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 muthu.n@msquaresystems.com.

www.msquaresystems.com Phone: 703-222-5500/202-400-5003.