Lecture 2

Lecture 2

1. Issue with Big Data Quality

   • Reasons
     • ► Volume is too big to be process in real-time or right time
   • Impacts
     • ► Cannot sense important events in timely fashion will lead to threats and missed opportunities ► Organizations making losses due to poor data quality
   • Characteristics of Data Value
     • ► Accuracy ► Completeness ► Consistency ► Uniqueness ► Timeliness

2. Business Intelligence

   • Functions
     • ► Involves the creation and use of: data warehouse, data mart, use of front end analytics tools ► Dashboards with visualization ► Alerting ► Data Exploration - slide/ dice, drill down/ up, what-if analysis ► Exception handlings, finding patterns ► Adhoc query, report generation
   • Benefits
     • ► Allows users to access data, ask queries, get adhoc reports, examine scenarios, create CRM activities, devise pricing,... For example, deep learning can be carried out by performing multilayer queries ► Data can be linked and data cubes are formed. Without it, raw data are kept in corporate databases with information scattered across multiple systems and may be seemed unrelated.
   • Objectives
     • ► COO: What are the problems with the operations and how to solve them? ► VPs Sales: What are the problems in sales revenue and how to solve them ► CEO: What are the strategic objectives, 5 goals, current issues that hinder the organization from achieving these goals
   • Competitive Advantage vs Degree of intelligence
     • ► Standard Report: What happened? ► Adhoc Report: How many, how often, where? ► Query/ Drill down: Where is the problem? ► Alerts: What actions are needed? ► Statistical Analysis: Why is it happening? ► Forecasting/ Extrapolation: What if these trends continue? ► Predictive modeling: What will happen next? ► Optimization: What's the best that can happen?

3. DW, OLTP, & OLAP

   • Data Warehouse