data science in retail - growing your capability (keynote at polish data science in business 2016)

Data Science in Retail: Growing your Capability for Strategic AdvantageENDA RIDGE, PHDHEAD OF ALGORITHMS, UK FMCG

“Data is the new oil”“The sexiest job of the century”

Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net

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Science

We know what the scientific method is

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4What is Data Science for the Enterprise?

“Data Science is the discipline of discovering data and understanding data to find opportunities, efficiencies and improvements”

Copyright Enda Ridge 2016


5What is Data Science?

Opportunities in new data sources, new products, new understanding

Efficiencies in automation, process changes, organisation change Improvements in product features, product offerings




6What is Data Science?



Data Science uses the scientific method Experiments to test hypotheses Rigorous measurement Making changes and observing effects


7High maturity organisations

Frame a business hypothesis

Gather and generate data

AnalyseConfirm with experiment


Data-driven operations

Data-driven products

What Data Science is not…

Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge

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https://vimeo.com/88093956


9Data Science Involves Uncertainty


Data Processes Questions Solutions


10Data Science Involves New Data


Surveys Web scrapes Systems Logs 3rd party


11Data Science involves Varied Activities


Data joins Visualizations Algorithms Languages

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12Data Science involves Experiments


Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net

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17The Enterprise Matrix


Marketing Sales / Trading Logistics Other

IT, InfoSec, ArchitectureProduct Development

BI & AnalyticsHR & Recruitment


18The Enterprise Matrix





Data Science


195 Challenges for Data Science

Org structure & the customer

Enabling the team

Making insights actionable

Integration with the data community

Getting and keeping people



20Challenge #1: structure and customer





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


21Action #1: Chief Data Scientist (CDS) Centre of Excellence A Senior Advocate Business Side Clear Engagement

Model ‘Ready’ ‘Done’ Pipeline


Project

Project

Project

CDS


22Challenge #2: Enabling the team





Data Science

You need Data and Technology to do Data Science

But Tradition of control Generally slow Incentivised to maintain

status quo


23Action #2: build tactical environment Then build out

Scale Permission groups Proxy access Local admin rights Licencing Tech Support Data governance Data feeds


Lab

Data store

Applications

Dev tools


24Challenge #3: making insights actionable





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


25Action #3: Operating Model

Realistic middle ground Use version control Create ‘integration tests

cases’ Create Data Science

user stories Keep algorithms simple See ‘Guerrilla Analytics’


Lab Factory

Interfaces


26Challenge #4: data community





Data Science

You need access to dataYou need customers

But Gatekeepers Perceived threat Rebranding Confusion with customer

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 2016#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge

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28Challenge #5: Getting & keeping people





Data Science

You need key hires and the market is competitive

But Existing pay structures Existing job formats Hiring agencies


29Action #5: Prioritised Hires Less genius, more resilience

and practicality Keep them focused Clear progression structures Establish training budget



305 Challenges for Data Science

Org structure & the customer

Enabling the team

Making insights actionable

Integration with the data community

Getting and keeping people



31Data Science A practical definition of Data Science and

how retail can benefit Operational and organisational challenges

and conflicts Get started! 5 tips to proving your worth now

Find me on Twitter @enda_ridge #

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://guerrilla-analytics.net


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data science in retail - growing your capability (keynote at polish data science in business 2016)

Data & Analytics