building data science teams from scratch (polish business analytics summit, march 2016)

Building Data Science Teams from ScratchENDA RIDGE, PHD

Copyright Enda Ridge 2016

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

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 Leadership: how to set the direction How to enable a team How to fit into the enterprise Practitioners:

The support you must lobby for Your focus 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 Data

Analytics

Senior Manager

Professional

Services

Head of Algorith

ms


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


4

The Strategic Advantage of Data Science



5Typical ChallengesHave we changed customer online behaviour?

Could we tell when our plant will fail?

Can we improve our supply chain?



6Typical ChallengesWhich financial products should we offer?

Where do we next locate a store?

Etc etc



7Problem characteristics


Complex, interrelated, living systems


8Problem characteristics Uncertainty

Data Process Questions Solutions



9Problem characteristics New data, ‘informal’ data sources

Disparate sources Surveys Web scrapes Logs 3rd party



10Problem characteristics Huge variety of solutions to try out

Data joins Visualizations Algorithms



11You’re not ready for the factory line



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



13Strategic advantage?Have we changed customer buying behaviour?

Could we tell when our plant will fail?

How do we make our warehouse more efficient?


Experiment design

Predictive modelling

Operations Research


14Strategic advantage?Which financial products should we offer?

Where do we next locate a store?


Logistic regression Geo queries


15What Data Science is not…

Big Data

Business Intelligence

creating beautiful visualizations just because we can


https://vimeo.com/88093956


16Are you doing Data Science?

Frame a business problem

Gather and generate data

AnalyseConfirm with experiment


Business operations

Data-driven products

Best in class organisations integrate Data Science into everything they do


17

3 Components of a Data Science Capability



18Typical mistakes Not knowing how Data Science really

works in the trenches




works in the trenches Expecting magic




works in the trenches Expecting magic Bundling with IT

or isolating from IT




works in the trenches Expecting magic Bundling with IT

or isolating from IT Too much structure / bureaucracy


http://workplacereport.com/


223 Components of a Capability

Data Science

Leadership

DataPeople and Technology



23Component 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


Frame a business problem

Gather and generate data

Analyse

Confirm with experiment

Business operation

sData-driven

products


24Component 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



25Component 1: Leadership Prioritise the pipeline

Pitfalls: No strategic focus



26Component 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’?



27Component 2: People & Technology

What you need Pitfall



28Component 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



29Component 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’



303 Components of a Capability

Data Science

Leadership

DataPeople and Technology


• Vision• Smash barriers• Priority targets

• Access• Security• Service Ops

• Coal face• Soft skills• Flexible tech


31

5 Steps to Build a Capability



325 Steps

Build a customer base

Assemble the right people

Enable them

Engage and Operate

Work with product development



33Step 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…



34Step 2: Assemble the right people

Data Science

Data Scientists

+Tech

Support+

Enlightened

Customer



35Step 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



36Step 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



37Step 4: Engage and Operate Simple Operating model

Track your projects Simple conventions on data Version control Track deliverables



38Step 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!



39Building 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


building data science teams from scratch (polish business analytics summit, march 2016)

Data & Analytics