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



DISCLAIMERDuring 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

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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

AnalyticsModel 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


and its contents are subject to change without notice. © Copyright 2000-2017 TIBCO Software Inc. All rights reserved. TIBCO Proprietary Information. 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 TimeV

alu

e

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 INCREMENTQuarters/

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.



Bat

ch

APIs

Adapters / Channels

Integration

Messaging

Transformation

Aggregation

Enrichment

Filtering

Process Management

Analytics (Real Time)

Applications

Analytics / DW Reporting

• Transform• Deep ML• Analytics• Data Lake

Rea

l-Ti

me

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

ManagementOptimize Routes

Deliver proactive customer service

Predict impending equipment failure

Anticipate and handle disruptions

Analytic AppsS

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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 timeMultiple options

Streaming analytics overview for R

Technology

Transcript of Streaming analytics overview for R