The document outlines key aspects of big data analytics, including unified information management, real-time analytics, and intelligent processes. It emphasizes the importance of accommodating various forms of data, ensuring consistency in information, and integrating analysis to drive actionable insights. Additionally, it discusses multi-channel delivery and the structural components necessary for effective big data architecture.

1. Your C ompany N ame
Big Data
Analytics
Architecture

2. Content
2
Three Important Aspects of Big Data & Analytics
› Unified Information Management
› Real Time Analytics
› Intelligent Processes
Architecture Principles for Big Data Analytics
› Accommodate All Forms of Data
› Consistent Information and Object Model
› Integrated Analysis
› Insight to Action
Sample showing how these Big Data Components
fit within reference Architecture
Conceptual View for Big Data Reference Architecture
Different Types of Data Incorporated in Big Data Analytics

3. Content
3
Three Important Aspects of Big Data & Analytics
› Unified Information Management
› Real Time Analytics
› Intelligent Processes
Architecture Principles for Big Data Analytics
› Accommodate All Forms of Data
› Consistent Information and Object Model
› Integrated Analysis
› Insight to Action
Sample showing how these Big Data Components
fit within reference Architecture
Conceptual View for Big Data Reference Architecture
Different Types of Data Incorporated in Big Data Analytics

4. Conceptual View for Big Data Reference Architecture
4
Multi Channel Delivery
Single Version Of the Truth
Law Latency Data Processing
High Volume Data Acquisition
Multi Structured Data Organization &
Discovery
Unified Information
Management
Interactive Dashboards
Event Processing
Speed of Thought Analysis
Advanced Analytics
Real-Time Analytics
Application Embedded Analysis
Optimized Rules &
Recommendations
Guided User Navigation
Performance & Strategy
Management
Intelligent Processes
Public Cloud Managed Services Private Cloud Traditional IT
Operational
Data
Content External
Data
COTS Data Historical
Data
System-
Generated Data
Analytical
Data
Authoritative
Data
The conceptu
reference arch
capabilities to pro
description of th
Analytics solutio
depicts suppo
channels that a c
perform analys
intelligence i
represents deliv
channels and mo
stationary and m
connected and
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5. Different Types of Data Incorporated in Big Data Analytics
5
› A High-Quality Data that
is used to provide
Context to Operational
Data
› Includes Master Data -
Standardized Key
Business Entities such
as Customer and
Product
Authoritative Data
› Documents, Videos,
Presentations, etc., are
typically Managed by a
Content Management
System
Content
› This Data often
Originates from within
the Organization and
has Historically been
Overlooked in terms of
Business Analytics
Value
System-Generated
Data
› Data that is Organized
to Accommodate large
Volumes and Structured
to easily Accommodate
Business Changes
without Revisions
Historical Data
Operational Data
› Data residing in
Operational Systems
such as CRM, ERP,
Warehouse
Management Systems,
etc
› This data constitutes the
bulk of Traditional
Structured Data
Warehouses, Data
Marts
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6. 6
Important Aspects of
Big Data & Analytics

7. Unified Information Management
7
This Covers the following Aspects
› System must be able to acquire data despite high volumes, velocity,
and variety
› It may not be necessary to persist and maintain all data that is received
› Add text here
High Volume Data Acquisition
› Data processing can occur at many stages of the architecture.
› To support the processing requirements of Big Data, the system must
be fast and efficient
› Add text here
Low Latency Data Processing
› Developing the capability to organize data of different structures into a
common schema
› New business opportunities can be discovered by looking at different
forms of data in new ways
› Add text here
Multi-Structured Data Organization and Discovery

8. Real-Time Analytics
8
This covers the following Aspects:
› It can enhance customer interactions and buying decisions, detect
fraud and waste, and enable the business to adjust according to
current conditions
› Add text here
Advanced Analytics
› Enables the user to immediately react to information being displayed,
providing the ability to drill down and perform root cause analysis of
situations at hand
› Add text here
Interactive Dashboards
› System performance must keep pace with the users’ thought process
› Add text here
Speed of Thought Analysis
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9. Application-Embedded
Analysis
› Embedding Analysis into the
Applications they use helps them
to make more Informed Decisions
› Add text here
Optimized Rules &
Recommendations
› Such Processes Execute using
Pre-defined Business Logic
› Insight from Analysis is used to
Influence the Decision Logic as
the Process is being Executed
› Add text here
Performance & Strategy
Management
› It can help to Ensure that Strategy
is based on Sound Analysis
› It can track Business Performance
versus Objectives in order to
Provide Insight on Strategy
Achievement
› Add text here
Intelligent Processes
9
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10. Content
10
Three Important Aspects of Big Data & Analytics
› Unified Information Management
› Real Time Analytics
› Intelligent Processes
Architecture Principles for Big Data Analytics
› Accommodate All Forms of Data
› Consistent Information and Object Model
› Integrated Analysis
› Insight to Action
Sample showing how these Big Data Components
fit within reference Architecture
Conceptual View for Big Data Reference Architecture
Different Types of Data Incorporated in Big Data Analytics

11. 11
Architecture Principles for
Big Data Analytics

12. Rational
Architecture must be flexible enough to
support different forms of data in a way
that best supports analysis along with
being efficient and cost effective
Your text here Your text here
Implications
Capture, Process, Organize, & analyze
all forms of Data in order to meet the
Business requirements & Support
Discovery of new Business Opportunities
Impose the proper amount of structure to
each form of Data
Your text here
Accommodate All Forms of Data
12
Architecture must accommodate all forms of Business relevant Data

13. Rational
Business analytics have a greater value
when the results of analysis
are consistent and can be duplicated.
Analytics can be applied
to a greater audience when analysis
objects can be designed by subject
matter experts & re-used by all workers.
Your text here
Implications
Maintain conformity of dimensions &
facts across dimensional data stores.
Provide a means to define and share
analysis objects.
Your text here
Consistent Information and Object Model
13
System must present consistent Information and Object Model

14. Rational
Reach of decision-making analysis must
expand to include all knowledge workers
in organization & applications they use.
Your text here Your text here
Implications
Integrated analysis into devices and
processes so that users can gain
insight anywhere & everywhere
Enable end users who are not familiar
with structures & BA tools to view
information pertinent to their needs
Your text here
Integrated Analysis
14
Information and analysis must be available to everyone across the organization

15. Rational
Organization must link the results of
analysis to actions that are taken.
Your text here Your text here
Implications
Alert users when events are detected
and enable users to subscribe to
different types of events.
Guide users to the appropriate
applications & processes from which
they can take required action.
Your text here
Insight to Action
15
System must provide the ability to initiate actions based on insight gained through analysis

16. Content
16
Three Important Aspects of Big Data & Analytics
› Unified Information Management
› Real Time Analytics
› Intelligent Processes
Architecture Principles for Big Data Analytics
› Accommodate All Forms of Data
› Consistent Information and Object Model
› Integrated Analysis
› Insight to Action
Sample showing how these Big Data Components
fit within reference Architecture
Conceptual View for Big Data Reference Architecture
Different Types of Data Incorporated in Big Data Analytics

17. Sample showing how these Big Data Components fit within reference Architecture
17
Presentation Services Information Services Business Rules
Text here Event Handling
Process-Based Analytic Applications Text here Custom Analytic Applications Business & Strategy Planning
Interactive Dashboards Reports Spreadsheets
Charts & Graphs Guided Analysis
Text here
Text here
System
Generated Data
External
Data
COTS
Data
Reference
Data
Operational
Data
Content
In – DB
Analytics
Discovery
Data
In-Memory
Analytics
Historical
Data
Analytical
Data
Logical Data
Warehouse
Data Virtualization
Data Processing Event Direction
Data Ingestion
Print
Laptop E-mail
Mobile
Tablet
Desktop SMS
Multi Channel Delivery : The results of analysis can be delivered via many different channels
Multi Channel Delivery
Services Layer
Process Layer
Information layer
Interaction Layer
Interaction Layer : Comprised of components used to support interaction with end users
Process Layer : Represents components that perform higher level processing activities
Services Layer : Includes components that provide or perform commonly used services
Information layer : Includes all information management components
Data Infrastructure Big Data Infrastructure Analytics Infrastructure
Shared Infrastructure Layer
Shared Infrastructure Layer : Includes the hardware and platforms on which the Big Data and Analytics components run
Modelling
Management
Monitoring
Security
Governance
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