1. April 25, 2012
The Three R’s Are Old School – Now It Is All About Volume, Velocity & Variety
Peter Guest Alberta Public Sector Client Technical Advisor

2. Why analytics now? Because we can…

3. The world is changing and becoming more…

4. Big Data = huge opportunity to generate new outcomes…
Steve Mills
03/20/12
Big Data = huge opportunity to generate new outcomes…
Volume
Velocity
Variety
Grab the <5% of tweets of interest in 12 terabytes of tweets created everyday
Identify potential fraud in five million trade events per second
Identify events of interest from hundreds of surveillance cameras
Predict power consumption using 350 billion meter readings from 20 million households annually.
Analyze up to 500 million Call Detail Records per day for revenue assurance
Harvest insight from the 80% of new data growth coming from , documents, images, video and audio
However, organizations are challenged to achieve the potential insight from big data with traditional approaches.
For US equity electronic trading brokerage, handling the speed of the market is of critical importance because latency impedes a broker’s ability to provide best execution. In 2008, 16% of all US institutional equity commissions are exposed to latency risk, totaling $2B in revenue. As in the Indy 500, the value of time for a trading desk is decidedly non-linear. TABB Group estimates that if a broker’s electronic trading platform is 5 milliseconds behind the competition, it could lose at least 1% of its flow; that’s $4 million in revenues per millisecond. Up to 10 milliseconds of latency could result in a 10% drop in revenues. From there it gets worse. If a broker is 100 milliseconds slower than the fastest broker, it may as well shut down its FIX engine and become a floor broker.
We all know that Big Data is “big”, but I think it’s always helpful to ground us in numbers.
As much as I read about this, I’m still amazed when I come across a new piece of data.
Look at that cell phone number – 5B cell phones in 2010, only 12% were smart phone. Obviously all phones generate data that can be analyzed, but smart phones even more so. The CAGR on smart phones is usage is forecasted to be 20%.
Sensors are another amazing one. 30% grown on 30M networked sensors. We’ve talked to clients with vast networks of sensors, but only use the data off 10% of them. The installed the sensors because they know the data is valuable, they just don’t know how to handle that data.
I’m sure you can think of similar examples in your business or industry.
Mills Smarter Analytics Event NYC 4 4

5. Why Business Analytics Matter The Need for Analytics is Pervasive Across Business and Industry
The healthcare industry spends $250 - $300 billion on healthcare fraud, per year. In the US alone this is a $650 million per day problem.1
One rogue trader at a leading global financial services firm created $2 billion worth of losses, almost bankrupting the company.
$93 billion in total sales is missed each year because retailers don’t have the right products in stock to meet customer demand.
5 billion global subscribers in the telco industry are demanding unique and personalized offerings that match their individual lifestyles.2
Source: 1.Harvard, Harvard Business Review, April 2010.
2,IBM Institute for Business Value, The Global CFO Study, 2010.

6. Why Business Analytics Now Market Forces are Driving New Client Needs
The emergence of Big Data –
enabled by cost effective storage and processing of data
The shift of power to the consumer
Pressure to do more with less

7. BAO Competencies and Offerings
Steve Mills
03/20/12
BAO Competencies and Offerings
Business Intelligence & Performance Management
Enterprise Information Management
Enterprise Content Management
Advanced Analytics and Optimization
BAO Strategy
BAO Strategy and Roadmap
BAO Process Improvement
BAO Governance
Dashboards & Scorecards
Planning, Budgeting, & Forecasting
Business Analytics & Reporting
Advanced Analytics
Analytic Applications
Predictive Modeling
Business Optimization
Visualization
Data Integration
Data Quality
Data Architecture
Master Data Management
Document & Records Management
Web 2.0 / Web Content Management
Digital Asset & Rights Management
Archiving & Record Management
Mills Smarter Analytics Event NYC

8. The BAO Reference Architecture Overview
Steve Mills
03/20/12
The BAO Reference Architecture Overview
Content
Management
Master Data Management
Data Integration
Data Repositories
BI / Performance Monitoring
Advanced
Analytics
Access
Sources
Enterprise Apps
Unstructured Data Stores
Informational
External
Web
Structured Data Stores
Devices
Master / Reference Data
Document
Management
Services
Federation
Ingestion
Base Services
Records
Extraction
Reference Data Management
Operational Orchestration
Components
Data Load Components
CRUD Transactional
Operational Data Store
Data Warehouse
Time Persistent Repository
Dimensional Layer
Master Data Store
Content Store
Staging Area
Reporting
Planning, Forecasting, Budgeting
Scorecards
Guided Analysis
Dashboards
Querying
Monitoring
Simulation
Optimization
Visualization
Predictive
Analytics
Data
Mining
Text Analytics
BATCH
Extract / Subscribe
REALTIME
Web / Services
Data Quality
Portal
Device
Transform
Composite Application
Load / Publish
Collaborative Application
TRANSACTIONAL
Productivity Application
Enterprise Search
Business Unit Application
Business Process Management
Service Management
Information Governance
Collaboration
Security, Privacy & Compliance
Transport & Delivery
Infrastructure
Mills Smarter Analytics Event NYC