1. Sharing Enterprise Data Data administration Data administration Data downloading Data downloading Data warehousing Data warehousing

2. Data administration Organization-wide activity (the DBA of a particular database is only a part of this) Organization-wide activity (the DBA of a particular database is only a part of this) Challenges: Challenges: Many types of data exist Many types of data exist Basic categories of data are not obvious Basic categories of data are not obvious The same data can have many names, descriptions, and formats The same data can have many names, descriptions, and formats Data are changed – often concurrently Data are changed – often concurrently Political and organizational issues complicate operational issues Political and organizational issues complicate operational issues

3. Marketing Communicate existence of data administration to organization Communicate existence of data administration to organization Explain reason for existence of standards, policies, and guidelines Explain reason for existence of standards, policies, and guidelines Describe in a positive light the services provided Describe in a positive light the services provided

4. Data standards and policies Establish standard means for describing data items; standards include name, definition, description, processing restrictions, etc. Establish standard means for describing data items; standards include name, definition, description, processing restrictions, etc. Establish data proponents Establish data proponents Establish organization-wide data policy; examples are security, data proponency, and distribution Establish organization-wide data policy; examples are security, data proponency, and distribution

5. Forum for data conflict resolution Establish procedures for reporting conflicts Establish procedures for reporting conflicts Provide means for hearing all perspectives and views Provide means for hearing all perspectives and views Have authority to make decision to resolve conflict Have authority to make decision to resolve conflict

6. Return on organization's data investment Focus attention on value of data investment Focus attention on value of data investment Investigate new methodologies and technologies Investigate new methodologies and technologies Take proactive attitude toward information management Take proactive attitude toward information management

7. Downloading data for local processing

8. Data downloading via file-sharing systems

9. Data downloading via client-server systems

10. Downloading: potential problems Coordination Coordination Conform downloaded data to database constraints Conform downloaded data to database constraints Coordinate local updates with downloads Coordinate local updates with downloads Consistency Consistency Downloaded data should not be updated Downloaded data should not be updated Applications need features to prevent updating Applications need features to prevent updating Warn users of possible problems Warn users of possible problems Access control Access control Data may be replicated on many computers Data may be replicated on many computers More difficult data access control procedures More difficult data access control procedures Risk of computer crime Risk of computer crime Disks and modem access are easy to conceal Disks and modem access are easy to conceal Illegal copying is difficult to prevent Illegal copying is difficult to prevent

11. Data warehousing What if every department wants to download the organization’s data? What if every department wants to download the organization’s data? The data management problem becomes immense The data management problem becomes immense Data warehouse: a centralized repository to facilitate management decision making and increase the value of the enterprise data assets Data warehouse: a centralized repository to facilitate management decision making and increase the value of the enterprise data assets

12. Data warehouse architecture

13. Integrated From Various Sources Operational Data Data Warehouse Operational Data Data Warehouse appln A - m,f appln. B - male, female m, f appln. C - x,y appln.. D - 1,0

14. Data in Data Warehouse Older Detail Data Current Detail Lightly Summarized Highly Summarized Sales Detail 1992-98 Sales Detail 1998-99 Regional Sales by Week 83-98 National Sales by Month 85-98

15. Time Variant Operational Data Operational Data time horizon 60-90 days time horizon 60-90 days key may / may not have element of time key may / may not have element of time can be updated can be updated Data Warehouse Data Warehouse time horizon 5-10 years time horizon 5-10 years key contains element of time key contains element of time once snapshot is made data cannot be updated once snapshot is made data cannot be updated

16. Non - volatile Operational Data Operational Data Data is updated on a record by record basis Data is updated on a record by record basis To support the record- by-record on line update, requires the technology to have very complex foundation To support the record- by-record on line update, requires the technology to have very complex foundation Data Warehouse Data Warehouse Data is not updated Data is not updated The physical design levels liberties can be taken to optimize the access of data The physical design levels liberties can be taken to optimize the access of data Replace Insert Change Replace Load Access

17. Data warehouse components Data extraction tools Data extraction tools Extracted data Extracted data Metadata of warehouse contents Metadata of warehouse contents Warehouse DBMS(s) Warehouse DBMS(s) Warehouse data management tools Warehouse data management tools Data delivery programs Data delivery programs End- user analysis tools End- user analysis tools User training courses and materials User training courses and materials Warehouse consultants Warehouse consultants

18. Data warehouse requirements Queries and reports with variable structure Queries and reports with variable structure OLAP: On-Line Analytical Processing OLAP: On-Line Analytical Processing User- specified data aggregation User- specified data aggregation User- specified drill down User- specified drill down Graphical outputs Graphical outputs Integration with domain- specific programs Integration with domain- specific programs

19. OLAP OLAP -- to gain insight into data through fast, consistent, interactive access to wide variety of views -- to gain insight into data through fast, consistent, interactive access to wide variety of views --functionality characterized by dynamic multidimensional --functionality characterized by dynamic multidimensional analysis of consolidated enterprise data analysis of consolidated enterprise data Data Extraction Data Extraction --ability to capture, convert, & deliver data to various sources --ability to capture, convert, & deliver data to various sources --provides fast disk-to-disk transfer capabilities and automate data compression --provides fast disk-to-disk transfer capabilities and automate data compression

20. Data Mining Tools Data Mining Tools -- helps by focusing end user attention on a smaller subset of data -- subset is determined by data mining “discovery”process, which is done in advance of in- depth analysis Executive Information System Executive Information System -- for senior executives with little computing experience -- for senior executives with little computing experience -- available on demand with whatever level of detail ( drill-down) -- add value, improve strategic & financial control, market & economical information, better competitive analysis

21. Financial & Marketing Analysis Financial & Marketing Analysis -- provides end user with highly value added report like -- provides end user with highly value added report like accounts receivable / payable, ledger mgmt., cost control accounts receivable / payable, ledger mgmt., cost control cost budgeting & planning, cost budgeting & planning, -- in marketing - product pricing, demand analysis, -- in marketing - product pricing, demand analysis, estimation estimation -- use non-technical language, run queries in fast, reliable manner.. -- use non-technical language, run queries in fast, reliable manner.. Report & Query Tools Report & Query Tools -- most important & widely used -- most important & widely used -- emphasize generating value added reports -- emphasize generating value added reports -- user have flexibility to use either common English/ SQL -- user have flexibility to use either common English/ SQL -- support graphical interface -- support graphical interface

22. FINGERHUT FINGERHUT 150 catalog mailings in 1997 150 catalog mailings in 1997 based on statistically predicted consumer response based on statistically predicted consumer response 30 million customers, 14% annual growth 30 million customers, 14% annual growth database captures 1400 pieces of information about a household database captures 1400 pieces of information about a household demographics, purchasing histories demographics, purchasing histories Example

23. Data warehouse challenges Inconsistent data Inconsistent data E.g., different timing, different domains... E.g., different timing, different domains... Tool integration Tool integration E.g., spreadsheets versus databases… E.g., spreadsheets versus databases… Lack of warehouse data management tools Lack of warehouse data management tools In-house software development (expensive) In-house software development (expensive) Ad-hoc requirements Ad-hoc requirements

24. Data warehousing Is it as good an idea as it seemed? Is it as good an idea as it seemed? What about the Internet? What about the Internet? Data mart: limit the scope of the warehouse Data mart: limit the scope of the warehouse