From Predictive Models to Production Apps - Inspire 2017

FROM PREDICTIVE MODELS TO PRODUCTION APPSPresented by Austin Ogilvie

September 12, 2017

FORWARD-LOOKING STATEMENTS This presentation includes “forward-looking statements” within the meaning of the Private Securities Litigation Reform Act of 1995. These forward-looking statements may be identified by the use of terminology such as “believe,” “may,” “will,” “intend,” “expect,” “plan,” “anticipate,” “estimate,” “potential,” or “continue,” or other comparable terminology. All statements other than statements of historical fact could be deemed forward-looking, including any projections of product availability, growth and financial metrics and any statements regarding product roadmaps, strategies, plans or use cases. Although Alteryx believes that the expectations reflected in any of these forward-looking statements are reasonable, these expectations or any of the forward-looking statements could prove to be incorrect, and actual results or outcomes could differ materially from those projected or assumed in the forward-looking statements. Alteryx’s future financial condition and results of operations, as well as any forward-looking statements, are subject to risks and uncertainties, including but not limited to the factors set forth in Alteryx’s press releases, public statements and/or filings with the Securities and Exchange Commission, especially the “Risk Factors” sections of Alteryx’s Quarterly Report on Form 10-Q. Thesedocuments and others containing important disclosures are available at www.sec.gov or in the “Investors” section of Alteryx’s website at www.alteryx.com. All forward-looking statements are made as of the date of this presentation and Alteryx assumes no obligation to update any such forward-looking statements.

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AGENDA•Quick overview

•Challenges in deploying models

•Solution

•Customers

•Demo

PRESENTER

To watch a recording of this session from Inspire Europe 2017, visit

alteryx.com/inspire-europe-2017-tracks

OVERVIEW

Founded 2013Headquarters in NYC

We help data teamsbuild & deploy apps

You may knowus from

THE PROBLEM

Making data science actionable is challenging & expensive.

• Lack of understanding the benefits

• Lack of trust in effectiveness

• Technical complexity to

operationalize

• No way to measure ROI

MODEL DEPLOYMENT METHODS

Interactive Dashboards Real-time ApplicationsReports

MODEL DEPLOYMENT METHODS

Interactive Dashboards Real-time ApplicationsReports

W H A T I S A P R E D I C T I V E A P P L I C A T I O N ?

Data-Driven Apps

Oscar Health

InsuranceInsurance

UberTransportation &

Logistics

TurboTaxAccounting

http://www.informationweek.com/big-data/big-data-analytics/big-data-success-remains-elusive-study/d/d-id/1318891

DATA SCIENCE VALUE CHAIN

Apps that reach

customers and front-line

employees operationally

are more valuable than

static reports

D A T A S C I E N C E I S A B O U T P R A C T I C A L , R E A L - W O R L D S O L U T I O N S

Carl wants to watch a good movie.

Hey, Carl. Check these out!

E X P L A N A T I O N I S N ’ T A L W A Y S I M P O R T A N T

Sarah builds the algorithm.

Carl would like Frozen because Cindy liked it.

Movie

1

Movie

2

Movie

3

Movie

4

Movie

5

Movie

6

Movie

7

Movie

8

Movie

9

Movie

10

… Movie

17770

User 1 1 2 3

User 2 2 3 3 4 ?

User 3 5 3

User 4 2 3 2 2

User 5 2 3 5 4 2 4

User 6 2

User 7 2 4 2

User 8 3 1 3 4 5 4

User 9 3

User 10 1 2 2

…

User 480189 4 3 3

Carl

Cindy

http://courses.washington.edu/css490/2012.Winter/lecture_slides/08b_collaborative_filtering_1_r1.pdf

E X P L A N A T I O N I S N ’ T A L W A Y S I M P O R T A N T

Sarah builds the algorithm.

>

PROBLEM

Business ProblemEvaluate Available

DataRequest Data

Access from ITRequest Compute Resources from IT Negotiate with IT for

Requested ResourcesWait for Resources to be Provisioned

Install Languages & Tools

Configure Connectivity, Access,

& Security

RAM/CPU Availability, Scalability, Monitoring

Request Network Config Change

Request to Install Another Package

Model BuildingCompose a

Powerpoint to Share Results

Edit Team Wiki to Document Your

Work

Negotiate with Product on Model Deployment

Timeline

Wait for Engineering to Implement the Model

Test Newly Implemented Model to

Ensure Valid Results

Request Modifications to the Model due to

Unexpected Results

Release the Model to Production

Document Release Notes and

Deployment Steps

Prepare for Change Management

DATA SCIENCE TEAMS FACE A MYRIAD OF CHALLENGES AT EVERY STEP OF THE WAY

Deployments take 12-20 weeks

Cost to deploy 1 model runs in excess of $250,000

< 10% of models make it to production

Your ApplicationsData Scientists Developers

Your Customers

Write more custom code to integrate Customer benefitsPainstakingly rewrite models into other languagesBuild a model in R or Python

SOLUTION

SOLUTION

Business ProblemEvaluate Available

DataRequest Data

Access from ITRequest Compute Resources from IT Negotiate with IT for

Requested ResourcesWait for Resources to be Provisioned

Configure Connectivity, Access,

& Security

RAM/CPU Availability, Scalability, Monitoring

Request Network Config Change

Request to Install Another Package

Model BuildingCompose a

Powerpoint to Share Results

Edit Team Wiki to Document Your

Work

Negotiate with Product on Model Deployment

Timeline

Wait for Engineering to Implement the Model

Test Newly Implemented Model to

Ensure Valid Results

Request Modifications to the Model due to

Unexpected Results

Release the Model to Production

Document Release Notes and

Deployment Steps

Prepare for Change Management

Install Languages & Tools

DATA SCIENTISTS NEED A WAY TO MANAGE THEIR PROJECTS FROM END-TO-END

•Build models in Python and R

• Instant model APIs

•Manage models

•Scale without IT

PROMOTE

ALTERYX + PROMOTE

CUSTOMERS

F e r r a t u m B a n k u s e s S c i e n c e O p s t o

m a k e r e a l - t i m e c r e d i t d e c i s i o n s .

• C r e d i t S c o r e s

• F r a u d C h e c k s

• K Y C

• L i n e A s s i g n m e n t

• R i s k - b a s e d P r i c i n g

• Propensity-to-buy

• Cross- and upsell opportunities

Tendril helps solar, smart thermostat, and other

energy providers target the right homeownersc

Results

• 4x faster time to market• Self-sufficient analytics team• $350,000 saved

DEMO

•Yhat joined Alteryx June 2017

•Easily productionize models

•Model management, made easy

WRAP UP

THANK YOU

Please complete a feedback survey

281-330-8004 | aogilvie@alteryx.com

Austin Ogilvie

#inspire16#

alteryx.com/trial

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