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Big Data in Business Application use case and benefits
- 1. Big Data in Business Applications, Use Cases, and Benefits
- 2. Data and History ● 1960-1970: The world of data (large data sets) was just getting started with the first data centers and the development of the relational database. ● 2005: People began to realize just how much data users generated through Facebook, YouTube, and other online services. ● 2010s: With the advent of the Internet of Things (IoT), more objects and devices are connected to the internet, gathering data on customer usage patterns and product performance.
- 3. What is Big Data? Big data is data that contains greater variety, arriving in increasing volumes and with more velocity. ● Variety: Refers to the many types of data that are available. ○ Structured relational database. ○ Unstructured and semistructured data types, such as text, audio, and video. ● Volume: The amount of data matters. ○ terabytes or petabytes. ● Velocity: The fast rate at which data is received and (perhaps) acted on. ○ Data streams directly into memory versus being written to disk. ○ Internet-enabled smart products operate in real time or near real time and will require real-time evaluation and action. In summary, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.
- 5. Big Data Technologies These technologies are the backbone of managing, processing, and extracting insights from the massive volume and variety of data. Hadoop, often represented by the Hadoop Distributed File System (HDFS) and the MapReduce programming model, allows distributed storage and parallel processing of data. This architecture is ideal for handling large-scale batch processing tasks. Spark, on the other hand, is designed for real-time and batch processing, with an emphasis on in-memory data processing, making it significantly faster than Hadoop's MapReduce. It is particularly suited for iterative and interactive analytics tasks. NoSQL databases, which stands for "Not Only SQL," offer a more flexible and scalable database structure than traditional relational databases. They are designed to handle unstructured and semi-structured data and can scale horizontally to accommodate vast amounts of data
- 6. Big Data Analytical Process Getting Data: We start by collecting information from different places like databases, social media, sensors, and logs. This is all about bringing together raw data. Fixing Data: Once we collect data, we often need to clean it up and get it ready to use. This means dealing with missing information, weird values, and changing data into a useful form. It's super important to make sure the data is good quality. Saving Data: We put the data in a good storage place, which could be a data warehouse, data lake, or cloud storage. The choice depends on what the organization needs and what technology they use. Working with Data: We use Big Data tools like Hadoop and Spark to handle a lot of data at once. In this step, we do things like change data, make it better, and do calculations. Understanding Data: We look at the processed data to find patterns, trends, and cool information. This is where we use statistics and machine learning tricks. Showing Data: To make the information easy to understand, we often use tools like Tableau or Power BI to create pictures and graphs. These visuals help to explain complicated information in a simpler way. Using Information: The main goal is to get useful information from looking at the data. This info can help in making decisions, planning strategies, and figuring out what to do. Making It Happen: Taking action based on what we learned is super important. It could mean improving how a business works, launching new things, or making customers happier. Checking and Changing: After making things happen, it's important to keep an eye on the results and make changes if needed. Big Data is an ongoing process, and organizations need to adapt to what's happening.
- 7. BENEFITS
- 8. Benefits of Big Data 1) Improved Decision-Making By having access to real-time and historical data The best example are global e-commerce websites that can easily track the behaviour pattern in demand 2) Enhanced Customer Understanding Ability to cluster customer behavior This also allows for personalised marketing strategies The result usually is an increased customer satisfaction and loyalty 3) Increased Operational Efficiency Ability to identify inefficient processes, which allows for easier optimizations These optimizations usually result in cost reductions
- 9. Benefits of Big Data (cont.) 4) Competitive Advantage Gather insights (example: emerging behaviour patterns) ahead of competitors 5) Innovation and Product Development Identifying market trends and demands Accelerating innovation through data insights This can result in improving current and creating new products and services 6) Risk Management Predictive analytics for risk assessment Early detection of potential issues Minimizing financial and operational risks
- 10. Benefits of Big Data (cont.) 7) Cost Reduction Efficient resource allocation Identifying cost-saving opportunities Improving overall financial performance 8) Improved Marketing and Sales Creation of Targeted marketing campaigns Sales forecasting and optimization Decisions based on data, for enhancing customer engagement (example: digital marketing campaigns) 9) Real-time Analytics Instant insights for quick decision-making Monitoring and responding to market changes
- 11. APPLICATIONS
- 12. Applications Big data is being used across various industries to improve efficiency and inform decision-making. Here are a few examples: Transportation: Big data optimizes GPS navigation, like in Google Maps, to suggest the least traffic-prone routes. Aviation uses it to analyze fuel efficiency and optimize safety . Vizion uses big data to track shipping containers for freight companies, while FourKites tracks packages in real time and predicts delivery times using data on traffic Oil, Gas & Renewable Energy: Big data analytics are used for tracking and monitoring oil well performance, predictive maintenance in remote locations, and optimizing drilling sites. It also plays a role in improving the safety of oil sites, fuel transportation, supply chain, and logistics . Banking and Financial Services: Banks leverage big data for fraud detection, risk management, and personalized marketing to create detailed customer profiles for targeted offerings .
- 13. Applications (cont.) Big data is being used across various industries to improve efficiency and inform decision-making. Here are a few examples: Marketing: Companies like Centerfield analyze customer data to gain insights into consumer behavior, informing sales strategies and client recommendations. Similarly, 3Q Digital employs big data to optimize marketing channel strategies and determine ad effectiveness . Also companies like Amazon use big data to target ads by analyzing consumer behavior on purchases, clicks, and preferences . Manufacturing & Supply Chain Management: In manufacturing, big data is crucial for predictive maintenance, operational efficiency, and production optimization. It helps to predict equipment failure, analyze production processes, forecast future demand, and decrease production costs
- 15. Walmart's Use of Big Data The company collects and analyzes vast amounts of data from various sources, including point-of-sale transactions, social media, online browsing (mobile apps, website) behavior, and supply chain management systems. ● Tracking customer purchasing patterns and preferences to make better product recommendations and improve inventory management. ● Optimize its pricing strategies, determining the optimal price points for various products to increase sales and profitability. ● Optimizing its supply chain, analyzing data on suppliers, logistics, and inventory levels, the company can identify bottlenecks and inefficiencies and make data-driven decisions to streamline operations and reduce costs.
- 17. Walmart Use Case: Click stream events from user interactions The use case is to take the click stream events, aggregate them based on the session id and generate metrics such as unique visitors, visits, orders, revenues, units, bounce rates, site error rates, performance metrics etc for both assigned and qualified experiments.
- 19. NETFLIX’s Use of Big Data In its early stages, Netflix revolutionized the entertainment industry by introducing a disruptive model that allowed customers to conveniently rent DVDs online, receiving them at their doorstep. Nevertheless, the true transformative moment occurred when Netflix astutely identified the latent opportunities within the vast reservoir of data amassed from their subscribers. Recognizing the potential insights encapsulated in this data proved to be the pivotal point, propelling Netflix beyond its initial disruption to redefine the streaming landscape and personalized content recommendations.
- 20. NETFLIX’s Use of Big Data Netflix utilized big data capabilities to comprehend customer behavior, elevate the user experience, and fine-tune their content library. Below are some of the primary technologies they deployed for these purposes: Recommendation Engine Netflix engineered a sophisticated recommendation system employing insights from its big data gathered. This system, fueled by extensive user data encompassing viewing patterns, ratings, and preferences, tailors personalized content suggestions for each viewer. The result is an enhanced viewing experience that boosts customer satisfaction and loyalty.
- 22. NETFLIX’s Use of Big Data Data Analytics: Netflix used data analytics, employing tools like Apache Hadoop and Apache Spark to gain real- time insights into viewer behavior and preferences. This empowered them to make informed, data-driven decisions on content acquisition, production, and targeted marketing campaigns. Impacts on Revenue and User Growth The incorporation of big data technologies transformed Netflix's fate, catapulting the company to unparalleled success by using: - Enhanced Personalization - Expanding Global Reach - Original Content Production
- 23. Adopting the capabilities of Big Data isn't merely a strategic choice; it signifies an evolutionary change in businesses towards a future characterized by innovative insights, data-guided decision-making, and success measured.