Streaming analytics overview for R

This document (including, without limitation, any product roadmap or statement of direction data) illustrates the planned testing, release and availability dates for TIBCO products and services. It is for informational purposes only
and its contents are subject to change without notice. © Copyright 2000-2017 TIBCO Software Inc. All rights reserved. TIBCO Proprietary Information.
Streaming Analytics
Lou Bajuk-Yorgan
Sr. Director, Product Management,
Streaming and Advanced Analytics

DISCLAIMER
During the course of this presentation, TIBCO or its representatives may make forward-looking statements regarding future events,
TIBCO’s future results or our future financial performance. Although we believe that the expectations reflected in the forward-looking
statements contained in this presentation are reasonable, these expectations or any of the forward-looking statements could prove to
be incorrect and actual results or financial performance could differ materially from those stated herein.
TIBCO could experience factors that could cause actual results or financial performance to differ materially from those contained in
any forward-looking statement made in connection with this presentation. TIBCO does not undertake to update any forward-looking
statements that may be made from time to time or on its behalf.
This document (including, without limitation, any product roadmap or statement of direction data) illustrates the planned testing,
release and availability dates for TIBCO products and services. This document is provided for informational purposes only and its
contents are subject to change without notice. TIBCO makes no warranties, express or implied, in or relating to this document or any
information in it, including, without limitation, that the information is error-free or meets any conditions of merchantability or fitness for a
particular purpose. This document may not be reproduced or transmitted in any form or by any means without our prior written
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The material provided is for informational purposes only, and should not be relied on in making a purchasing decision. The
information is not a commitment, promise or legal obligation to deliver any material, code, or functionality. The development, release,
and timing of any features or functionality described for our products remains at our sole discretion.

People &
Processes
Data &
Systems
APIs
Visual
Analytics
Streaming
Analytics
Predictive
Analytics

AUGMENT INTELLIGENCE
Visual
Analytics
Streaming
Analytics
Predictive
Analytics
Model business and system behavior
using data big and small
Deliver recommendation driven insight
to business and operations
Sense and take action in the right moment to
improve business outcomes

5
R Foundation Platinum Members Gold Members
Silver Members

Traditional Data Processing
• Data is collected from a variety of
sources, and placed in a persistent
store.
– Relational database.
– NoSQL store.
– Hadoop environment.
• Analytical processes are executed
against the stored data to detect
opportunities or threats.
• Actions are identified, delivered,
and executed across various
business channels.
Store
Analyze
Act

Traditional Data Processing: Challenges
Store
Analyze
Act
• Introduces too much decision
latency into the business.
• Responses are delivered after-the-
fact.
• Maximum value of the identified
situation is lost.
– Cross-sell / up-sell opportunities are
lost, impending equipment failure is
missed, business processes are slow
to respond and lack timely context.
• Decisions are made on old and stale
data.

Event Value Decreases Over Time
Value
Time
• Events are often most
valuable close to the point
of collection.
• As time passes, events
tend to lose their value.
• The ability to proactively
identify threats or
opportunities will typically
decrease.
• Real-time capability is
needed to maximize event
value.

Streaming Analytics
• Events are analyzed and processed in
real-time as they are generated.
• At the edge and in the data center.
• Decisions are timely, contextual, and
based on fresh data.
• Decision latency is eliminated, resulting
in:
Superior Customer Experience
Operational Excellence
Instant Awareness and Timely Decisions
Act &
Monitor
Analyze
Store

BATCH ANALYSIS REAL-TIME ANALYSIS
PROCESS ZONE STRATEGIC TACTICAL OPERATIONS EXECUTION
TIME INCREMENT
Quarters/
Years
Months/
Quarters
Hours/
Days/Weeks
Seconds/
Minutes
Evolution of Analytics

Evolution of Predictive Analytics
Insight Discovery
• TIBCO Spotfire
• R, TERR®, Python
• H2O, Matlab, SAS, KNIME
Insight Execution
• PFA, PMML
• POJOs
• Proprietary formats (SAS, TIBCO, ...)
Insight Execution Services
• Microservice with containers
• Devops / Data Science handshake
Azure
Stream

Evolution of Streaming Analytics
Complex Event
Processing
~ 2000 - 2010
FSI, Transportation
and Logistics, Retail,
Airlines, Telco,
Manufacturing
Event
Processing
~ 2011 - 2014
Big Data as DW,
open source as
toolkit.
Clickstream, Social.
Convergence of
Streaming and
Analytics
~ 2017+
IoT, Insight
Platform
Streaming
Analytics
~ 2015+
Big Data & open
source as platform,
IoT, Cloud, Self-
Service, Sensor,
Telemetry, ML & AI,
revisit old use cases.

Batch
APIs
Adapters /
Channels
Integration
Messaging
Transformation
Aggregation
Enrichment
Filtering
Process
Management
Analytics
(Real Time)
Applications
Analytics / DW
Reporting
• Transform
• Deep ML
• Analytics
• Data Lake
Real-Time
Index / SearchNormalization
• RT Analytics
• Event Rules
• Windowing
• Patterns
Event Ingest Data Prep & Features Event Analytics Delivery

Advanced Analytics and Big Data Tools (for Data Scientists)
Many more ….

What would the ideal R solution look like?
• A single environment that would allow you to prototype in R, and deploy to
production in R
• Without recoding, without delay, without compromises
• Enable agile response to changing opportunities and threats
Requires
• Analytic flexibility, power and breadth of R
• High performance, scalable, robust platform
• Easy to embed in Business Intelligence, Real time and custom applications
• Fully supported for mission critical applications
• Allows R users to continue to work in their preferred development environments (e.g., RStudio)

TIBCO Enterprise Runtime for R (TERR)
• Unique, enterprise-grade statistics engine,
architected from the ground up by TIBCO
• Based on TIBCO’s long history and expertise with S+
• Better performance and memory management than open
source R
• Designed for R language compatibility
• Wide range of built-in analytic methods
• Extensible through R community packages
• Designed for commercial embeddability
• TIBCO licensed & supported product
• Not GPL, not a repackaging of the Open source R engine
• TERR extends the reach of R in the enterprise
• Develop code in open source R
• Deploy on a commercially-supported and robust platform
• Without the delay and cost of rewriting your code
• Embed in Data Discovery, BI and real time applications

Insight Action

Insight Action
MONITOR
PREDICT
ACT
DECIDE
MODEL
ACCESS
ANALYZE
WRANGLE
TIBCO Insight Platform

Optimize
Pricing Prevent
Fraud
Smart cross-sell
offers
Real-Time
inventory
Management
Optimize
Routes
Deliver proactive
customer service
Predict impending
equipment failure
Anticipate
and handle
disruptions
Analytic AppsS
© Copyright 2000-2017 TIBCO Software Inc.

Request from Website Targeted Analytics Offer to Website
Customer Data
Product Data
Real-Time Customer Interactions / Offers
Login: Customer Propensity
No Login: Product Association

Real-Time Customer Interactions / Offers
No Login = No Customer History => Offer based on Product Association
Sarah Login = Sarah’s Customer History => Offer based on Propensity Model scored for Sarah

Use Case Example: Manufacturing Optimization
• Problem
• Producing defective,
unreliable or poor performing
product.
• Shorter lifecycles  faster new
product ramps.
• Value
• Increase Yield / % Good
product
• Reduce Defects & Rework
• Improve Reliability
• Use Cases
• Quality Monitors and
Dashboards
• Root Cause Analyses
Root Cause:
Machine
Effects
Monitor Product
Quality KPIs and flag
outliers.

Use Case Example: Retail / Real-Time Inventory
• Problem:
• Lack of visibility into inventory levels across all
channels.
• Difficulty allowing shoppers to buy anywhere, fulfill
from anywhere, and return anywhere.
• Difficulty identifying the best fulfillment locations.
• Value:
• Increased customer satisfaction.
• Reduction in excess inventory.
• Improved inventory visibility across all channels.
• Use Cases:
• “Order Online, Pick-up In Store”
• Beacons / Contextual Offers
• Endless Aisle
• Personalized Shopping Experience

Fraud Applications
Anti-Money Laundering
▪ Identify / trace unknown and suspect transactions across
context e.g. business lines and geographies
▪ Set thresholds, define segments and automate alert scoring
▪ Comply with OCC guidelines
Credit Card Fraud
▪ Identify fraudulent transactions by vendor, location
▪ Highlight patterns, context and identify root cause
Regulatory Compliance
▪ Identify violations, patterns
▪ Expose a clear methodology and workflow
© Copyright 2000-2016 TIBCO Software Inc.

• FSI: Algorithmic pricing and execution, foreign exchange (FX) trading, market data
management
• Retail: In-store promotions, campaign optimization, real-time inventory tracking, fraud
detection
• Entertainment: Zero-latency betting, decision support over concurrent games, online
fraud and phishing detection
• Telecommunication and Networking: Real-time SLA management, network monitoring and
protection, fraud detection
• Transportation and Logistics: Flight operations, train-to-shore communication, passenger
experience management, parcel tracking, route optimization
• Energy: Production, fuel exploration, predictive equipment maintenance Manufacturing:
Process optimization, order-to-inventory management, supply chain monitoring
• Healthcare: Patient flow monitoring, bed inventory management, procedure optimization,
real-time disease detection
• IoT: Airline operations, locomotive operations, connected cars, smart homes
More Use Cases

Summary
Closed Loop, Insight to Action
Event Value decreases with time
Multiple options

Streaming analytics overview for R

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