MIS5101 WK10 Outcome Measures


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Slides from week 10 of MIS5101: Business Intelligence taught by Prof. Steven L. Johnson at Temple University Fox School of Business.

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MIS5101 WK10 Outcome Measures

  1. 1. P R O F . S T E V E N L . J O H N S O N T w i t t e r : @ S t e v e n L J o h n s o n http://stevenljohnson.org http://community.mis.temple.edu/mis5101fall10/ MIS5101: Business Intelligence Week 10 – Outcome Measures
  2. 2. Schedule Reminders   11/10 Computer Lab – Accessing Data (SQL)   11/17 Computer Lab – Analyzing Data (Excel)   11/24 Happy Thanksgiving (Eve)   12/1 Topic: Security, Privacy and Data Mining (2 Cases)   12/8 Group Project Due Group Project Presentations   12/15 Final Exam
  3. 3. Today’s Agenda   Last Week Discussion   Case Discussion   Blog + Reading Discussion   NoSQL + SQL
  4. 4. Case Analysis Discussion Questions   If you ran an in-vitro fertilization clinic what are 2-3 key metrics you would want to track and why?   Are these the same metrics a potential IVF customer would care about?   Are they the same metrics that a national standards body should focus on?   What opportunities and challenges are there in developing better outcome measures and reporting for the IVF field?   What specific data, if any, do you think Dr. Goldfarb should advocate developing national standards for collecting and reporting?
  5. 5. 100 Second Reflection 1.  What is something you want to remember about the outcome measures at the end of the semester? 2.  What would you like to know more about? 3.  Any comments?
  6. 6. Blog Discussion   Performance Measurement   KPIs   Performance measurement systems   Performance measurement as a process   Deciding what to measure   Gathering performance data   Interpreting performance results   Avoiding performance measurement pitfalls   From performance measurement to performance management
  7. 7. Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics Summary of section 4.6, performance metrics   Task success metrics – characteristics of completion   Time-on-task - how quickly   Errors - number of mistakes   Efficiency - amount of effort (cognitive and physical)   Learnability –changes in efficiency over time Chapter 8   Combined and comparative measures 7
  8. 8. NoSQL Use Cases   Frequently-written, rarely read statistical data (e.g., a web hit counter): in-memory key/value store (Redis), or update-in-place document store (MongoDB)   Big Data (e.g., weather stats or business analytics): freeform, distributed db system (Hadoop).   Binary assets (e.g., MP3s and PDFs): datastore serves directly to browser (Amazon S3)   Transient data (e.g., web sessions, locks, or short-term stats): transient datastore (e.g., Memcache)   Replicate your data set to multiple locations (e.g.,as syncing a music database between a web app and a mobile device): add replication features (CouchDB).   High availability apps where minimizing downtime is critical: automatically clustered, redundant setup of datastores (Cassandra,Riak)   Highly normalized, transactional, ad-hoc-queries: SQL databases. Adding new tools to our toolbox, not removing old ones. Source: NoSQL, Heroku, and You - http://blog.heroku.com/archives/2010/7/20/nosql/
  9. 9. Introduction to SQL   Database Languages   Create database and table structures   Perform basic data management chores (add, delete, and modify)   Perform complex queries to transform data into useful information   Structured Query Language (SQL) is a database computer language designed for managing data in relational database management systems (RDMS)   Data definition language (DDL)   Data manipulation language (DML) 9
  10. 10. DDL vs. DML examples Name DDL: Data Definition Language DML: Date Manipulation Language Purpose Defines Structure of Database and Database Objects Manipulates the Data housed in the tables Add Create table: creates a new table Insert Into: adds a new record to a table Change Alter table: modifies the tables structure (add a column, change a datatype, add constraint, etc.) Update: changes the values of an attribute in a record Remove Drop table: deletes the table from the database Delete: deletes a record from a table 10
  11. 11. Data Dictionary and System Catalog   Data dictionary   Provides detailed account of all tables found within database   Metadata   Attribute names and characteristics   System catalog   Detailed data dictionary   System-created database   Stores database characteristics and contents   Tables can be queried just like any other tables   Automatically produces database documentation 11
  12. 12. Most Common Data Types Data Type Data Type Description CHAR(n) •  fixed length column can contain any printable characters. •  If the data entered into CHAR field < length of field, field is padded with spaces. •  maximum length of CHAR column ~ 200. e.g: a state abbreviation - CHAR(2) since it is always 2 characters long. VARCHAR2(n) variable length column with a fixed length. If the length of the data is less than the maximum length of the field, then the field is not padded with spaces. •  maximum length of the column ~ 2000. e.g: A customer’s first name - VARCHAR2(35) since name length is variable. NUMBER Integer and real values occupying up to 40 spaces. INTEGER Same as number, but no decimals. DATE Contains a date and time between the 1st of January 4712 BC to the 31st of December 4712 AD. •  Standard date format: DD-MMM-YY (i.e. 01-JAN-99) •  Any other format will require input mask. 12
  13. 13. SQL Data Integrity Constraints   Entity integrity - enforced automatically with PRIMARY KEY constraint   Referential integrity - enforced FOREIGN KEY constraint   Other specifications to ensure conditions met:   ON DELETE RESTRICT   ON UPDATE CASCADE 13
  14. 14. Entity Relationship Diagram From Wikipedia (ERD) http://en.wikipedia.org/wiki/Entity-relationship_diagram
  15. 15. SQL Select Examples   Mathematical operators   Mathematical operators on character attributes   Mathematical operators on dates 15 SELECT P_DESCRIPT, P_INDATE, P_PRICE, V_CODE FROM PRODUCT WHERE V_CODE <> 21344; SELECT P_CODE,P_DESCRIPT,P_ONHAND,P_MIN,P_PRICE FROM PRODUCT WHERE P_CODE < ‘1558-QWI’; SELECT P_DESCRIPT,P_ONHAND,P_MIN,P_PRICE,PINDATE FROM PRODUCT WHERE P_INDATE >= ‘01/20/2002’;
  16. 16. SQL SELECT Component A query includes a list of columns to be included in the final result immediately following the SELECT keyword. An asterisk ("*") can also be used to specify that the query should return all columns of the queried tables. SELECT is the most complex statement in SQL, with optional keywords and clauses that include:   The FROM clause which indicates the table(s) from which data is to be retrieved. The FROM clause can include optional JOIN subclauses to specify the rules for joining tables.   The WHERE clause includes a comparison predicate, which restricts the rows returned by the query. The WHERE clause eliminates all rows from the result set for which the comparison predicate does not evaluate to True.   The GROUP BY clause is used to project rows having common values into a smaller set of rows. GROUP BY is often used in conjunction with SQL aggregation functions or to eliminate duplicate rows from a result set. The WHERE clause is applied before the GROUP BY clause.   The HAVING clause includes a predicate used to filter rows resulting from the GROUP BY clause. Because it acts on the results of the GROUP BY clause, aggregation functions can be used in the HAVING clause predicate.   The ORDER BY clause identifies which columns are used to sort the resulting data, and in which direction they should be sorted (options are ascending or descending). Without an ORDER BY clause, the order of rows returned by an SQL query is undefined. Source: http://en.wikipedia.org/wiki/SQL 16
  17. 17. Example SELECT Book.title, count(*) AS Authors FROM Book JOIN Book_author ON Book.isbn = Book_author.isbn GROUP BY Book.title; Title Authors ---------------------- ------- SQL Examples and Guide 4 The Joy of SQL 1 An Introduction to SQL 2 Pitfalls of SQL 1 Source: http://en.wikipedia.org/wiki/SQL
  18. 18. P R O F . S T E V E N L . J O H N S O N E M A I L : S T E V E N @ T E M P L E . E D U T w i t t e r : @ S t e v e n L J o h n s o n http://stevenljohnson.org http://community.mis.temple.edu/mis5001fall10johnson/ For More Information