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Big Data and Analytics (Big Data Analytics Framework (Big Data Analytics…
Big Data and Analytics
Maximizing Value from Business Analytics
Speed to Insight
Raw data
Useful Information
Reuse of Data
data standards and
metadata
automate data onboarding,
integration and quality processes
more automated processes
faster data can be physically transformed into usable Information
rapidly identify
business requirements for data
translate requirements into business analytics products and services
agile development methods
sandbox environments
colocating developers with business users
Recommendations
Create an Optimized Ecosystem of
Advanced and Traditional Data Technologies
Develop Data Standards, Even if it Means Creating a Standards Layer on Top of Diverse Systems
Pervasive use
User Engagement
Mobility
high adoption success
and user enthusiasm
Graphics
appropriate uses of maps, colorful dashboard displays and advanced visualization approaches
Users react
positively
Recommendations
Invest in Business-savvy IT Staff
Encourage User-intensive Development Practices
Exploit the “in” Technology
Big Data Analytics Framework
Big Data Analytics Framework
Data Sources
Data Preparation
Data Storage
Analysis
Data Access
and Usage
Big Data Management and Governance
Seeding Analytics Capabilities in the Business-Organization-Techology Ecosystem
Business
Value Propositions
Business Strategy
Lesson Learnt
Leverage process focus
Organization
Culture
People (HR Strategy)
Lessons Learnt
Foster data awareness
Adopt agile Development practices
Technology
Data Sets
ICT Strategy
Lesson Learnt
Move from isolated tools to open platforms
Project to Seed Analytics Capabilities in SUC’s Grid Planning Ecosystem
Identify Business Value Opportunities Using Process Know-how (Set Project Scope)
Articulate Vision and Set Seed Project Goals
Address Data Access Challenges
Select an Open Analytics Platform
Develop and Implement Applications in Incremental Steps
Evaluate Both Immediate Business Value and Long-term Potential of Analytics
IoT: Its The (Sensor) Data Stupid
"Sensors Sometimes Lie" Data are not always accurate
Even when sensors don't lie they are not always saying the whole truth
Extracting useful signal from time-series sensor data requires multi-genre analytics and additional data
Sensors typically don't measure the quantity of interest directly
Sensor data by themselves are of only limited value
Mind Map - Seminar 4 - Yaser Odeh
19810315-T794