Be Heroic

Analytics. For Anyone.

Turn Data into Action

2

Analytics on big data is no longer just a

competitive advantage.

It’s a Business Requirement.

Progressive businesses must accelerate time-to-value not only to thrive, but survive.

3

Unlike traditional analytics providers, RapidMiner enables anyone to make the

most of all data in all environments,creating a powerful advantage from the

wisdom of over 250,000 users.

RapidMiner is the industry's easiest-to-use

Modern Analytics Platform that

significantly accelerates productivity – from data prep to predictive action.

Built by data scientists for data scientists, businesses analysts, and developers.

4

The Analytics Spectrum

Count

Mean

SQLAnalytics

DescriptiveStatistics

DataMining

Predictive Analytics

Simulation Optimization

Univariate distribution

Central tendency

Dispersion

Association rules

Clustering

Feature extraction

Classification

Regression

Time series

Text

Spatial

Machine learning

Monte Carlo

Agent based modeling

Discrete event modeling

Linear optimization

Non-linear optimization

Business Intelligence Advanced Analytics

5

MModern

Key Trends & Drivers in Modern Analytics Market

Forces Internet of Things Consumerization Mass Personalization

Business Accelerate time-to-value Maximize business value Simplify getting to value

Technology Big Data New compute engines Cloud

6

TTraditional

Evolving Advanced Analytics Market

MModern

Limitations• Limited handling of variety of data source• Legacy compute engines• On-premises, if not offline

Limitless• Big Data• New compute engines• Cloud

7

TTraditionalMModern

Advanced Analytics Market Maturity

Lagging innovation

High-velocity innovation

8

Traditional vs. Modern Analytics Market

Magic Quadrant forAdvanced Analytics PlatformsFebruary 2014

Challengers Leaders

Niche players Visionaries

Completeness of vision

Ab

ility

to

exe

cute

Revolution Analytics

Alteryx

RapidMiner

IBMSAS

StatSoft

SAPAngoss Knime

Actuate

InfoCentricity

Microsoft Alpine

Data

Labs

Megaputer

Oracle FICO

9Data Science Skills Gap in 2018 (McKinsey 2012)

Skills Gap in the Modern Analytics Market

MathDomain

Expertise

ComputerScience

Data ScientistStatistician

Actuarial

Quant

+Searching for UnicornsMcKinsey projects by 2018 there will be a

shortage of 1.7M professionals with

analytics expertise in the U.S. alone.

++

+

10

Unlocking Value with Modern Analytics

MathDomain

Expertise

ComputerScience

Business Analysts

Next Generation Data Scientist

(aka: the hero you are looking for)Data Scientist

Statistician

Actuarial

Quant

++

+

+

11

Enter RapidMiner. Analytics. For Anyone.

Accelerate

Pre-Built Models

One-Click Deployments

Connect

All Data

All Environments

Simplify

Code Free

Wisdom of Crowds

12

Wisdom of CrowdsHow do we create data science heroes?

Store them in aknowledge baseof analytic best practices

Anonymously collect analytic modelsfrom analysts across theenterprise

Use machinelearning algorithmsto recommend and

empower any user at any skill level to become a

data science hero

1

2

3

13

RapidMiner Modern Analytics Platform

RapidMiner StudioCode free design your analytics using 1500+ operators

RapidMiner RadoopPush down computations to where your data lives

RapidMiner StreamsAnalyze streaming data while in motion

RapidMiner CloudElastic compute environment for high performance analytics

RapidMiner ServerEnterprise analytics environment for integration with business processes

Orchestrate

Design

Compute

Business Analysts Data Scientists

Consume

Machine

Business Users

Web App

Custom App

BizApp

VizBI

Studio

Code Free GUI Engine

Engine

Server

Engine Engine Web Services API

In-Memory In-DatabaseIn-Hadoop In-Stream

Streams

Engine

Studio

Engine

Radoop

Engine

Cloud

Engine Engine

14

RapidMiner Radoop Architecture

Hadoop environment

Impala(In-memory SQL)

Mahout(Machine Learning)

Pig(Scripting)

HDFS

YARNMapReduce

Hive(SQL)

PR

OG

RA

MM

ING

CO

DE

VIS

UA

LD

EVEL

OP

MEN

T

Data Integration

Data PrepModel

BuildingModel

ValidationModel Scoring

Radoop

Studio Server

Spark(MLib)

Data Discovery

Code free designin RapidMiner

with 70+ Operators

Optimized distributed execution in Hadoop

environment

One-click push down to Hadoop environment

15

VIS

UA

LD

EVEL

OP

MEN

T

Data Integration

Data PrepModel Scoring

Streams

Studio Server

Apache Storm cluster

Node

Engine

Message broker

Apache Kafka

Amazon SQS

or

Application

Cassandra MongoDB SOLRApplication

push pull

pull

storeNode

deploy processas topology

monitor andmanage

Spout Bolt

BoltBolt

Redis

Storm Topology

Node

Node

Node

Engine

Streams

Storm Topology

Bolt

Bolt

RapidMiner Streams Architecture Code free designin RapidMiner leveraging

1500+ Operators

Distributed execution in Storm environment

One-click push down to Storm environment

16

RapidMiner Server ArchitectureWeb Services APIIntegrate analytic models into any type of applicationRESTful API

App DesignerBuild web based predictive reportsCreate ad hoc reportsBuild predictive apps without codingEmbed app into business process

Shared RepositoryCollaborate on analytic processesShare data, processes and models

User ManagementCreate and manage users, roles and access rights

SchedulerExecute analytics at certain times or automate repeating execution patterns

EngineHigh-performance compute engine for distributed and remote work

Server

Viz

Web Services API

App Designer

Shared Repository

User Management

Engine

BI

Custom AppBiz App

MachineWeb App

Scheduler

17

RapidMiner

RapidMiner Modern Analytics Flow

Data Sources

• Work with any data, from any source

Compute Engines

• Work in any environment, at any time

Model Building &

ScoringModel

Deployment

• Deploy models any way you want

Model Consumption

• Embed your insights and take action

• Data Integration, Discovery & Preparation

• Model Building, Validation & Scoring

18

One Platform To Rule Them AllModel Building and Scoring (1500+ operators, 200+ community contributed operators)

Data Integration Data Discovery Data Preparation Model Building Model Validation Model Scoring

• 50+ data connectors withaccess to 100+ sources including 40+ file types

• Any data type• Structured • Semi-structured • Unstructured • Binary• 700+ data parsing,

data blending, data cleansing, transformations, aggregations, set operations, rotations ,filtering, outlier detection, value type transformations, feature creation, window functions, feature extraction

• 20+ process control structures

• 25+ interactive data visualizations including:• Data tables• Scatter matrices• Bubble charts• Parallel

coordinates• Deviation plots• 3-D scatter plots• Density plots• Histograms• Survey plots• Andrews curve• Quartile• Pareto charts• Network & tree

visualizations

• 30+ image format exports

• 45+ featureselection –automatic & manual

• 20+ missing value replacement & Imputation

• 96+ feature creation –automatic & manual

• 20+ anomaly & outlier detection

• 60+ dimension reduction / feature selection

• 20+ segmentation & clustering

• 80+ processing & feature extraction from unstructured data

• 25+ statistical• 250+ machine

learning• 200+ association

mining, frequent item set, similarity computation, feature weighting

• 10+ ensemble and hierarchical models

• 10+ model and parameter optimization

• Automatic model fitting

• Integration of 3rd

party analytics, optimization solvers or simulations tools

• 10+ crossvalidation

• 20+ visualevaluation

• 30+ numerical / nominal / categorical model performance criteria

• 10+ significancetests

• 5+ optimal threshold cutofffor binomialclasses

• 5+ clusterperformancemeasures

• Model scoring for all applicablemodel building

Add’l Analytics

• 50+ text analytics• 15+ web mining• 30+ image / audio

/ video mining• 85+ time series• 30+ financial &

economics

19

Data Sources (access to 450+ data sources)

Flat Files

Text Files

Databases via JDBC or ODBC

Database & Cube Queries

Hadoop Sources

NoSQL database

Cloud Data Sources

Web Services

Mail Services

Work with Any Data, from Any Source

MDX

Web pages Web services

POP3 IMAP

Logos & icons represent a partial list. Full list available upon request.

20

Compute Engines (in-memory, in-SQL, in-database, in-cluster, in-Hadoop, in-Cloud, in-stream)

In-memory

In-SQL & In-database

In-cluster

In-Hadoop

In-Cloud

In-stream

Work in Any Environment, at Any Time

Logos & icons represent a partial list. Full list available upon request.

21

Deploy Models Any Way You Want

Model Deployment

Model Scheduling • Scheduled model execution

Model Publishing

Publishing model results via web services API into:• Web services• Web application• Business application (ERP, CRM, Marketing Automation, etc.) • Machines• Streaming application• Rule engines• Complex event processing• Business intelligence • Data visualization• Cloud application• Custom application

Model Embedding

Embedding of model via Java API into:• Any application • Callable from any application

Model Export • PMML export

22

Model Consumption

Business Intelligence

CRM

Marketing Automation

Cloud Applications (connecting to 300+ cloud services)

ERP

Custom Custom web applications, web portals

Embed Your Insights and Take Immediate Action

Logos & icons represent a partial list. Full list available upon request.

23

Get to Meaningful Business Value in a Snap

AccelerateDrop development time from

days to minutes

ConnectAutomate data

integration

SimplifyMake data science

accessible to all

24

Design Your Analytics. Coding Not Required.

Liberate your business analysts with a code free

environment

Leverage the wisdom of over 250,000 users

worldwide

Supercharge your results with +1500 analytic operators

Boost your data science knowledge

with interactive help

25

Machine Learning on Hadoop

? !Pushing data prep and machine learning into Hadoop clusters is complex and requires coding.Not an viable option!

Push computations intoHadoop clusters from acode free environment.Heroes use RapidMiner!

How do we become big data heroes?

!?

26

Use Case Example: Churn Prevention with HadoopTask: Separate loyal customers from customers who are likely to churn.

Solution with Hadoop + Mahout + (a lot of) custom coding

Disconnected individuals get bogged down in endless process, coding and queries.

In the meantime, your competition beats you to the punch.

1. Define a schema and create tables for customer data, past transactions, service usage log

files, and so on. Manually list columns, types, defining separator characters, etc.

2. Write HiveQL queries (or Pig scripts or other code) to aggregate transactions and service

logs for each customer and calculate attributes describing them

3. Implement and execute a custom MapReduce job to convert data to Mahout’s input format

4. Run the Mahout Naïve Bayes algorithm with proper parameters from the command line

5. Repeat each step for the customers you want to apply the model on

6. Implement and execute a custom MapReduce job to convert predictions back into a

delimited format

7. Export the result from HDFS

8. Import the result into an RDBMS

DAY 3

DAY 12

DAY 18 TIME: 3 WEEKS

DAY 1

27

Task: Separate loyal customers from customers who are likely to churn.

Solution with RapidMiner

Use Case Example: Churn Prevention with Hadoop

Your team designs the process in collaboration with each other just like they would on a white board. And then you press “play”. That’s it.

Combine data from Hadoop and any

traditional source

Apply modelin RapidMinerand integrate

seamlessly

Train model in distributed

Hadoop cluster

12 3

TIME: 10 MINUTES

28

performance increases of up to

RapidMiner Radoop consistently delivers

4,000%compared to pure scripting approaches*

*RapidMiner results compared against traditional Hadoop approaches including data integration, data prep, modeling, deployment and maintenance.

29

RapidMiner Fills In The Skills Gap

MathDomain

Expertise

ComputerScience

Business AnalystsNext Generation Data Scientists

(this is the realm of heroes)

Data ScientistStatistician

Actuarial

Quant

++

+

+

30

Companies Around The World Use RapidMiner

Oil & Gas, Chemicals

Retail

Software & Analytics

Consumer Products

Business Services

Pharma & Healthcare

Manufacturing

Aerospace

Technology

Entertainment

Consulting

Government & Defense

AcademiaFinancial Services

31

Signature Customers

32

Data Science Hero Spotlight “Business executives, who hold

the power to allocate text analytics resources, are

beginning to see and realize the benefits to help better focus

and solve business problems.”

-- Han-Sheong LaiDirector of Operational Excellence & Customer

Advocacy

Process Customer Feedback In Multiple Languages To Increase Retention Rates

AccelerateProcess massive amounts of

text at high speed

ConnectAnalyze multiple silos

of global customer data

SimplifyAutomatically determine

intent-to-churn

Challenge: Applying basic voice-of-the-customer-concepts and text analytics to customer feedback in over 60 countries worldwide.

Solution: Use RapidMiner’sPlatform to detect churn and identify customer service issues regardless of time, location or language.

150,000customer comments and tweets in almost every language processed on

RapidMiner

33

Data Science Hero Spotlight“RapidMiner is extremely

powerful, has the best operators, and can handle Big

Data from wearables. It also allows us to rapidly prototype

sophisticated analytics, machine learning and

classification applications, saving time and money.“

-- Kevin LoganCEO

Quickly Prototype Analytics Models for Under Armour Challenge Wearables Data

AcceleratePrototype multiple analytics

processes quickly and easily

ConnectAnalyze Big Data from

wearables devices

SimplifyUse code free, drag and

drop GUI for analytics

Challenge: Quickly prototype analytics processes for Under Armour wearable data, for the Under Armour39 Challenge.

Solution: Use RapidMiner’s code free, drag and drop GUI to quickly design 11 analytics processes, iterate them for optimization, and win the challenge.

1.8Mdata points analyzed,

per hour, by the Under Armour39 wearable

34

,

Data Science Hero Spotlight“We benefit from the public

availability of extensions and the RapidMiner Marketplace. We can easily search for what

others have designed in RapidMiner, and use the

extensions that are a fit for us.”

-- Tom GattenCEO

Track Data from Millions of Companies to Identify Critical Economic Drivers

AcceleratePrototype analytics and

visualizations quickly

ConnectAnalyze data from the digital

footprint of UK businesses

SimplifyRapidMiner Marketplace

public extensions

Challenge: Monitor corporate performance data in real time, and identify correlations, outliers, and economic drivers.

Solution: Use RapidMiner’s algorithms for rapid prototyping and visualizations for correlations, and to identify outlying, unusual, data.

4.5 Msubject matter experts’

content analyzed in the United Kingdom

35

,

Data Science Hero Spotlight“Some years ago (the patent team) had tried a dedicated

patent classification tool that didn’t work - RapidMiner

does. It provides a framework for substantially reducing the

time it takes us to find interesting patents.”

-- Thomas HartmannBusiness Engineer

Search Millions of Patents Online and Automatically Mine Image Data

Challenge: Search millions of patents online and automatically mine image data for applicable information.

Solution: Use RapidMiner text and image mining to quickly and easily identify several thousand images of interest.

1M+detailed patent records

mined online, including images

AccelerateAutomatically mine millions of

online patent images

ConnectSearch through a wide

variety online data sources

SimplifyNo programming required to

connect insights to action

36

Data Science Hero Spotlight:“RapidMiner allows us to

leverage Big Data, in real-time, for the TV industry.”

-- Avi BernsteinProfessor at the University of

Zurich, Department of Informatics

Drive Broadcast Revenues and Customer Retention with Streaming, Real-Time Analytics

AcceleratePersonalized recommendations

in less than five seconds

ConnectStream and analyze from set-top

boxes, mobile devices and PCs

SimplifyCode free design of

streaming analytics

Challenge: Better understand TV viewing habits to prevent churn and optimize advertising.

Solution: Process streaming Big Data from three million TV viewers, in real-time, to make program content recommendations and target advertising.

<5stime to generate high

value activities based on predictive analytics

TelevisionBroadcasters

Project

37

“RapidMiner was most frequently selected based on ease of use, license cost, and speed of model development/ability to build large numbers of models. A number of templates guide users on the most common set of predictive use cases. Customer references cite high levels of satisfaction with the data access, data filtering and manipulation, predictive analytics and further advanced analytics components of the product.”

Gareth HerschelResearch Director

Don’t Take It From Us

"Radoop also makes an eponymous product, focused on Hadoop analytics functionality, that is also visually-oriented and is 'powered by' RapidMiner itself, making the union quite logical.”

Andrew Brust Research Director

“RapidMiner is an excellent data mining and statistics platform with a large following. With version 6 the product and company became much more commercial, and the recent acquisition of Radoop puts it in the big data league.”

Martin ButlerResearch Director

38

"Customer references cite high levels of satisfaction with the data access, data filtering and manipulation, predictive analytics and further advanced analytics components of the product.”

Recognized Leader in Advanced Analytics

As of February 2014. Gartner Magic Quadrant for Advanced Analytics Platforms (Feb. ’14). www.rapidminer.com/gartner2014

Challengers Leaders

Niche players Visionaries

Completeness of vision

Ab

ility

to

exe

cute

Revolution Analytics

Alteryx

RapidMiner

IBMSAS

StatSoft

SAPAngoss Knime

Actuate

InfoCentricity

Microsoft Alpine

Data

Labs

Megaputer

Oracle FICO

39

Industries

Manufacturing Government

Retail/CPG Automotive

Financial Life Science

Utilities/Energy Telecom

Our HistoryRapidMiner was born from a data science project at the University of Dortmund, Germany, by Ingo Mierswa, Ralf Klinkenberg and Simon Fischer. Initially known as YALE in 2001, the product led to Rapid-I, a company founded by Ingo and Ralf in 2007. Later, the company was renamed to RapidMiner and in 2012, global HQ were established in Cambridge, Massachusetts, USA.

Customers600+ worldwide

Corporate Locations North America | EMEA

Our Milestones

Investors

Earlybird Venture Capital

Open Ocean Capital

2007 – Open Source

2010 – Open Core

2013 – Business Source

2014 – Big Data & Cloud

5,000 30,000

150,000

250,000

Global Users2007 2010 2013 2014

40

www.rapidminer.com

Activating the data science hero in every business analyst!