MIS2502: Data Analytics The Information Architecture of an Organization. Jing Gong
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1 MIS2502: Data Analytics The Information Architecture of an Organization Jing Gong
2 What Do You Do With Data? Gather Retrieve Interpret
3 The Information Architecture of an Organization Data entry Transactional Database Data extraction Analytical Data Data analysis s real-time transactional data Called OLTP: Online transaction processing s historical transactional and summary data Called OLAP: Online analytical processing
4 The Transactional Database s real-time, transactional data In business, a transaction is the exchange of information, goods, or services. For databases, a transaction is an action performed in a database management system. transactional databases deal with both: they store information about business transactions using database transactions Examples of transactions Purchase a product Enroll in a course Hire an employee Data is in real-time Reflects current state How things are now
5 The Relational Paradigm How transactional data is collected and stored Primary Goal: Minimize redundancy Reduce errors Less space required Which of these do you think? is more important today Most database management systems are based on the relational paradigm MySQL, Oracle, Microsoft Access, SQL Server
6 The Relational Database Online Retailer Example A series of tables with logical associations between them The associations (relationships) allow the data to be combined Product ProductID Name Description Price Shipping SalesRank Review ReviewID ProductID StarRating Text ReviewerName Likes
7 Why more than one table? Every review has an associated product Every product can have a review Products and reviews have a unique ID number Split the details off into separate tables This is good because: Product ProductID Name Description Price Shipping SalesRank Review ReviewID ProductID StarRating Text ReviewerName Likes Information is entered and stored once Minimizes redundancy
8 Analyzing transactional data Can be difficult to do from a relational database Having multiple tables is good for storage and data integrity, but bad for analysis Tables must be joined together before analysis can be done The solution is the Analytical Data Relational databases are optimized for storage efficiency, not retrieval Analytical data stores are optimized for retrieval and analysis, not storage efficiency and data integrity
9 The Analytical Data s historical and summarized data Historical means we keep everything Data is extracted from the transactional database and reformatted for the analytical data store Extract Transform Load Transactional Database Query Data conversion Query Analytical Data We ll discuss this in much more detail later in the course!!
10 The Dimensional Paradigm Data is stored like this around a business event and can be summarized like this for analysis Product M&Ms Diet Coke Doritos Famous Amos Ardmore, PA Temple Main Cherry Hill, NJ Mar King of Prussia, PA Jan Feb. 2012
11 Dimensional Data and the Data Cube or it can be expanded in detail like this so that data mining (complex statistical analysis) can be done. Sales ID Qty. Sold Total Price Prod. ID Prod. Name Prod. Price Prod. Weight ID Address City State Type Time ID Day Month Year Sales Fact Product Dimension Dimension Time Dimension
12 Comparing Transactional and Analytical Data s Transactional Database Based on Relational paradigm Storage of real-time transactional data Optimized for storage efficiency and data integrity Analytical Data Based on Dimensional paradigm Storage of historical transactional data Optimized for data retrieval and summarization Supports day-to-day operations Supports periodic and ondemand analysis
13 The agenda for the course Weeks 1 through 6 Weeks 7 through 8 Weeks 9 through 15 Data entry Transactional Database Data extraction Analytical Data Data analysis s real-time transactional data s historical transactional and summary data Data interpretation, visualization, communication
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