Big Data Analytics PDF

LEVERAGE BIG DATA TO GAIN A COMPETITIVE EDGE THE ROLE OF BIG DATA ANALYTICS IN BUSINESS DECISION MAKING PRESENTED BY GROUP III: AUSTERO, CRISTELA MARIE M. COLARINA, SHEILA MAE MIRAFUENTES, LOUISE AMOR BIG DATA ANALYTICS Big Data...

LEVERAGE BIG DATA TO GAIN A COMPETITIVE EDGE THE ROLE OF BIG DATA ANALYTICS IN BUSINESS DECISION MAKING PRESENTED BY GROUP III: AUSTERO, CRISTELA MARIE M. COLARINA, SHEILA MAE MIRAFUENTES, LOUISE AMOR BIG DATA ANALYTICS Big Data analytics is transforming how organizations make decisions. By leveraging extensive datasets, businesses can gain insights that drive strategy, enhance customer engagement, and improve operational efficiency. This presentation delves into the history, importance, functionality, applications, benefits, challenges, and future trends of Big Data analytics in business. HISTORY AND EVOLUTION OF BIG DATA ANALYTICS 1950s - Early Analytics 1980s - Emergence of Databases 2000s - Big Data Concept Present - Real-Time Analytics IMPORTANCE OF BIG DATA ANALYTICS Cost Reduction: Cloud-based solutions and data lakes help manage large data volumes affordably, minimizing storage costs. Faster Decision-Making: Real-time data analysis enables companies to quickly adapt to market changes, enhancing responsiveness. Opportunity Identification: Analyzing diverse data sources allows organizations to uncover new opportunities and refine strategies. HOW DATA ANALYTICS WORKS AND KEY TECHNOLOGIES Types of Big Data Structured Data Unstructured Data Semi -Structured Data Structured Data - refers to the data that has a proper structure associated with it. -pre-defined data models like databases -usually text only -easy to search and filter Examples: Dates, phone numbers, transaction information Unstructured Data -refers to the data that does not have any structure associated with it at all. - usually stored as different types of files. Examples: Social media Data, Audio files, Images Semi-Structured Data -refers to the data that does not have a proper structure with it. - considerably easier to analyze than unstructured data. Examples: Emails, Csv Files, JSON Files  Characteristics of Big Data Big Data is categorized into 3 important characteristics Volume Velocity Variety  Big Data Application Domains ✓ Banking and Securities ✓ Insurance ✓Transfortation ✓ Education ✓ Manufacturing ✓ Energy and Utilities ✓ Healthcare ✓ Madia and Entertainment KEY TECHNOLOGIES IN DATA ANALYTICS Cloud Computing Predictive Analytics Data Management Text Mining Data Mining In-Merory Analytics Data Storage Machine Learning Hadoop  Functionality: Cloud computing provides scalable, subscription-based access to data storage and processing power. Benefits: It eliminates physical and financial barriers, making it easier for organizations of all sizes to align IT resources with evolving business goals. This flexibility supports quick deployment and enhances overall IT efficiency.  Functionality: Ensures high-quality data governance through established processes and standards. Benefits: Reliable data is crucial for analysis. Organizations implement master data management to unify data sources and maintain consistency across the enterprise, allowing for accurate and trustworthy insights.  Functionality: Examines large datasets to discover patterns and relationships. Benefits: Data mining software filters out noise and identifies relevant information, enabling businesses to answer complex questions and make informed decisions quickly. It accelerates the process of uncovering actionable insights from data.  DATA STORAGE HADOOP Types: Includes Data Lakes and Data Functionality: An open-source Warehouses. framework designed for storing and processing large datasets across Data Lakes: Store vast amounts of raw, distributed computing clusters. unstructured data in its native format, ideal for data types like social media Benefits: It handles the increasing posts and streaming content. volumes and varieties of data efficiently. Hadoop’s cost-effectiveness Data Warehouses: Centralize structured stems from its ability to run on data for easier access and analysis. commodity hardware, making it Both systems complement each other, accessible for businesses looking to allowing organizations to manage leverage big data. different data types effectively. Functionality: Analyzes data stored in system memory rather than on traditional storage drives. Benefits: This method significantly reduces latency, allowing organizations to derive insights and react to data quickly. It supports agile decision- making and iterative analysis, making it ideal for dynamic business environments.  MACHINE LEARNING PREDICTIVE ANALYTICS Functionality: A subset of AI that Functionality: Uses historical data, enables systems to learn from data statistical algorithms, and machine and improve their performance over learning to forecast future time. outcomes. Benefits: Machine learning Benefits: This type of analytics automates the creation of analytical provides organizations with the models, enabling faster and more likelihood of various scenarios, accurate results. This technology enhancing confidence in decision- helps businesses identify making. Common applications opportunities and mitigate risks by include fraud detection, risk analyzing complex datasets at scale. assessment, and operational planning. Functionality: Analyzes unstructured text data to uncover hidden insights. Benefits: By employing natural language processing and machine learning techniques, text mining allows businesses to analyze vast amounts of textual information (e.g., customer feedback, social media) to identify trends and relationships that inform strategy. KEY APPLICATIONS OF BIG DATA ANALYTICS 1. Customer Insights and Personalization: Targeted Marketing: Segmenting customers for tailored marketing campaigns increases engagement and conversion rates. Enhanced Customer Experience: Understanding customer pain points allows businesses to improve services and products. 2. Operational Efficiency and Process Optimization: Identifying Bottlenecks: Analyzing operations helps pinpoint inefficiencies, leading to streamlined processes. Optimizing Supply Chains: Big Data facilitates better logistics and inventory management, minimizing costs and enhancing service. 3. Predictive Analytics and Forecasting: Historical Data Analysis: Helps organizations anticipate market changes and make informed predictions for demand and sales. REAL-WORLD EXAMPLES OF BIG DATA SUCCESS STORIES  CHALLENGES AND CONSIDERATIONS IN LEVERAGING BIG DATA 1. Data Security and Privacy: Organizations must implement robust security measures to protect sensitive information. 2. Data Quality and Integration: Integrating data from diverse sources requires effective strategies to ensure consistency and reliability. 3. Skills and Resources: The demand for data professionals necessitates investments in training and development to build in-house expertise. THANK YOU! SHORT QUIZ 1. It examines large datasets to discover patterns and relationships. 2. This type of analytics provides organizations with the likelihood of various scenarios, enhancing confidence in decision-making. 3. A data that does not have a proper structure and considerably easier to analyze than unstructured data. 4. An open-source framework designed for storing and processing large datasets across distributed computing clusters. 5. Give one characteristic of Big Data. KEY TO CORRECTION 1. It examines large datasets to discover patterns and relationships. - Data Mining 2. This type of analytics provides organizations with the likelihood of various scenarios, enhancing confidence in decision-making. - Predictive Analytics 3. A data that does not have a proper structure and considerably easier to analyze than unstructured data. - Semi-structured Data 4. An open-source framework designed for storing and processing large datasets across distributed computing clusters. -Hadoop 5. Give one characteristic of Big Data. - Volume, Variety, Velocity