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
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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
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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.