enterprise data science (chief data scientist forum 2016)

Enterprise Data Science:Navigating the MatrixENDA RIDGE, PHDALGORITHMS LEAD, UK FMCG

Copyright Enda Ridge 2016

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

2What 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


3What I’ve Learned

PhD‘Design of Experime

nts for Tuning

Algorithms’

Boutique Consultanc

y

Forensic Analytics Manager

Senior Manager

Professional

Services

Head of Algorith

ms


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


4What is Data Science?

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






Opportunities in new data sources, new products, new understanding

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





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


7What Data Science is not…


https://vimeo.com/88093956



Big Data





Big Data

Business Intelligence & Analytics





Big Data

Business Intelligence & Analytics

Playing around with data




11Uncertainty Data Process Questions Solutions



12New Data Disparate sources Surveys Web scrapes Logs 3rd party



13Variety Data joins Visualizations Algorithms Languages



14High maturity Enterprise Data Science

Frame a business hypothesis

Gather and generate data

AnalyseConfirm with experiment


Data-driven operations

Data-driven products


15The Enterprise Matrix


Marketing Sales / Trading Logistics Other

IT, InfoSec, ArchitectureProduct Development

BI & AnalyticsHR & Recruitment


16The Enterprise Matrix





Data Science


175 Challenges in Enterprise Data Science

Org structure & the customer

Enabling the team

Making insights actionable

Integration with the data community

Getting and keeping people



18Challenge #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


19Action #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


Project

Project

Project

CDS


20Challenge #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


21Action #2: build tactical environment Quick and simple ‘within the walls’


Lab

Data store

Applications

Dev tools


22Action #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


Lab

Data store

Applications

Dev tools


23Challenge #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


24Action #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’


Lab Development

Interfaces


25Challenge #4: data community





Data Science

You need access to data You need customers

But Gatekeepers Perceived threat Rebranding Confusion with customer


26Action #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)



27Challenge #5: Getting & keeping people





Data Science

You need key hires and the market is competitive

But Existing pay structures Existing job formats Hiring agencies


28Action #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


X


29Enterprise 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


https://twitter.com/enda_ridge

https://twitter.com/enda_ridge

http://guerrilla-analytics.net/

enterprise data science (chief data scientist forum 2016)

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