Big Data and Data Analytics Module 2

Big Data and Data Analytics Module 2

• 1. Big Data and Data Analytics Big Data and Data Analytics
• 2. CONTENTS CONTENTS Module 1: Big Data Module 2: Business Intelligence/Analytics Module 3: Visualization Module 4: Data Mining
• 3. Business Analytics Business Analytics MODULE 2
• 4. Slide1111 Business Analytics/Business intelligence (BI) is a broad category of applications, technologies, and processes for: gathering, storing, accessing, and analyzing data to help business users make better decisions. Business Analytics/Business Intelligence
• 5. Slide1112 Things Are Getting More Complex Many companies are performing new kinds of analytics (**sentiment analysis, etc.), to better and more quickly understand and respond to what customers are saying about them and their products. The cloud, and appliances are being used as data stores Advanced analytics are growing in popularity and importance **Sentiment analysis (also known as opinion mining) refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials.
• 6. Uncertainty of Data Uncertainty of Data
• 7. Analytics Models Analytics Models 7 Prescriptive Analytics Predictive Analytics Diagnostic Analytics What happened? What will happen? How can we make it happen? Why did it happen? Descriptive Analytics VALUE DIFFICULTY Hindsight Insight Foresight Optimization Information
• 8. Slide1113 Descriptive Analytics What has occurred? Descriptive analytics, such as data visualization, is important in helping users interpret the output from predictive and predictive analytics. •Descriptive analytics, such as reporting/OLAP, dashboards, and data visualization, have been widely used for some time. •They are the core of traditional BI.
• 9. Slide1114 Predictive Analytics What will occur? Marketing is the target for many predictive analytics applications. Descriptive analytics, such as data visualization, is important in helping users interpret the output from predictive and prescriptive analytics. Algorithms for predictive analytics, such as regression analysis, machine learning, and neural networks, have also been around for some time.
• 10. Slide1115 What should occur? Prescriptive analytics can benefit healthcare strategic planning by using analytics to leverage operational and usage data combined with data of external factors such as economic data, population demographic trends and population health trends, to more accurately plan for future capital investments such as new facilities and equipment utilization as well as understand the trade-offs between adding additional beds and expanding an existing facility versus building a new one. Prescriptive analytics are often referred to as advanced analytics. Often for the allocation of scarce resources Optimization Predictive Analytics
• 11. Slide1117 Analytics are a competitive requirement For BI-based organizations, the use of BI/analytics is a requirement for successfully competing in the marketplace. TDWI report on Big Data Analytics found that 85% of respondents indicated that their firms would be using advanced analytics within three years IBM/MIT Sloan Management Review research study found that top performing companies in their industry are much more likely to use analytics rather than intuition across the widest range of possible decisions. Organizational Transformation
• 12. Complex Systems Require Analytics Complex Systems Require Analytics Tackle complex problems and provide individualized solutions Products and services are organized around the needs of individual customers Dollar value of interactions with each customer is high There is high level of interaction with each customer Examples: IBM, World Bank, Halliburton
• 13. Volume Operations Require Analytics Volume Operations Require Analytics Serves high-volume markets through standardized products and services Each customer interaction has a low dollar value Customer interactions are generally conducted through technology rather than person-to-person Are likely to be analytics-based Examples: Amazon.com, eBay, Hertz
• 14. The Nature of the Industry The Nature of the Industry Online retailers like Amazon.com and Overstock.com are high volume operations who rely on analytics to compete. When you enter their sites a cookie is placed on your PC and all clicks are recorded. Based on your clicks and any search terms, recommendation engines decide what products to display. After you purchase an item, they have additional information that is used in marketing campaigns. Customer segmentation analysis is used in deciding what promotions to send you. How profitable you are influences how the customer care center treats you. A pricing team helps set prices and decides what prices are needed to clear out merchandise. Forecasting models are used to decide how many items to order for inventory. Dashboards monitor all aspects of organizational performance
• 15. Slide1127 Knowledge Requirements for Advanced Analytics Choosing the right data to include in models is important. Important to have some thoughts as to what variables might be related. Domain knowledge is necessary to understand how they can be used. Role of Business Analyst is crucial Consider the story of the relationship between beer and diapers in the market basket of young males in convenience stores. You still have to decide (or experiment to discover) whether it is better to put them together or spread them across the store (in the hope that other things will be bought while walking the isles). The findings were that men between 30- 40 years in age, shopping between 5pm and 7pm on Fridays, who purchased diapers were most likely to also have beer in their carts. This motivated the grocery store to move the beer isle closer to the diaper isle and instantaneously, a 35% increase in sales of both!