Application_of_big_data_presentation.pptx

Application_of_big_data_presentation

1. APPLICATION OF

2. WHAT IS BIG DATA Big data is a combination of structured, semi- structured and unstructured data collected by organizations that can be mined for information and used in: machine learning projects predictive modeling and other advanced analytics applications What is predictive modeling? Predictive modeling is a mathematical process used to predict future events

3. WHAT ARE THE 5 V'S OF BIG DATA? Volume the size and amounts of big data that companies manage and analyze Value comes from insight discovery and pattern recognition that lead to more effective operations, stronger customer relationships and other clear and quantifiable business benefits Variety the diversity and range of different data types, including unstructured data, semi-structured data and raw data

4. Velocity the speed at which companies receive, store and manage data – e.g., the specific number of social media posts or search queries received within a day, hour or other unit of time Veracity: the “truth” or accuracy of data and information assets, which often determines executive- level confidence Variability: the changing nature of the data companies seek to capture, manage and analyze – e.g., in sentiment or text analytics, changes in the meaning of key words or phrases

5. APPLICATIONS OF BIG DATA IN THE HEALTHCARE SECTOR It can helps doctors to recommend the best solutions by old patient’s feedback Track the improvement of the patient’s health based on real time data collection which can be later used for analysis purposes Can be used to record the spread rate of chronic disease Patient can easily find the nearest and the best reviewed hospital (so proper awareness can be made)

6. APPLICATIONS OF BIG DATA IN THE EDUCATION INDUSTRY Can be used to track the interest of student through the usage of big data terminologies Teacher can track the time spend by the student on different pages Recommendation of courses can be made based on students interest Teacher’s performance can be traced through feedback of student Student’s and teacher performance can be analyzed to take further decisions on updating the system or flow of education

7. APPLICATIONS OF BIG DATA IN THE COMMUNICATIONS, MEDIA AND ENTERTAINMENT INDUSTRY Organizations in this industry simultaneously analyze customer data along with behavioral data to create detailed customer profiles that can be used to: Create content for different target audiences Recommend content on demand Measure content performance Spotify, an on- demand music service, uses Hadoop Big Data analytics, to collect data from its millions of users worldwide and then uses the analyzed data to give informed music recommendations to individual users.

8. APPLICATIONS OF BIG DATA IN THE FINANCES INDUSTRY Big Data transforms finance by catching fraud with smart algorithms in real- time. It's a game- changer in risk management, helping banks understand and handle risks in investments and operations. Big Data tailors financial services by analyzing customer behavior, enhancing satisfaction, and building long- term loyalty. This shift in decision- making signifies a significant change in the finance industry.

9. APPLICATIONS OF BIG DATA IN THE RETAIL SECTOR Big Data helps stores understand what customers like. It makes shopping more personal based on what customers prefer. Stores use it to manage stock better, so they don't have too much or too little. Prices are set smarter based on what people are buying. Overall, it makes stores better at giving customers what they want.

10. APPLICATIONS OF BIG DATA IN THE MANUFACTURER SECTOR Big Data helps factories predict when machines might break, saving money and time. It also makes sure products are good quality by checking them as they're made. Big Data helps factories use resources better, making everything run smoother. Using Big Data and smart tools is not just a tech upgrade but really important for making things better in factories.

11. ADVANTAGES OF APPLICATION OF BIG DATA Informed Decision- Making: Organizations are able to make data- driven, well- informed decisions thanks to big data analytics. A thorough grasp of trends, patterns, and consumer behavior is possible through the use of insights obtained from sizable and diverse datasets. Operational Efficiency: Big Data analytics can help organisations optimise their operations. For example, predictive analytics enables proactive maintenance, which lowers manufacturing downtime and boosts overall productivity across a range of industries. Personalization and Customer Experience: Big Data makes it easier for customers to have individualised experiences. Businesses can better satisfy and retain customers by customising their offerings in terms of goods, services, and marketing tactics. Innovation and Product Development: Big Data provides insights into consumer demands, market trends, and emerging technologies, which spurs innovation. This facilitates the creation of novel goods and services that meet consumer demands. Cost Reduction: Organisations can save costs through improving supply chain management, anticipating equipment failures, and optimising processes. Finding areas where efficiency improvements can result in lower operating costs is made easier with the aid of big data analytics. Competitive Advantage: Businesses that use big data well have a competitive advantage. Companies are positioned as industry leaders when they can innovate based on data insights, anticipate customer needs, and react swiftly to changes in the market.

12. DISADVANTAGES OF APPLICATION OF BIG DATA Data Privacy and Security Concerns: Security and privacy issues are brought up by the extensive gathering and storing of private and sensitive data. Information misuse, data breaches, and unauthorised access can all have serious repercussions. Skill Shortage and Expertise Gap: The field of big data analytics is in need of more qualified workers. Finding and keeping specialists who can handle and analyse big datasets efficiently is a common challenge for organisations. Data Quality and Veracity: Ensuring data reliability and accuracy can be difficult. Inaccurate or lacking data can result in faulty analysis and, ultimately, poor decision- making. Integration Challenges: It can be difficult and expensive to integrate Big Data solutions into the current infrastructure. Compatibility problems may arise because legacy systems are not built to handle the volume and complexity of Big Data.