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

Building a Successful Enterprise Data Science Capability

ENDA RIDGE, PHD

HEAD OF DATA SCIENCE & ALGORITHMS, UK SUPERMARKETAUTHOR OF “GUERRILLA ANALYTICS – A PRACTICAL APPROACH TO WORKING WITH DATA”

“Data is the new oil”

“The sexiest job of the century”

Copyright Enda Ridge 2017#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge

What I’ve Learned

PhD

‘Design of Experiments

for Tuning Algorithms’

Data mining Softwarepre-sales

Forensic Data Analytics

Senior Manager

Professional Services

Head of Data Science

& Algorithms


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

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


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


We know what Science is

What is Data Science for the Enterprise?

“Data Science is the discipline of understanding processes described by data for the benefit of the business”


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



What is Data Science?



Data Science uses the scientific method

Trying to model our businesses and our customers

Experiments to test hypotheses

Making changes, measuring and observing effects

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

Mature Data Science in the Enterprise

Frame a business hypothesis

Gather and generate data

Analyse

Confirm with experiment


Business change

Data Science Involves Uncertainty


Data

Processes

Questions

Solutions

Data Science Involves New Data


Surveys

Web scrapes

Systems

Logs

3rd party

Data Science involves Varied Activities


Data joins

Visualizations

Algorithm automation

Programming languages

Data Science involves Experiments


Navigating the Enterprise Matrix


MarketingSales / Trading

Logistics Other

IT, InfoSec, Architecture

Product Development

BI & Analytics

HR & Recruitment

Navigating the Enterprise Matrix


Marketing Sales Logistics Other

IT, Information Security, Architecture

Product Development

BI & Analytics

HR & Recruitment

Data Science

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


Challenge #1: Org structure and the customer


Marketing Sales Logistics Other


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

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


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

Challenge #2: Enabling the team



Logistics Other


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

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


‘Lab’

Data store

ServerDev tools

Challenge #3: making insights actionable



Logistics Other


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

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


Challenge #4: Distinction from data community



Logistics Other


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

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


• 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

Challenge #5: Hiring & keeping people



Logistics Other


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

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


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


Building a Successful Enterprise Data Science Capability

Questions, training?

Find me

on Twitter @enda_ridge #GuerrillaAnalytics

on my blog http://guerrilla-analytics.net


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

https://twitter.com/enda_ridge

https://twitter.com/search?q=#guerrillaanalytics&src=typd

http://guerrilla-analytics.net/

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

Data & Analytics

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