predictive analytics from a to z

Learn more about Advanced Analytics at http://www.alpinenow.com

Predictive Analytics: A to Z


PanelistsMichael Zeller, CEO Zementis

Steven Hillion, CPO Alpine Data Labs

Michael Zeller manages the strategic direction of Zementis. As Co-Founder of Zementis, his vision is to help companies deepen and accelerate insights from big data through the power of predictive analytics. Michael has extensive experience in strategic implementation of technology, business process improvement and systems integration.

Steven Hillion has been leading large engineering and analytics projects for fifteen years. Before joining Alpine Data Labs, he founded the analytics group at Greenplum, leading a team of data scientists and also designing and developing new open-source and enterprise analytics software.


Alpine Data Labs• Advanced Analytic Software Company

– Founded in 2011– Agile Advanced Analytics, Collaboration and Management at Enterprise

Scale– Partnerships with EMC, Pivotal, MapR, Cloudera, QlikView and Tableau

• 50+ employees, based in San Francisco– Machine Learning, Statistics and Big Data (Stanford, Berkeley, MIT)

• Growing in excess of 200% YOY with a broad international customer base– Financial Services, Online Media, Government, Retail, Manufacturing…

Alpine Data Labs. Confidential and Proprietary.


Alpine Data Labs• One of 16 in Gartner’s Magic Quadrant, Advanced

Analytics Platforms– Recognized for “scalability”, “state-of-the-art UI” and “very high levels of customer

satisfaction”.

• Top Big Data Company in CIO Magazine– “Collaboration, Search, Modeling, Machine Learning under the same roof. Alpine

Data Labs makes Predictive Analytics accessible to all”.

• Other awards include…– Top 5 Big Data Company (Enterprise Magazine)– Top 10 Company to Watch in 2014 (Inside Analysis)– Top Big Data Company in (Sand Hill Business)– 2014 Top Startup (TiE50)



Predictive Analytics: A to Z


Advanced Analytics on Big Data

Timeframe of Relevance

Work independently and re-use data scientist work. Collaborate across functions and teams. Iterate quickly.

Scalable Business Analytics

Allowing the Enterprise to manage “Data as an Asset.”

Scale and guard data practices

Data Science Productivity

Work faster, safer, in a more open manner. Industry leading machine learning algorithms built natively for parallel processing.


ALPINE CHORUS 4.0

ENTERPRISE DATA ENVIRONMENT

Data Scientist

Database Analyst

Data Engineer

Business Analyst

Campaign Manager

Sales Division

CustomerSuccess

Product Manager


Alpine Demo

Copyright 2014 Zementis, Inc. All rights reserved. Confidential

Company: Capabilities:Products:

Software for operationalizing predictive analytics for big data, in both batch and real-time environments.

• Launched in 2004

• Global customer base

• Deployments across multiple industries

• Partnerships with software and analytics industry leaders

• Offices in USA, Asia

• Vendor-neutral architecture for

- Data mining tools

- Analytics and data warehouse platforms

• Multi-platform support: Hadoop, MPP and SQL

• Supports PMML industry standard

• Flexible deployment: cloud, on-site

• Separates model development from deployment

• Supports wide range of predictive modeling techniques

• Rapidly deploys and executes predictive models

• Scores data in batch and real-time modes

• Accelerates business insight

Zementis provides predictive analytics solutions for big data

13

Zementis: Company Snapshot


Path to Business Value

14

Predictive analytics helps organizations unlock the value of their big data …

Big Data

Applications

Cloud

Log Files

RSS Feeds

Other Sources

Predictive Models

Machine Learning Techniques

Data Mining Tools

Business Insights

Business Value

Decisions&

Actions

• Accelerated time-to-market

• More precise targeting

• Real-time responsiveness

• Enhanced operational agility

• Competitive advantage

• Higher revenue growth rates

• Greater profitability

• More relevant

• More accurate

• More comprehensive

• More nuanced

• Faster

• Lower risk

• Greater positive impact

PredictiveAnalytics

Databases


Predictive model deployment becomes a rework cycle

• Extensive manual coding

• Cross-checking

• Fixing coding errors

• Delayed insight

• Less accurate decisions

• Missed opportunities

• Loss of value

Traditional Deployment Cycle

Develop Operationalize Utilize

Data Scientist IT Engineer

Business Decisions

Business Professional

15

… but model deployment challenges can often erode much of the value that predictive analytics can deliver


Within 2 days *

~ 6 months

Series1

Without ZementisWith Zementis

Time-to-insight

Eco

no

mic

Va

lue

Model Deployment Cycle Time

* And sometimes even within a few hours!

• Accelerated deployment timeline

• Enhanced accuracy

• Minimal rework, if any

Rapid insight

Rapid time-to-value

from predictive analytics

=

Deployment with Zementis

16

Enter Zementis, whose solutions accelerate time-to-insight for predictive analytics


ADAPA & UPPI

• Scoring engine (decision engine) for

predictive analytics

• Supports real-time or batch scoring

• ADAPA functionality packaged as a

plug-in tool for industry-leading

analytics and data warehouse platforms

• In-database or Hadoop

UPPIUniversal PMML Plug-in

• Scoring engine (decision engine) for

predictive analytics

• Supports real-time or batch scoring

• Available as cloud-based or on-premise

solution

ADAPAAdaptive Decision And Predictive Analytics

17

Zementis’ 2 solutions offer organizations powerful and flexible capabilities to accelerate big data insights


Data Mining Tools

Predictive Modeling Techniques

• IBM WebSphere

• RedHat JBoss

• Oracle WebLogic

• SAP HANA

• IBM PureData / Netezza

• Pivotal / EMC Greenplum

• SAP Sybase IQ

• Teradata + Teradata Aster

• Hive

• Cloudera

• Datameer

• Hortonworks

• IBM

Cloud

On-site

• Amazon Web Services

• FICO Analytic Cloud

• Private Cloud

• Supports multiple broad categories of predictive modeling techniques

• Examples:- Clustering Models- Decision Trees- Neural Network Models- Naive Bayes Classifiers- Random Forest Models

Analytics and Data Warehouse Platforms

In-database

Hadoop

ADAPA

UPPI

Execute Anywhere

18

Vendor-neutral architecture enables compatibility with industry-leading analytics and data warehouse platforms

• Compatible with most leading commercial and open-source data mining tools

• Examples:- IBM SPSS- KNIME- Python- R- SAS


Fraud & Risk Management

• Leading financial services conglomerate

• US-based, with global operations

• Diverse portfolio:

- Banking

- Private wealth management

- Consumer finance

- Insurance

• Size:

- Annual revenue > $45 billion

- Annual op. income > $25 billion

• Complex data management environment

• High risk and growing complexity of financial fraud

• Enormous costs when fraud succeeds

• Small data science team

• Multiple analytics applications

• Scalability constraints (people, processes)

• Accelerated and more holistic risk detection

• Greater interoperability between apps

• Increased predictive accuracy

• Lower risk profile

• More efficient data science

• ADAPA

Decision engine for predictive analytics

• UPPI

Packaged for industry-leading analytics and data warehouse platforms

Customer Profile

19

Zementis in Action: Customer Case Study

Challenge

Results

TheZementisSolution


Broad Applicability

Fraud & Risk Scoring

• Financial institutions

• Scoring bureaus

• Online transaction processing

• Advanced decision management

Marketing & Sales

• Up- /cross-sell and next-best-offer

• Marketing campaign optimization

• Real-time recommendations

• Rotating equipment

• Energy

• Biometrics

• IP network security

Sensor & Device Data Processing

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ADAPA and UPPI accelerate predictive model insights for multiple industries and business use cases


For more information, contact us

3570 Carmel Mountain RoadSuite 300San Diego, CA 92130USA

+1 (619) 330-0780

www.zementis.com

1550 Bryant Street Suite 1000San Francisco, CA 94103USA

+1 (877) 542-0062

www.alpinenow.com

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