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

ANY QUERIES???