deliver quick wins Educate the organisation Market the team
Business benefit, business benefit, business benefit…
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33Step 2: Assemble the right people
Data Science
Data Scientists
+Tech
Support+
Enlightened
Customer
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34Step 3: Enable your people
1 Laptops2 Database3 ApplicationServers
Laptops Powerful Elevated privileges Internet access
Database Pick good enough general analytics
database Application Servers
Internet access Plenty of RAM Pick a good enough general analytics
language
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35Step 4: Engage and Operate Simple Engagement model
Short sharp studies When are we done? What does success look like? What Data Science doesn’t do
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36Step 4: Engage and Operate Simple Operating model
Track your projects Simple conventions on data Version control Track deliverables
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37Step 5: Work with product development
Language incompatibility Agile incompatibilities
What’s a Data Science sprint? Influence for Data Science features
Data Scientists have user stories too! Influence for Data Science data
Data Scientists have user stories too!
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38Building a Data Science Capability The strategic advantage of Data Science
finding opportunities, efficiencies and product enhancements in data
3 components Leadership & Targets People and Technology Data
5 steps Build a customer base Gather the right people Enable them Engage and Operate Work with product development
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