Business intelligence, Data Analytics & Data Visualization
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The document provides an overview of business intelligence (BI) and data analytics for U.S. federal government agencies, highlighting their roles in addressing challenges such as legacy systems and data security while empowering analysts to make data-driven decisions. It discusses key BI technologies and trends, including cloud-based models and predictive analytics, alongside various methodologies for data mining and visualization techniques. The text emphasizes the importance of utilizing analytics for improved decision-making and operational efficiency within organizations.
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
- 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.