Session 10 : E-business models, Big Data, Data Mining, Cloud Computing

Transcription

1 INFORMATION STRATEGY Session 10 : E-business models, Big Data, Data Mining, Cloud Computing Tharaka Tennekoon B.Sc (Hons) Computing, MBA (PIM - USJ) POST GRADUATE DIPLOMA IN BUSINESS AND FINANCE 2014

2 Internet Five Forces

3 Internet Value Chain

4 Data Mining

5 Data Mining Data mining, or knowledge discovery, is the computer-assisted process of digging through and analyzing enormous sets of data and then extracting the meaning of the data. Data mining tools predict behaviors and future trends, allowing businesses to make proactive, knowledge-driven decisions. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. Data mining is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Data mining derives its name from the similarities between searching for valuable information in a large database and mining a mountain for a vein of valuable ore. Both processes require either sifting through an immense amount of material, or intelligently probing it to find where the value resides.

6 Data Mining ERP CRM SCM

7 Data Mining Relationships and Patterns Classes: Stored data is used to locate data in predetermined groups. For example, a restaurant chain could mine customer purchase data to determine when customers visit and what they typically order. This information could be used to increase traffic by having daily specials. Clusters: Data items are grouped according to logical relationships or consumer preferences. For example, data can be mined to identify market segments or consumer affinities. Associations: Data can be mined to identify associations. Example : beer-diaper purchasing pattern. Sequential patterns: Data is mined to anticipate behavior patterns and trends. For example, an outdoor equipment retailer could predict the likelihood of a backpack being purchased based on a consumer's purchase of sleeping bags and hiking shoes.

8 Data Mining Major components/ steps in Data Mining. 1. Extract, Transform, and Load (ETL) transaction data onto the data warehouse system. 2. Store and manage the data in a database system. 3. Provide data access to business analysts and information technology professionals. 4. Analyze the data by application software. 5. Present the data in a useful format, such as a graph or table. Different levels/ techniques of analysis o Artificial neural networks o Genetic algorithms o Decision trees o Nearest neighbor method o Rule induction o Data visualization

9 Data Mining Decision Tree : Credit Risk

10 Data Mining Decision Tree : Waiting Time

11 Data Mining Personal Loan offer

12 Data Mining - Benefits Basket Analysis - predict future customer behavior by past performance, including purchases and preferences o Credit Card usage fraud, limits, promotions o Telecom services usage innovators, early adopters o Fraudulent insurance claims Sales Forecasting when customers will buy again (realistic, optimistic and pessimistic) Database Marketing create consumer profiles Merchandise Planning product selection, balancing stock, pricing Call Detail record analysis customer service hotline o Improve customer experience o Average time per call o Common issues - Interactive voice response solution (leads to cost savings) Customer Loyalty predict when customers switch to competition, LCV, what keeps them from churning Segment customers Segment consumers (STP), identify segment competitors Product mix which product to which segment, new features Warranties how many are availed (e.g. 110% money back guarantee)

13 Cloud Computing

14 Cloud Computing Support Applications Upgrades

15 Cloud Computing

16 Cloud Computing = Utility = Software as a Service (SaaS) Infrastructure as a Service (IaaS) Platform as a Service (PaaS) Network as a Service (NaaS)

17 Cloud Computing

18 Cloud Computing Good vs. Bad Advantages 1. Cost Efficiency 2. Convenience and continuous availability 3. Backup and Recovery 4. Cloud is environmentally friendly 5. Resiliency and Redundancy 6. Scalability and Performance 7. Quick deployment and ease of integration 8. Increased Storage Capacity 9. Device Diversity and Location Independence (collaboration) 10.Increased Competitiveness Disadvantages 1. Security and privacy 2. Dependency and vendor lock-in 3. Technical Difficulties and Downtime (e.g. nonavailability of internet) 4. Limited control and flexibility 5. Increased Vulnerability

19 Tharaka Tennekoon, B.Sc (Hons), MBA (PIM - USJ) info@topmostline.com