Business Intelligence BUS 782

Topics Import/Export Data Chart Online Analytical Process, OLAP – Excel’s Pivot table/Pivot chart Scenario Management Data warehousing Data Mining Big Data

Access Tools for Import/Export External Data – Import – Export Exchange data between: – Other Access databases – Excel – Text file – XML – ODBC data sources

Data Exchange with Excel Import from Excel: – The first row of Excel’s list should contain field names Or define a name for the list range. – External Data/Import/Excel Export to Excel: – External Data/Export/Excel Note: Saved imports/exports

Open Database Connectivity (ODBC) Provide a standard to retrieve data from a database. It manages one or more "database drivers“ that enables the communication between database and applications. To access a database, we use ODBC facilities to define a ODBC data source name for the database.

ODBC Demo Define an ODBC data source name: – Control Panel/Administrative tools/Data source(ODBC) – Note: ODBC32 Import from an ODBC data source – External Data/Import/ODBC data source

Export/Import XML File Export: – External Data/More/XML – XML Data file – XML schema file Import: – External Data/Import XML

Export to Word Write data to a Word file: – External Data/More/Word Merge data with a Word document – Select the table or query – External Data/Word Merge – Example: Write a letter to students whose GPA is low and ask them to see their faculty advisor.

Excel’s Tools for Import/Export Data/Get External Data – From Access – From Web – From Other Sources ODBC XML – Existing Connections Saved Queries Save Query Refresh: Data/Refresh All – Compare the difference between Copy/Paste and Import

Excel’s Web Query Demo Data/From Web – HTML Table Save query Import

Charts

Charting Decision Rules An Internet Service Provider charges customers based on hours used: – First 10 hours$15 – Each of the next 20 hours$2 per hour – Hours over 30 hours$1 per hour

Comparing Decision Rules Plan 2: – First 20 hours: $20 – Hours over 20$1.5 Plan 3: – $35 unlimited access.

Charting Functions Demand function: – P = 150 – 6*Q ^2 Supply function: – P = 10* Q ^2 + 2*Q Note: – Positive area – Value axis maximum/minimum value: Format Value Axis

Chart Stock Market Data Download Dow Jones Historical Data – Yahoo/Finance/DJI/Historical Data – Enter critia – Copy/Paste to Excel To chart: – Insert/Chart/Other Charts/Stock chart

Display Chart on Web page Google Chart: –

On-Line Analytical Processing (OLAP) Tools The use of a set of graphical tools that provides users with multidimensional views of their data and allows them to analyze the data using simple windowing techniques OLAP Operations – Cube slicing–come up with 2-D view of data – Drill-down–going from summary to more detailed views – Roll-up – the opposite direction of drill-down – Reaggregation – rearrange the order of dimensions

Slicing a data cube

Example of drill-down Summary report Drill-down with color added Starting with summary data, users can obtain details for particular cells

Excel’s Pivot Table Insert/Pivot Table or Pivot Chart – Drill down, rollup and reaggregation – Filter Pivot Chart – Filter – Drilldown, rollup, reaggregation Import queries from Access to perform analysis. – Sales related to: Customer’s location, Rating and Products

Scenario A scenario is an assumption about input variables. Excel’s Scenarios is a what-if-analysis tool. A scenario is a set of values that Microsoft Excel saves and can substitute automatically in your worksheet. You can use scenarios to forecast the outcome of a worksheet model. You can create and save different groups of values on a worksheet and then switch to any of these new scenarios to view different results. Data/What If analysis/Scenario

Creating a Scenario – Add scenario Changing cells – Scenario Summary Resulting cells Demo: benefit.xls

Data Warehouse Data warehouse is a repository of an organization's electronically stored data. A data warehouse houses a standardized, consistent, clean and integrated form of data that: – sourced from various operational systems in use in the organization, – structured in a way to specifically address the reporting and analytic requirements.

Example: Transaction Database Customer Order Product Has 1 M M M CID Cname City OIDODate PID Pname Price Rating SalesPerson Qty

Analyze Sales Data Detailed Business Data Total sales: – by product: Qty*Price of each detail line Sum (Qty*Price) Detailed business data: qty*price Total quantity sold: – By product: Sum(Qty) Detailed business data: Qty

Dimensions for Data Analysis: Factors relevant to the business data Analyze sales by Product Analyze sales related to Customer: – Location: Sales by City – Customer type: Sales by Rating Analyze sales related to Time: – Quarterly, monthly, yearly Sales Analyze sales related to Employee: – Sales by SalesPerson

Data Warehouse Design - Star Schema - Dimension tables – contain descriptions about the subjects of the business such as customers, employees, locations, products, time periods, etc. Fact table – contain detailed business data with links to dimension tables.

Star Schema FactTable LocationCode PeriodCode Rating PID Qty Amount Location Dimension LocationCode State City CustomerRating Dimension Rating Description Product Dimension PID Pname Category Period Dimension PeriodCode Year Quarter Can group by State, City

Define Location Dimension Location: – In the transaction database: City – In the data warehouse we define Location to be State, City San Francisco -> California, San Francisco Los Angeles -> California, Los Angeles – Define Location Code: California, San Francisco -> L1 California, Los Angeles -> L2

Define Period Dimension Period: – In the transaction database: Odate – In the data warehouse we define Period to be: Year, Quarter Odate: 11/2/2003 -> 2003, 4 Odate: 2/28/2003 -> 2003, 1 – Define Period Code: 2003, 4 -> , 1 -> 20031

The ETL Process E T L One, company- wide warehouse Periodic extraction  data is not completely current in warehouse

The ETL Process Capture/Extract Transform – Scrub(data cleansing),derive – Example: City -> LocationCode, State, City OrderDate -> PeriodCode, Year, Quarter Load and Index ETL = Extract, transform, and load

Performing Analysis Analyze sales: – by Location – By Location and Customer Type – By Location and Period – By Period and Product Pivot Table: – Drill down, roll up, reaggregation

Data Mining Knowledge discovery using a blend of statistical, Artificial Intelligence, and computer graphics techniques Goals: – Explain observed events or conditions – Explore data for new or unexpected relationships

Typical Data Mining Techniques Statistical regression Decision tree induction Clustering – discover subgroups Affinity – discover things with strong mutual relationships Sequence association – discover cycles of evens and behaviors Rule discovery – search for patterns and correlations Text mining (analytics)

Typical Data Mining Applications Profiling populations – High-value customers, credit risks, credit card fraud Analysis of business trends Target marketing Campaign effectiveness Product affinity – Identifying products that are purchased concurrently Up-selling – Identifying new products and services to sell to a customer based on critical events

Affinity Analysis: Market Basket Analysis Market Basket Analysis is a modeling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items. The set of items a customer buys is referred to as an itemset, and market basket analysis seeks to find relationships between purchases. Typically the relationship will be in the form of a rule: Example: – IF {beer, no bar meal} THEN {chips}.

Basket Analysis and Cross- Selling For instance, customers are very likely to purchase shampoo and conditioner together, so a retailer would not put both items on promotion at the same time. The promotion of one would likely drive sales of the other. A widely used example of cross selling on the internet with market basket analysis is Amazon.com's use of suggestions of the type: – "Customers who bought book A also bought book B", e.g.

Big Data Data Size: – Gigabyte – Terabyte: Terabyte USB – Petabyte: Wal-Mart handles more than 1m customer transactions every hour at more than 2.5 petabytes – Exabyte: the amount of traffic flowing over the internet about 700 exabytes annually

Big Data Challenges Big Data are high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization. “3Vs": – Volume: Size – Velocity: Processing speed – Variety: Structured: able to fit in a database table unstructured data

Applications Text mining: deriving high-quality information from text. – text categorization, text clustering, concept/entity extraction, sentiment analysis, etc. Web mining: – Web usage mining – Web content mining Social media mining – Salesforce Radian6 Social Marketing Cloud

NoSQL Database NotOnlySQL is a broad class of database management systems identified by non- adherence to the widely used relational database management system model. They are useful when working with a huge quantity of data when the data's nature does not require a relational model.

In-Memory Database An in-memory database is a database management system that primarily relies on main memory for computer data storage. It is contrasted with database management systems that employ a disk storage mechanism. Main memory databases are faster than disk- optimized databases. Good for Big Data analytics. Use non-volatile memory module that retains data even when electrical power is removed.