1. Operational Intelligence
Opportunities

2. What is Operational Intelligence (OI)
• Category of real-time dynamic, business
analytics that delivers visibility and insight
into data, streaming events and business
operations.
• OI solutions run queries against streaming
data feeds and event data to deliver real-
time analytic results as operational
instructions.
• OI provides ability to make decisions and
immediately act on these analytic
insights, through manual or automated
actions.

3. Operational Intelligence
• Real-time monitoring and Event
detection
• Real-time dashboards
• Correlation of events
• Industry-specific dashboards
• Multidimensional analysis
• Root cause analysis
• Time Series and trending analysis
• Big Data Analytics
• Continuous monitoring and analytics
of high velocity, high volume Big Data
sources

4. Components
• Business activity monitoring (BAM)
• Dashboard customization and personalization
• Complex event processing (CEP)
• Advanced, continuous analysis of real-time information and historical data
• Business process management (BPM)
• To perform model-driven execution of policies and processes defined as Business Process
Model and Notation (BPMN) models
• Metadata framework to model and link events to resources
• Multi-channel publishing and notification
• Dimensional database
• Root cause analysis
• Multi-protocol event collection

5. Comparison
Business Intelligence
• Data-centric
• On Demand, Post-fact
• Input: Structured Data
Sources, RDBMS
• Long term analytics for Reactive
planning
Operational Intelligence
• Activity-centric
• Real time dynamic business
analytics
• Input : Data Stream, Machine
Data
• Short term analytics of in-flight
data for Pro-active response

6. Opportunities and Key Markets
• Real Time Data stream analytics
for
• Telecom Operators
• Banks
• Security and Defense
• Social Media
• Inbound call centers
• Projected to be $ 140 Billion
market by 2020.
• Product and Services models
• Key Markets
• US, Europe and India
• Telecom, Banking and internal
Security
• Social Media Monitoring

7. Operational Intelligence for
Retail Warehousing and
Logistics
Use Cases

8. Retail - Why Machine Data Matters
Why?
• Delivering a better customer experience
• Scaling aggressively to meet customer demands
• Quickly introducing services on new devices
How?
• Servers, applications, databases and networks infrastructure
generate terabytes of machine data every day
• Logs from Application, POS, Server, VM, Messaging, Proxy
logs, IPS/IDS, PCs and Mobiles devices
• Interactions from Social Media
What to do :
• Increasing online store uptime
• Enhancing customer experience
• Timely order processing
• Scaling infrastructure
• Better customer data security.

9. Order Profiling and Tracing for Better
ServiceAn Order goes through, multiple applications and elements in an IT infrastructure
• Machine data visibility across IT infrastructure helps Retailers eliminate transaction bottlenecks and
address them in a timely manner.
Systems and applications that span physical, virtual and cloud environments,
• Retailers to have end-to-end visibility to ensure a scalable, flexible and reliable infrastructure -
indexing machine data from routers, switches, firewalls, wireless controllers and VM servers to gain
key operational metrics.
Case :
• Staples indexes data across their order management infrastructure to trace the order transaction
path.
• they now have end-to-end monitoring across the systems traversed by a transaction rather than at
an individual system level.
• Benefit : Decrease time to Resolution, Reduce resources required to troubleshoot issues.

10. Benefits
Resolve Problems Faster, Reduce Downtime
• Gain end-to-end operational visibility across virtualized, private or public cloud infrastructure from a single, central interface
• Find the root cause of problems up to 70% faster, without having to search through systems, server by server or virtual machine by virtual
machine
• Monitor infrastructure in real time to prevent problems before they impact users, retain knowledge of recurring events to prevent outages
• Reduce escalations by up to 90% by providing Tier 1 support staff direct and secure access to the data they need to resolve issues the first
time or to find the right team to work on problems
Correlate Events Across All Layers of Infrastructure
• Find the causal links between user noticeable performance issues or outages and underlying infrastructure events with the time based
correlation
• Combine real-time streaming data analysis with terabytes of historical data correlation and analysis to detect patterns that can help predict
and prevent future outages or performance issues
• Persist 100% machine data from across every tier of datacenter - physical servers, network and storage devices, hypervisors, virtual
machines and applications, whether it is in datacenter or in public clouds
• Monitor environment for changes and correlate instantly to system performance deviations, availability problems or security and
compliance issues
Reduce Costs of Providing IT Services
• Support audits, compliance mandates, security forensics
• Reduce the number of tools and skills you need to maintain to manage complex infrastructure

11. Goto Market strategy
• Phase 1
• Develop Framework Definition and Architecture
• Metadata framework to model and link events to resources
• Prototype for Complex event processing (CEP) and Business Activity Monitoring (BAM)
• Data Stream Monitoring and Analytics
• BAM Dashboard customization and personalization
• Pilot for 2-3 customers
• Effort cap : 20-30 man months
• Phase 2
• Framework Scrubbing and Refactoring
• Multi-channel publishing and notification
• Root cause analysis
• Big Data analytics
• Phase 3
• Market entry

12. Subbu Jois
srjois@gmail.com
+91 9945103337