Business intelligence and analytics
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Transcript of Business intelligence and analytics
BUSINESS INTELLIGENCE AND ANALYTICS
PRESENTED BY
RAJIV KUMAR V
13M510
CSED
CONTENTS• INTRODUCTION
• BIG DATA
• CHALLENGES FACED BY BUSINESSES
• WHAT IS BIA?
• ANALYTICS
• STAGES IN BIA
• CONCLUSION
• REFERENCES
INTRODUCTION
• TECHNOLOGIES, SYSTEMS, PRACTICES
• ANALYZE CRITICAL BUSINESS DATA
• BETTER UNDERSTAND ITS BUSINESS AND MARKET
• PROVIDE BUSINESS MANAGERS AND ANALYSTS TO CONDUCT APPROPRIATE ANALYSES
• IMPROVE BUSINESS DECISION MAKING
BIG DATA
• DEFINED AS WHAT FIRMS CANNOT HANDLE WITH TYPICAL DATABASE SOFTWARE
• COMPUTING SYSTEMS TODAY ARE GENERATING OVER 15 PETABYTES OF NEW INFORMATION EVERY DAY
• OVERWHELMING AMOUNT OF SENSOR-GENERATED DATA
• USER-GENERATED CONTENTS AVAILABLE FROM WEB, SOCIAL MEDIA AND MOBILE
• HIGH DIMENSIONALITY
• COMPUTERIZED TRANSACTIONS
• DATA IS GETTING UBIQUITOUS AND CHEAP
BIG DATA[CONTD…]
• 80% OF THE DATA GENERATED EVERYDAY IS TEXTUAL AND UNSTRUCTURED
• 3 VS OF DATA:
• VOLUME (FROM GIGABYTES TO PETABYTES),
• VELOCITY (FROM BATCH TO NEAR-TIME DATA AND REAL-TIME STREAMS),
• VARIETY (FROM STRUCTURED RECORDS TO SEMI-STRUCTURED AND UNSTRUCTURED TEXT
• HUGE VOLUMES OF DATA STRAINING OUR TECHNICAL CAPACITY TO MANAGE IT
CHALLENGES FACED BY BUSINESSES
• BIG DATA ANALYSIS REQUIRES NEW APPROACHES TO OBTAIN INSIGHTS
• ACCESS TO DIVERSE AND DISPARATE DATA IS DIFFICULT
• MANIPULATION AND TRANSFORMATION OF BIG DATA
• DEVELOPING THE CAPABILITY TO UNDERSTAND AND INTERPRET THE DATA
WHAT IS BIA?
• INTERDISCIPLINARY AREA THAT INTEGRATES
• DATA MANAGEMENT
• DATABASE SYSTEMS
• DATA WAREHOUSING
• DATA MINING
• NATURAL LANGUAGE PROCESSING (TEXT ANALYTICS AND TEXT MINING. I.E. STATISTICAL, LINGUISTIC AND STRUCTURAL TECHNIQUES FROM TEXTUAL SOURCES)
• NETWORK ANALYSIS/SOCIAL NETWORKING
• STATISTICAL ANALYSIS
BIA [CONTD...]
• ANALYZING TRENDS
• CREATING PREDICTIVE MODELS FOR FORECASTING
• OPTIMIZING BUSINESS PROCESSES
• REPORTING DATA
• TURNING DATA INTO KNOWLEDGE AND INTELLIGENCE
DATA WAREHOUSING
DATA CUBE
TEXT MINING
TEXT MINING[CONTD…]
SOCIAL NETWORK ANALYSIS
ANALYTICS
• MAIN CATEGORIES OF ANALYTICS:
• (1) DESCRIPTIVE :THE USE OF DATA TO FIND OUT WHAT HAPPENED IN THE PAST;
• (2) PREDICTIVE :
• USE OF DATA TO FIND OUT WHAT COULD HAPPEN IN THE FUTURE
• APPLICATION OF STATISTICAL OR STRUCTURAL MODELS FOR PREDICTIVE FORECASTING OR CLASSIFICATION
• (3) PRESCRIPTIVE :THE USE OF DATA TO PRESCRIBE THE BEST COURSE OF ACTION FOR THE FUTURE.
• THREE BROAD RESEARCH DIRECTIONS:
• (A) BIG DATA ANALYTICS
• (B) TEXT ANALYTICS
• (C) NETWORK ANALYTICS
STAGES IN BIA
• DESIGNING TOOLS
• FOR CONVERTING AND INTEGRATING ENTERPRISE-SPECIFIC DATA
• FOR EXTRACTION
• TRANSFORMATION,
• AND LOADING (ETL) OF DATA
• FOR DATA CHARACTERISTICS
• DATABASE QUERY
• ONLINE ANALYTICAL PROCESSING (OLAP)
• FOR ANALYZING AND VISUALIZING VARIOUS METRICS USING ADVANCED REPORTING TOOLS
STAGES [CONTD…]
• ADVANCED KNOWLEDGE DISCOVERY FOR ASSOCIATION RULE MINING
• DATABASE SEGMENTATION AND CLUSTERING
• ANOMALY/OUTLIER DETECTION
• PREDICTIVE MODELING IN HUMAN RESOURCES
• ACCOUNTING
• FINANCE
• AND MARKETING APPLICATIONS.
CONCLUSION
• BUSINESSES ARE GAINING INSIGHTS FROM THE GROWING VOLUMES OF DATA GENERATED BY ENTERPRISE-WIDE APPLICATIONS
• ENTERPRISE RESOURCE PLANNING (ERP)
• CUSTOMER RELATIONSHIP MANAGEMENT (CRM)
• SUPPLY-CHAIN MANAGEMENT (SCM)
• KNOWLEDGE MANAGEMENT
• COLLABORATIVE COMPUTING
• WEB ANALYTICS
• USED IN
• AIRLINES, ASTRONOMY, BUSINESS
• IT AND TELECOMMUNICATION FIRMS
• PHYSICS, SEARCH ENGINES AND MORE
REFERENCES:• BUSINESS INTELLIGENCE AND ANALYTICS: RESEARCH DIRECTIONS
• EE-PENG LIM, HSINCHUN CHEN, GUOQING CHEN.
• BUSINESS INTELLIGENCE AND ANALYTICS EDUCATION, AND PROGRAM DEVELOPMENT: A UNIQUE OPPORTUNITY FOR THE INFORMATION SYSTEMS DISCIPLINE
• ROGER H. L. CHIANG, PAULO GOES, EDWARD A. STOHR.
• WIKIPEDIA
THANK YOU
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