1. MPIII Database Technologies Relational Concepts Data Warehouses & Marts Queries, OLAP, Data Mining

2. Terms/Examples Database –a collection of related data. Usually organized according to topics: e.g. customer info, products, transactions Database Management System (DBMS) –a program for creating & managing databases; ex. Oracle, MS-Access, Sybase DBMS - the program. Manages interaction with databases. database - the collection of data. Created and defined to meet the needs of the organization. Client - makes requests of the DBMS server request response Server - responds to client requests

3. A Simple Database File/Table –Customers Field/Column –5 shown: CUSTID, FIRST, LAST, CITY, STATE Record/Row –5 shown: one for each customer

4. A More Complex Example Entry & Maintenance is complicated –redundant data exists, increases chance of error, complicates updates/changes, takes up space

5. Normalize Data - Remove Redundancy One Many Customer Table Transaction Table

6. Key Terms Relational DBMS –manages databases as a collection of files/tables in which all data relationships are represented by common values in related tables (referred to as keys). –a relational system has the flexibility to take multiple files and generate a new file from the records that meet the matching criteria (join). SQL - Structured Query Language –Most popular relational database standard. Includes a language for creating & manipulating data.

7. Now With More Data One Many One Many

8. Meta-Data Data that describes the characteristics of stored data Enterprise Data Model –consistent, cross-functional, shareable meta-data model –standardization increases flexibility & use (data to info) –facilitates the creation of data warehouses 1 1 m m Customer Table Transaction Table Broker Table

9. Management Levels of IS DSS MIS TPS Strategic Planning Management Control Operational Control

10. Warehouses & Marts Data Warehouse –a database designed to support decision-making in an organization. It is batch-updated and structured for fast online queries and exploration. Data warehouses may aggregate enormous amounts of data from many different operational systems. Data Mart –a database focused on addressing the concerns of a specific problem or business unit (e.g. Marketing, Engineering). Size doesn’t define data marts, but they tend to be smaller than data warehouses.

11. Data Warehouses & Data Marts TPS & other operational systems Data Warehouse Data Mart (Marketing) Data Mart (Engineering) 3rd party data = query, OLAP, mining, etc. = operational clients

12. Differing System Demands network traffic & processor demands time network traffic & processor demands time Managerial Systems Operational Systems

13. Transform Data from TPS to Warehouse Consolidate data –e.g. from multiple TPS around the country/world “Scrub” the data –keep definitions consistent (e.g. translate part numbers/product names if they differ per country) Calculate fields (decrease processor load) Summarize fields (decrease processor load) De-normalize data (ease of use)

14. Calculated Fields Customer Service Application: Customer support person TPS - focuses on customer info Total is calculated on the fly Database Query Application: Marketing manager Aggregate reporting of business intelligence Total calculated in advance

15. Query Tools & OLAP Query Tools –user-lead discovery. Can return individual records or summaries. Requests are formulated in advance (e.g. “show me all delinquent accounts in the northeast region during Q1”). OLAP - Online Analytical Processing –user-lead discovery. Data is explored via “drill down” into the data by selecting variables to summarize on. Results are usually reported in a cross-tab report or graph (e.g. “show me a tabular breakdown of sales by business unit, product type, and year”).

16. OLAP Online Analytical Processing. (example of cross-tab results presented below) 1. business unit 2. product type 3. year

17. Data Mining automated information discovery process, uncovers important patterns in existing data –can use neural networks or other approaches. Requires ‘clean’, reliable, consistent data. Historical data must reflect the current environment. e.g. “What are the characteristics that identify when we are likely to lose a customer?”

18. Data Mining Uses Market Segmentation - e.g. Dayton Hudson Direct Marketing - e.g. Chase Market basket analysis - e.g. Wal-Mart Customer Churn - e.g. Fleet Bank Fraud Detection - e.g. Bank of America Cost Reduction Prospecting - e.g. Merk Medco.

19. Stupid Data-Miner Tricks Ad-Hoc Theories –when an oddity jumps out of the data, it’s tempting to develop a theory for it. Sometimes findings are just statistical flukes. Using Too Many Variables –the more factors considered, the more likely a relationship will be found - valid or not. Not Taking No for an Answer –it’s OK to stop looking if you can’t find anything. There are no silver bullets.

20. MPIII Internal & External Integration Enterprise Resource Planning (ERP)

21. Challenges Facing IS Depts. Y2K & Legacy Systems Globalization (euro, currency issues) Rapid Technology Advancement –e.g. Client/Server & Internet IS Staffing & Retention Changing Organizational Structures –e.g. Owens Corning Tighter Integration with Buyers & Suppliers

22. Legacy Systems Many firms have limited to no integration across geographic areas functional areas (v-chain) products, plants, & business units BuyersSuppliers

23. External Integration EDI - Electronic Data Interchange –uses standard formats to pass data between disparate systems –US format - X.12, European format - UN/EDIFACT Cost Savings –paper order = $50 - $70 –EDI order = $2.50 (VANs / private networks) –I-EDI order = less than $1 (Internet) XML - eXtensible Markup Language –tagging language for the web

24. What is ERP? ERP - Enterprise Resource Planning Software –sometimes called Enterprise Applications, Enterprise Packages, Enterprise Suites, or Enterprise Systems –connects all of the information which flows through a company to a single integrated set of systems –implemented in modules which can be integrated (all at once or at a later date) e.g. Financials, Logistics, HR –may work with a wide variety of databases, hardware, and operating systems Leading Vendors –SAP, Oracle, JD Edwards, Baan, Peoplesoft

25. ERP in Action Sales Inventory Production Staffing Purchasing Order Tracking Planning Source: BusinessWeek Int’l, 1997

26. The Benefits Internal & external integration –squeeze out waste & enable strategies Standard software enables - –inter-organizational systems (easier if buyers & suppliers use the same system, e.g. petrochem. ind.) –broad selection of add-on packages (e.g. data warehouses, etc.) Package upgrading and new technology development is handled by vendor Speed of deployment

27. The Risks Staff retention (e.g. Grace case) Tied to a single vendor Flexibility limited by options offered by the vendor –may inappropriately force generic processes –may inappropriate force structure Complexity - particularly regarding mapping and standardizing processes across the organization.

28. Make vs. Buy Adapted from Applegate et al., p. 61.

29. Successful Deployment of ERP Business Case –benchmark, cost justify (e.g. unplug mainframes) Leadership –from the highest levels (e.g. success at Owens Corning, failure at Westinghouse) Staffing –largely from business, not IT (users know the process) –‘compensation handcuffs’ (e.g. end of deployment bonuses, training payback agreements) –experienced consultants - check refs., clients Execute with proven methodologies