IMS9300 IS/IM Fundamentals

1. IMS9300IS/IM Fundamentals Lecture 8 Strategic Information Management

2. Last Week • What IM is • Words as keys to full-text retrieval • Metadata [esp. DC] • Key Issues • Volume • Storage • Retrieval • Preservation/Long-term access

3. Outline • What informs enterprise strategy • Business Intelligence/ Datawarehouse • [Web] Content Management Systems • Monash CMS as a case

4. References Hummingbird White Paper on BIhttp://mimage.hummingbird.com/alt_content/binary/pdf/collateral/wp/bi.pdf[For a broader context look at Hummingbird’s overview aper - http://mimage.hummingbird.com/alt_content/binary/pdf/collateral/wp/360_view_wp.pdf ] University of Sydney’s brief page on Metadata [http://staff.library.usyd.edu.au/webman/metaguide.html ] and lengthy document on their Content Management System [ http://www.ssdu.usyd.edu.au/courses/cms/resources/CMS_Handbook.doc]

5. Enterprise strategy • The strategy an enterprise will adopt is aimed at • Survival • Growth • Specific markets • Long-term outcomes

6. Information Sources for Strategic Decision-making • Transaction processing system • External information • Internal documents • Explicit and tacit knowledge

7. Transaction Processing Systems as Information Sources • Day to day data • Seldom contains historical information • Optimised for throughput • Typically supported on Relational Data Base • Information is summarised into reports

8. External Sources as input to Strategy • Information about rivals [How does the enterprise get this information?] • Information about demographics, market trends, business confidence, etc. [What are some sources of such information] • What form is this information likely to take?

9. Textual Information in the Enterprise • Reports • Documents • Minutes • Files • Records

10. Business Intelligence and OLAP • BI and On Line Analytical Processing Systems • Front Ends for a data warehouse • Data is typically extracted from the warehouse [and from the OLTP system] to provide fast multi-facetted access to subsets of complex information

11. Operational Data • Often the material of the transaction processing activities • Typically geared to tactical level activities • Subsequently, may not be bound well to enterprise strategy • Geared for specific tasks • Often from different vendor platforms

12. Data Cubes • Multidimensional databases [MDDB] • Often represented as 3 dimensional models • Can have more dimensions, but that is difficult to represent • Dimensions can have aggregations in them • Optimised for analysis

14. The data warehouse • A central store of • subject oriented data • originating from the enterprise’s transactions systems and • external data sources, so that it becomes • a critical information system. • fosters a culture of knowledge sharing.

15. Events driving data warehousing • Recognition of the need for more sophisticated information for management decision-making • More powerful systems • “Middleware” products to link disparate databases • “User friendly” interfaces

16. Purposes of Data Warehouses • Data warehouses, utilized correctly, can provide insights into • customer behaviour [CRM], • product or campaign performance, • profitability, • cost structures, etc. • the data must relate specifically to the area of knowledge to be attained [i.e. not everything is put into the warehouse]

17. Other driving factors • Legacy data systems [perhaps from mergers] • Heterogeneous data • Need to access historic data • Hardware capability • Intranets and other increased network capacity

19. Elements of Data Warehouse operation • Getting data in - operational data and its transformation [ETL operations] • The heart of the warehouse - enterprise warehouse and Data Marts • Getting information out - Knowledge discovery and data mining, information tools

21. Data Extraction • Data coming from a variety of [often operational system] sources usually needs to be massaged to ensure internal consistency in the data warehouse • Techniques of pattern matching and [semi]intelligent analysis are used to modify data errors. • Data are then loaded to the warehouse

22. Data Transformation • Data may need to be changed from one format to another [e.g. Fo to Co ] • Data structures need to be made alike • Data are normalized • Data may need to be aggregated • Fields can be split or merged

23. Data Load • Data load is the relatively easy step • Needs to be managed, especially for sequencing • As data changes in the source systems become more complex, sometimes a complete re-load, rather than an update will be undertaken • Often run to tight schedules, with little scope for data load failure

24. Data Marts • Sometimes seen as departmental or even workgroup level operation of the warehouse • geared to anticipated queries and operations [reports, projections, specific analyses] • dynamic • feed from the warehouse data [and rarely but sometimes back to the data warehouse]

25. Types of Data Mart • Dependent - all data in the data mart is derived from the [reconciled data in the] data warehouse • Independent - some of the data in the Mart comes from the data warehouse, with other data coming from operational systems. • Virtual – if the data structures of the datamarts are consistent, then they can exchange data directly, not using the datawarehouse as a central repository.

26. CRM • Customer Relationship Management • Customer centred databases • Often associated with marketing • Aimed at customer retention through • Analysis of customer purchasing behaviour • Analysis of long term relations with customers • Web analytics – what customers do when they are at the web site.

27. Workflow as Information Management Source • Many enterprises try to manage documents through a process of workflow, especially electronic documents/records/forms etc. • This structured dealing with documents • ensures that procedures are observed, • all relevant data is collected, • work processes can be monitored, and • can provide a basis for a CMS

28. Content Management Systems • Content Management System (CMS) aims to separate management of content from design. • Page designs are stored in templates while the content may be stored in a database or separate files. • When a user requests a web page, the parts are combined to produce a standard HTML page. The resultant web page may include content from multiple sources. • A CMS usually has the following components: • Document templates • A scripting language and/or a markup language • Integration with a database

29. Advantages of a CMS • Benefits for both website administrators and authors. • Use templates and common design elements • to ensure consistent design throughout the site. • Authors can concentrate on content over design. • To change the appearance of the website, the administrators need only modify the templates and not each individual page. • Supports the delegation of responsibility for the provision and maintenance of content on the website. • Provides consistent support/framework for e-commerce

30. Monash CMS • http://www.its.monash.edu.au/projects/webcms/index.html • TeamSite, an enterprise level web content management system (CMS), has been installed and is in the process of being rolled out at Monash. • Interest in uptake has greatly exceeded expectations. The CMS is very versatile and has many more potential uses than originally anticipated. • The system as initially installed lacks the capacity to exploit these extended benefits. Hardware upgrades will be required to maximise the potential of the CMS to support Monash’s strategic web initiatives and to prevent unacceptable system response times when the CMS is fully rolled out.

31. Issues • Support • Hardware • Personnel • Design • Workflow • Metadata • Uses • University administration or Faculties? • Supporting Education delivery or admin? • Users • Web administrators or content creators?

32. Summary • What informs enterprise strategy • Business Intelligence/ Datawarehouse • [Web] Content Management Systems • Monash CMS as a case