how to build successful data team - dataiku ?

How to Build a Successful Data Team ?

Hi ! Self-Introduction

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FlorianDouetteau DATAIKU

Dataiku - Data Tuesday

Meet Hal Alowne

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Big Guys • 10B$+ Revenue • 100M+ customers • 100+ Data Scientist

Hal Alowne BI Manager Dim’s Private Showroom

Hey Hal ! We need a big data platform like the big guys.

Let’s just do as they do!

‟”Average E-commerce Web site

• 100M$ Revenue • 1 Million customer • 1 Data Analyst (Hal Himself)

Dim Sum CEO & Founder Dim’s Private Showroom

Big Data Copy Cat Project

TECHNOLOGY DISCONNECT

4

Welcome to Technoslavia !

5

LOL PLATFORM ANTI-PATTERN

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Test and Invest in Infrastructure == Skilled People or

Go For Cloud / Packaged Infrastructure

YourBrandNewHadoopClusterisperceivedasslow,notsousedandnotreliable

TECHNO MISMATCH ANTI-PATTERN

7

Assume Being Polyglot or

Be a Dictator

VS

VS

ThePythonClan

TheRTribe

TheOldElephantFraternity

TheNewElephantClub

PREDICTIVE ANALYTICS DEPLOYMENT STRATEGY

8

Website2000’winners

Companiesthatwereabletoreleasefast

"ArtificialIntelligencewithDataforInternetofThings"2010’winners

Companiesabletoputintelligenceinproduction

?

Design a way to put “PREDITICTIVE MODELS” IN PRODUCTION

PEOPLE DISCONNECT

9

Classic BI Team Org

Business Leader Data Consumer

Line-of-business Data Consumer

Business Project Sponsor BI Solution Architect

Model Designer

ETL Developer

Dashboard / Report Designer

DBA / IT Data Owner

Specs

Data Science Team Org

Business Leader Data Consumer

Line-of-business Data Consumer

Business Project Sponsor Data Team Manager

Data Engineer

Data Analyst

Data System Engineer / Data Architect

Specs

Data Scientist

Built From Scratch

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Business Leader Data Consumer

Line-of-business Data Consumer

Business Project Sponsor

DBA / IT Data Owner

Specs

Built From Engineering

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Business Leader Data Consumer

Line-of-business Data Consumer

Business Project Sponsor

Specs

Built From Analysts

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Business Leader Data Consumer

Line-of-business Data Consumer

Business Project Sponsor

Specs

Manage Expectations

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Data Plumberer

Data Engineer

Data Scientist

Data Waiter

Data Cleaner

Data Analyst

REALJOB

DREAMJOB

Perfectly Natural Hidden thoughts

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Business Project Sponsor

Data Team Manager

Data EngineerData Analyst

Data Scientist

Managing Extreme Personalities

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Data SCIENTIST

Highly Creative

Passionate

Hard to hire ?

Hard to manage ?

Want to take your job ? Ambitious

Paired for Data

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Data Analyst

Discover Patterns

Data Engineer

Make things work

Fight

data entropy

Entropy tech

entropy

When do you prefer ?

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One Analyst

One Engineer That work together ?

Two data scientists

Data Disconnect

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DATA DISCONNECT

What is the main reason for data project to fail ?

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DATA NOT

AVAILABLE

BUT FOR ONLY INCREMENTAL GAIN

Contribu=ontotheoverallprojectperformance

0% 25% 50% 75% 100%

20%30%50%

BusinessGoalDefinitionandData FeatureEngineering Algorithm

How to Get Data if you don’t have it

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THEGRASSHOPER THESPIDER THEFOX

The Cicada : Optimistic and Opportunistic Data

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THECICADA

As a startup

As a group inside a company

- Build a new product using open data

- Benefit from the data sharing initiative within your company

- Wait for data to be available in your data lake

The Spider: Power of the Network

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THESPIDER

As a startup

As a group inside a company

- Create a network of (web trackers | sensors)

- Make it available for free

- Build your service on people’s collected data

- Make a web service available to collect data

- Promote it internally so that people use it

The Fox: Hunt for the Big Money first

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THEFOX

As a startup

As a group inside a company

- Hunt for a Business Group within a large company with a problem

- Build a SaaS solution using their data

- Replicate to competitors

- Take in a charge a critical problem as per the CEO’s request

- Build your own integrated tech team to solve it

- Use those ressources to reset data services internally

Product Disconnect

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PRODUCT DISCONNECT

What is Big Data about ?

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The Age Of Distributed Intelligence

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Global,PersonalisedandRealTimeDataDrivenServices

Data to Visualize or Data to Automate ?

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2013 2014 2015 2015 2017 2018

AutomatedDecision VIsualizeToDecide

Moving to a world of automated decision making

Where is your added value ?

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IstheproblemattheCoreofmyBusinessProcess?

Isitacommonproblem/withsharedata?

GoforBestofBreedSAASSolution

CanISolveitonmyown?

Really?

Buildbythedatateam

Buildbythedatateam?

Buildbythedatateam

HireConsultantsandLearn

Yes

Yes No

I can’t Ok, I can try

Yes!

No!

No

Be aware of the confort zone

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Mission Critical

Small Structured

Large Diverse

Sheer Curiosity

Reporting for Financein Any Industry

Analyze Each Tweet

Web Navigation For E-Merchant

Ticket DataFor Discountsin Retail

Phone Call Logs for Security

RTB Data For Advertising

Customer Consumption For Anti-Churn in Utilities

Optimization

FilingsFor Fraud in Insurance

Not EnoughData To Learn From ?

Not Enough“Hard" Examples So that you can learn

Infuse the Data and Try Mindset

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BrendanSternisnowaSpecialistforDataScienceinHealthcareatDataiku

“ When I was 20, as I was working as a manager at my Starbucks shop, I realised that I could probably enhance the amount

of sales for ground coffee. Depending on the day and time of days, I kept moving around the ground coffee. I manage to made some

A/B tests that optimised the average sale amount by 12%”

Involve product team

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ProductFeaturePersonalisedItemRanking

ProductFeatureNotifyUserOnlywhenNeeded

ProductFeature:HistoricalDataForPathOptimisation

Have Product Management Deeply Involved In the Data Team

Create an "API" Culture

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Do not share • Random Piece of Code • Flat File

Do share • Reproductible documented workflows • Clean, documented APIs

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WAITING FOR QUESTIONS SLIDE WAITING FOR QUESTIONS SLIDE

Morefoodforthoughts onDataiku’sblog

http://www.dataiku.com/blog/

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@fdouetteau  @dataiku