Business Intelligence, Cloud Computing, Data Analytics, Data Scrubbing, Data Mining, Big Data & Intelligence, How to use Data into Information, Decision Based,Methods for Business Intelligence, Advanced Analytics, OLAP, MultiDimensional Data, Data Visualization
Business intelligence, Data Analytics & Data Visualization
1. Basic Understanding of BUSINESS
INTELLIGENCE AND DATA
ANALYTICS FOR US FEDERAL
GOVERNEMENT.
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.
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
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
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.