Basic Understanding of BUSINESS INTELLIGENCE AND DATA
ANALYTICS FOR US FEDERAL GOVERNEMENT.
MSquare Systems Inc.,dba M-Square
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.
In a nutshell.
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
Business Intelligence Methods.
Ø Advanced analytics Ø Reporting Ø Multidimensional Ø OLAP – Online
Analytical Processing on complex data.
Ø Mining visualization Ø Data warehousing
Business Intelligence Trends in
Ø Mobile
Ø Cloud
Ø Social Media
Ø Advanced Analytics
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.
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
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
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
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
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
Data Visualization
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
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
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 Over Time
Top performers are more likely to use an analytic approach over intuition* 5.4x
*within business processes
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.
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
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
Support & Partner
Getting Started or Support –
Muthu Natarajan [email protected].
www.msquaresystems.com Phone: 703-222-5500/202-400-5003.