To s u c c e e d i n t h e w o r l d ’s r a p i d l y e v o l v i n g e c o s y s t e m , c o m p a n i e s ( n o m a t t e r w h a t t h e i r i n d u s t r y o r s i z e ) m u s t u s e d a t a t o c o n t i n u o u s l y d e v e l o p m o r e i n n o v a t i v e o p e r a t i o n s , p r o c e s s e s , a n d p r o d u c t s . T h i s m e a n s e m b r a c i n g t h e s h i � t o E n t e r p r i s e A I , u s i n g t h e p o w e r o f m a c h i n e l e a r n i n g t o e n h a n c e - n o t r e p l a c e - h u m a n s .

B U S I N E S S E ST H AT L E V E R A G EE N T E R P R I S E A I :

C O N N E C TT E C H + S M E S

1 .

B r i n g a l l p e o p l e , f r o m b u s i n e s s p e o p l e t o a n a l y s t s a n d d a t a s c i e n t i s t s , t o g e t h e r. T h i s h a p p e n s v i a h o r i z o n t a l ( t e a m - w i d e ) a n d v e r t i c a l ( c r o s s - t e a m ) c o l l a b o r a t i o n .

O P E R AT I O N A L I Z EM A C H I N EL E A R N I N G

3 .

G e t m o d e l s o u t o f a s a n d b o x e n v i r o n m e n t a n d i n t o p r o d u c t i o n f o r r e a l r e s u l t s .

B U I L D F O RT O M O R R O W

4 .

F o c u s o n s h o r t - t e r m s u c c e s s f u l p r o j e c t s a n d a t t h e s a m e t i m e a l o n g - t e r m e n t e r p r i s e t r a n s f o r m a t i o n s t r a t e g y .

E M B R A C ES E L F - S E R V I C E

2 .

E n a b l e s e l f - s e r v i c e a n a l y t i c s b y c r e a t i n g t h e t o o l s f o r d a y - t o - d a y a n a l y s i s a n d a g i l e u s e o f d a t a f o r a n s w e r s f r o m t h e g r o u n d u p .

Y O U ’ V E G O TC O M PA N Y

T h e p a t h t o E n t e r p r i s e A I i s e a s i e r w i t h a c o m m u n i t y o f p r o f e s s i o n a l s w h o h a v e b e e n t h e r e b e f o r e . D a t a i k u p a r t n e r s w i t h t h e b e s t i n t h e e c o s y s t e m a n d p r o v i d e s u n p a r a l l e l e d s u p p o r t a n d r e s o u r c e s t o e n s u r e i t s c u s t o m e r s s t a y o p e n , c u t t i n g - e d g e , a n d a g i l e .

F R O M R A W D ATA T OB U S I N E S S I M PA C T

Netezza

Test

Vertica

Train

HDFS_Avro Joined_Data

HDFS_Parquet

Cassandra

Oracle

Teradata

2

2

Amazon_S3

Test_Scored

MLlib_Prediction

Network_dataset

D a t a i k u i s t h e c e n t r a l i z e d d a t a p l a t f o r m t h a t m o v e s b u s i n e s s e s a l o n g t h e i r d a t a j o u r n e y f r o m a n a l y t i c s a t s c a l e t o e n t e r p r i s e A I , p o w e r i n g s e l f - s e r v i c e a n a l y t i c s w h i l e a l s o e n s u r i n g t h e o p e r a t i o n a l i z a t i o n o f

m a c h i n e l e a r n i n g m o d e l s i n p r o d u c t i o n .

Braund, Mr. Owen Harris

Moran, Mr. James

Heikkinen, Miss. Laina

Futrelle, Mrs. Jacques Heath

Allen, Mr. William Henry

McCarthy, Mr. Robert

Hewlett, Mrs (Mary D Kingcome)

22

38

26

35

35

29

54

male

male

female

female

male

male

male

female

female

female

male

female

female

female

male

female

female

male

Natural lang. IntegerGender

Name AgeSex

Remove rows containing Mr.

Keep only rows containing Mr.

Remove rows equal to Moran, Mr. James

Keep only rows equal to Moran, Mr. James

Clear cells equal to Moran, Mr. James

Filter on Moran, Mr. James

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CLEAN& WRANGLE1

• P e r f o r m m a s s a c t i o n s o n d a t a i n a s i m p l e , i n t e r a c t i v e e n v i r o n m e n t .

• L e v e r a g e a r i c h l i b r a r y o f b u i l t - i np r o c e s s o r s f o r a d v a n c e d a n d c u s t o m p r o c e s s i n g .

FROM RAW DATA TO BUSINESS IMPACT

BUILD + APPLYMACHINE LEARNING2

• U s e s t e p - b y - s t e p g u i d e d m a c h i n e l e a r n i n g p o w e r e d b y s t a t e - o f - t h e - a r t m a c h i n e l e a r n i n g l i b r a r i e s ( S c i k i t - L e a r n , M L l i b , X G b o o s t , e t c . ) .

• C u s t o m i z e c o d e d i r e c t l y i n P y t h o n a n d R f o r a d v a n c e d c u s t o m m a c h i n e l e a r n i n g .

MINING& VISUALIZATION3

• G e t i m m e d i a t e v i s u a l i n s i g h t s w i t h b u i l t - i n c h a r t f o r m a t s o r g o c u s t o m w i t h i n t e r a c t i v e P y t h o n , R , a n d S Q L n o t e b o o k s .

• M i n e a t s c a l e w i t h S p a r k & H a d o o p .

11/094/09 18/09 25/09 01/10 08/10 15/10

400

600

800

706

1k

1.2

1.4

Size

200

Range �2016-01-22 / 2016-01-23

21/09

Size946

RUNTIME

06 PM 03 PM 06 PM09 PM Fri 11 03 AM 06 AM 09 AM 12 PM03 PM

ACTIONS �

Refresh dashboard failed 21:35

Marketing reports 21:35

Check flow warning 21:34

Pipedrive reports 21:32

Base processing 21:32

Refresh dashboard 21:32

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MONITOR& ADJUST5

• R u n m u l t i p l e v e r s i o n s o f t h e s a m e m o d e l a t t h e s a m e t i m e f o r a u t o m a t e d A / B t e s t i n g .

• A c c e s s h i s t o r y o f l o g s q u e r i e s a n d p r e d i c t i o n s a t a n y t i m e t o c h e c k t h a t m o d e l p e r f o r m a n c e i s n o t d r i � i n g w i t h t i m e .

Image Recognizer v1DeploymentId

StaticTest (static)Updated 3 days ago

tag:normal tag:normal tag:normal

tag:normal

• D e p l o y m o d e l s i n o n e c l i c k o n t h e c l o u d w i t h K u b e r n e t e s .

• H a n d l e l a r g e q u a n t i t i e s o f r e a l - t i m e p r e d i c t i o n s w i t h q u e u i n g , p a r a l l e l i s m , a n d l o a d b a l a n c i n g f o r a s c a l a b l e a n d h i g h l y a v a i l a b l e s o l u t i o n .

DEPLOY TOPRODUCTION4

A S I N G L E E N T E R P R I S E A I P L AT F O R M F O R E V E R YL I N E O F B U S I N E S S

I N C R E A S E R E V E N U EM ar ket i ng i s one of the wide st uses cases i n t he ma chine learning and AI s pace w i t h t he largest potent ial for q u ick ROI . Churn pre dict ion, market ing attr ibut i on, more accurate lead scor ing, m achi ne -opt i mize d market ing mix , p e rs o na l i zed o�e rs and ads, automated p r ic ing opt i mi zat ion, more re le vant p ro d uct recommendations, b ette r cu stomer suppo rt - the sky’s the l imit .

D E C R E A S E C O S T SFrom predi ct i ve maintenance to supply chain opt imi zat i on a nd e ve r ything in b etween, the d ay-to -da y operat ions of a company are pr ime m ater i a l for le ve raging machine learning to f ind n e w e�i c i enc i es. Introducing AI in the en terpr i se for more e �ic ient operat ions f rees up sta� for more engaging and creat ive work that can br i ng more value to the b usiness.

B R I N G A CO M P E T I T I V E E D G ECompanies that want to get ahead and be on the cutt ing edge of innovat i o n wi l l do i t with machine learning and AI , which can al low for faster i terat i o n i n R&D or data mining and explorat i o n fo r brand ne w products and o�erings .

M I T I G AT E R I S KThe cost of not being compliant when i t come s to using data is extraordinar y. Data governance has ne ver been more important , including having a central place to access a l l data, logged and monitored use, and a ful l v ie w on exact ly what data is being used and where.

For Everyone in the Data-Powered OrganizationDataiku is the data platform that brings ease and e�iciency to

everyone in the data-to-insights process, including:

IMAGES/TEXT/VOICE

HADOOP

ENTERPRISE DATA WAREHOUSE

NO SQL

CLOUD

ON PREMISE

AI ENABLED SERVICES

INSIGHTS

MODELS

DATASCIENTIST

DATAENGINEER

ANALYTICSLEADER

BUSINESSANALYST

Use languages and tools you already know and love, but with extra added e�iciency.Reduce repetitive work and easily introduce automation.Spend less time on tedious tasks (like data prep) where analysts can step in.Easily operationalize data science projects (without relying on other teams).

DATASCIENTIST

IMAGES/TEXT/VOICE

HADOOP

ENTERPRISE DATA WAREHOUSE

NO SQL

CLOUD

ON PREMISE

AI ENABLED SERVICES

INSIGHTS

MODELS

Access and work with all data from a central location.Use a powerful visual interface for faster and more e�icient data preparation.Experiment with more advanced features (like machine learning) in a sandbox environment.

BUSINESSANALYST

IMAGES/TEXT/VOICE

HADOOP

ENTERPRISE DATA WAREHOUSE

NO SQL

CLOUD

ON PREMISE

AI ENABLED SERVICES

INSIGHTS

MODELS

ANALYTICSLEADER

Quickly operationalize data projects.Communicate data insights easily with stakeholders via shareable dashboards and visualizations. Maximize e�iciency of both technical and non-technical team members.Scale your team through reusability and automation.

IMAGES/TEXT/VOICE

HADOOP

ENTERPRISE DATA WAREHOUSE

NO SQL

CLOUD

ON PREMISE

AI ENABLED SERVICES

INSIGHTS

MODELS

DATAENGINEER

Control access to data in onecentral location. Automate model deployment. Manage changes and rollback with ease.Use languages and tools you already know and love, but with extra added e�iciency.

IMAGES/TEXT/VOICE

HADOOP

ENTERPRISE DATA WAREHOUSE

NO SQL

CLOUD

ON PREMISE

AI ENABLED SERVICES

INSIGHTS

MODELS

Employees & O�ices

150+ employees among o�ices in New York(headquarters), Paris, London, and Munich.

Funding

December 2014, €3 million, Alven Capital, and Serena CapitalOctober 2016, $14 million Series A led by FirstMark Capital

September 2017, $28 million Series B led by Battery Ventures (along with FirstMark Capital, Alven Capital, and Serena Capital)

Analysts

For the second consecutive year, Dataiku was named a Visionary in The Gartner 2018 Magic Quadrant for Data

Science and Machine-Learning Platforms.

Source: Carlie J. Idoine, Peter Krensky, et al., 22 February 2018. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its a�iliates in the U.S. and internationally, and is used herein with permission. All rights reserved.