Business intelligence, Data Analytics & Data Visualization

812 views
527 views

Published on

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

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
812
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
32
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Business intelligence, Data Analytics & Data Visualization

  1. 1. Basic Understanding of BUSINESS INTELLIGENCE AND DATA ANALYTICS FOR US FEDERAL GOVERNEMENT. MSquare Systems Inc.,dba M-Square
  2. 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. 3. In a nutshell.
  4. 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. 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. 6. Business Intelligence Trends in Ø Mobile Ø Cloud Ø Social Media Ø Advanced Analytics
  7. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 19. Support & Partner Getting Started or Support – Muthu Natarajan muthu.n@msquaresystems.com. www.msquaresystems.com Phone: 703-222-5500/202-400-5003.

×