The document provides an overview of business intelligence (BI) and its role in addressing challenges faced by organizations, particularly government agencies, in managing large data volumes and ensuring data quality. It emphasizes the importance of various BI technologies, such as predictive analytics and cloud computing, in enhancing decision-making capabilities through data aggregation and visualization. Additionally, it discusses data analytics methodologies, including both qualitative and quantitative techniques, to drive business productivity and inform decision-making.

1. Basic Understanding of BUSINESS
INTELLIGENCE, DATAANALYTICS
and Data Visualization.
MSquare Systems Inc.,dba M-Square

2. What role does BI plays?
Ø BI addresses the specific business and technical challenges faced
by government agencies — including legacy systems, large data
volumes, data quality and consistency, diverse sets of users and
data security.
Ø Turn data into information that inspires understanding and
reduces the manual manipulation of reports.
Ø Empower analysts with user-driven Business Discovery
capabilities that enables them to quickly and easily explore data
in a natural way.
Ø Aggregate and analyze high volumes of data from multiple,
disparate sources.
Ø Search across all data quickly to see the big picture and make
better decisions to support the mission.

3. In a nutshell.

4. What is Business Intelligence?
v A broad category of software and solutions for gathering,
consolidating, analyzing, and providing access to data in a way
that lets enterprise users make better business decisions.
Aggregate Data
Database, Data
Mart, Data
Warehouse, ETL
Tools, Integration
Tools
Present
Data
Enrich
Data
Inform a
Decision
Reporting Tools,
Dashboards, Static
Reports, Mobile
Reporting, OLAP
Cubes
Add Context to
Create Information,
Descriptive Statistics,
Benchmarks,
Variance to Plan &
forecast
Decisions are
Fact-based and
Data-driven

5. Business Intelligence Methods.
Ø Advanced analytics
Ø Reporting
Ø Multidimensional
Ø OLAP – Online
Analytical Processing on
complex data.
Ø Mining visualization
Ø Data warehousing

6. Business Intelligence Trends in
Ø Mobile
Ø Cloud
Ø Social Media
Ø Advanced Analytics

7. Taking it to the cloud!
Ø Cloud-based business
intelligence model DHS
can now access business
intelligence functionality in
a software as a service
model via a private cloud,
paying only for the
resources it uses.

8. What BI technologies will be the most important to your organization
in the next 3 years?
Ø Predictive Analytics
Ø Visualization/Dashboards
Ø Master Data Management
Ø The Cloud
Ø Analytic Databases
Ø Mobile BI
Ø Open Source
Ø Text Analytics

9. OLAP
Ø Activities performed by end users
in online systems
v Specific, open-ended query generation
v SQL
v Ad hoc reports
v Statistical analysis
v Building DSS applications
Ø Modeling and visualization
capabilities
Ø Special class of tools
v DSS/BI/BA front ends
v Data access front ends
v Database front ends
v Visual information access systems

10. Data Mining
Ø Organizes and employs information and knowledge
from databases
Ø Statistical, mathematical, artificial intelligence, and
machine-learning techniques
Ø Automatic and fast
Ø Tools look for patterns
v Simple models
v Intermediate models
v Complex Models

11. Data Mining & Decission.
Ø Data mining application classes of problems
v Classification
v Clustering
v Association
v Sequencing
v Regression
v Forecasting
v Others
Ø Hypothesis or discovery driven
Ø Iterative
Ø Scalable

12. Knowledge Discovery in Databases
Ø Data mining used to find patterns in data
v Identification of data
v Preprocessing
v Transformation to common format
v Data mining through algorithms
v Evaluation

13. DataVisualization
Ø Technologies supporting
visualization and
interpretation
v Digital imaging, GIS, GUI,
tables, multi-dimensions,
graphs, VR, 3D, animation
v Identify relationships and
trends
Ø Data manipulation allows
real time look at
performance data

14. Multidimensionality
Ø Data organized according to business standards,
not analysts
Ø Conceptual
Ø Factors
v Dimensions
v Measures
v Time
Ø Significant overhead and storage
Ø Expensive
Ø Complex

15. Embracing Business Analytics and Optimization gives
organizations the answers they need to outperform
• Information Strategy
• Mastering Information
• Business Analytics
Rapid, informed, confident decisions
consistent across the organization
Business
Value
Use OverTime
Top performers are more
likely to use an analytic
approach over intuition*
5.4x
*within business processes

16. What does Data Analytics
mean?
Ø Data analytics refers to qualitative and
quantitative techniques and processes
used to enhance productivity and business
gain.
Ø Data is extracted and categorize to
identify and analyze behavioral data and
patterns, and techniques vary according to
organizational requirements.

17. Ø Exploratory data analysis
(EDA), where new features in
the data are discovered, and
Ø Confirmatory data analysis
(CDA), where existing
hypotheses are proven true or
false.
Ø Qualitative data analysis (QDA)
is used in the social sciences to
draw conclusions from non-
numerical data like words,
photographs or video.
Its broadly classified into

18. Relational Data (Tables/Transaction/
Legacy Data)
Text Data (Web)
Semi-structured Data (XML)
Graph Data
Social Network, Semantic
Web(RDF), …
Streaming Data
You can only scan the data once

19. Support & Partner
Getting Started or Support –
Muthu Natarajan
muthu.n@msquaresystems.com.
www.msquaresystems.com
Phone: 703-222-5500/202-400-5003.