Managing Information Resources

1. Managing Information Resources Chapter 7 Information Systems Management In Practice 6E McNurlin & Sprague PowerPoints prepared by Michael Matthew Visiting Lecturer, GACC, Macquarie University – Sydney Australia

2. Chapter 7 • This chapter / lecture explores the management of data, information, and knowledge • It begins by identifying some problems in managing data, and then surveys the evolution of database management systems, including the next-generation systems • It explores the various types of information that companies need to manage as they treat information as an organizational resource • It concludes by discussing one of the most important issues facing companies today: how to manage knowledge • Case examples include Monsanto, Owens & Minor, HICSS Personal Proceedings, Tapiola Insurance Group, Tennessee Valley Authority, Eastman Chemical Company and Groove Networks

3. Today’s Lecture • Introduction • Managing Data • The Three-Level Database Model • Four Data Models • Getting Corporate Data into Shape • Managing Information • Four Types of Information • Data Warehouses • Document Management • Content Management

4. Introduction • “Managing information resources” initially meant managing data, first in files, then in corporate databases which were: • Well structured • Carefully defined, and • Controlled by IS department • Next = expanded to include “information” (data with meaning) • Also = much talk of managing knowledge • With the emergence of the Internet, talk has now turned to managing content: • Text, graphics, sound, video and animation • Covered in this chapter and chapter 13

5. Introduction cont. • Data vs. Information vs. Knowledge • Data: facts devoid of meaning or intent • Information: data in context • Knowledge: information with direction or intent • As the breadth of the kinds of information resources has expanded, so has the job of managing them. The job may not start in the IS department but it invariably ends up there • PCs users used ‘alone’ • Needed to share files • Version control, back-up etc. • Web sites / content • Initially created their own • Need for recovery, version control • Corporate consistency • IS to the ‘rescue’ • Management procedures • Discipline

6. Introduction cont. • Corporate databases are still a (the?) major IS department responsibility • Sometimes housed in a variety of database models • Production databases – transaction • Data warehouses • CRM • Information in the form of documents (electronic or paper) and Web content has exploded the size of databases organizations now manage • Knowledge management is becoming a key to exploiting “intellectual assets” • Information resources need to be well managed as information becomes an important strategic resource

7. Managing Data • Database management systems are the main tool for managing computerized corporate data • They have been around since the 1960s and are based on two major principles: • A three- level conceptual model and • Several alternative ‘data models’ for organizing the data

8. Managing Data:The Three-Level Database Model See Figure 7-1 • Level 1 - The external, conceptual, or local level, containing the various “user views” of the corporate data that each application program uses • Not concerned with how the data will be physically stored or what data is used by other applications • Level 2 - The logical or “enterprise data” level • ‘Technical’ (human) view of the database = under control of the DBAs • Level 3 - The physical or storage level, specifying the way the data is physically stored • End user not concerned with all these ‘pointers and flags’ (how the data is physically organized) = they are for use by the DBMS

10. The Three-Level Database Model: Advantages • Level 2 absorbs changes made at Level 3 such as using a new physical storage device • Individual application programs in Level 1 do not need to be changed when the physical layer changes • Data only needs to be stored once in Level 2, and different programs can draw on it and vary the relationships among the data

11. Managing Data:Four Data Models The second major concept in database management is alternate ways to define relationships among data • Hierarchical model: structures data so that each element is subordinate to another in a strict hierarchical manner • Parent, child etc. • Network model: allows each data item to have more than one parent, • Relationships stated by pointers stored with the data

12. Managing Data:Four Data Modelscont. • Relational model: where the data is stored in tables. • Eight relational operations can be performed on this data • Select, Project, Join, Product, Intersection, Difference, Union, Division • Microsoft Access • Relational systems are not as efficient as hierarchical or network database systems, but because relational systems allow people to create relationships among data on the fly, they are much more flexible • First used to handle end user queries – they are now widely used in high-volume transaction systems with huge files • Hence, they have become the database technology of choice in today’s systems • Also = largely due to decrease in costs of technology: processing and disk storage

13. Managing Data:Four Data Models cont. • Object model: can be used to store any type of data, whether a: • Traditional name or address, • An entire spreadsheet, • A video clip, • A voice annotation, • A photograph, or • A segment of music • The tenets of objects have become increasingly important in the world of computing • E.g. Web Services because the XML modules utilize object principles • Typical, yet complex database applications that may require objects: • CAD for a large office building • Large retail chains record every product code scanned • Insurance policy files e.g. claim forms, images, video etc.

14. Managing Data:Four Data Models cont. • Object models retain traditional DBMS features including: • End user tools • High level Query languages • Concurrency control • Recovery • Ability to handle huge amounts of data • Include two other major concepts • Object management • Management of complex kinds of data such as multimedia and procedures • Knowledge management • Management of large numbers of complex rules for reasoning and maintaining integrity constraints between data

15. Managing Data:Four Data Models cont. • Finally, security is of major importance in today’s DBMSs • Problem = compounded by distributed, heterogeneous Internet-linked databases • Companies may want to permit access to some portions of their databases whilst restricting other portions • This selective accessibility requires reliably authenticating ‘users’ • Unless security and integrity are strictly enforced, users will not be able to (fully) trust the systems

16. Managing Data Getting Corporate Data into Shape

17. Getting Corporate Data into Shape • In the midst of this growing richness of data and information, companies are still struggling to get their internal alphanumeric data under control • The installation of company-wide software packages such as SAP, enterprise data warehouses, and intranets has once again brought to the fore the problems of “dirty data” • Data from different databases that has: • Different names • Uses different time frames, or • That otherwise does not match • Attempts to get under control go back a long way: • Late ’60s / early ’70s = DBMS • Then = the still evolving and important role of “data administration: • Managing all the computerized data resources of a company

18. Getting Corporate Data into Shape: The Problem: Inconsistent Data Definitions • Problem: data definitions incompatible from: • Application to application • Department to department • Site to site, and • Division to division • Reason: to get application systems up and running quickly, system designers sought data from the cheapest source or politically expedient source • Result: different files with: • Different names for same data, and • Same name for different data etc.

19. Getting Corporate Data into Shape: The Problem: Inconsistent Data Definitions cont. • Account Number • AcctNum • AcctNumb • Acct# • A/CNum • Note: people (in the majority of cases) weren’t stupid • They never dreamt their files / databases etc. would be used in this manner • Historical ‘stand alone’ computing • Information collation, use, communication etc. = never thought possible

20. Getting Corporate Data into Shape: The Role of Data Administration • The use of DBMS - database management software, reduced, to some extent, the problems of inconsistent and redundant data in organizations • However merely installing & running a DBMS is not sufficient to manage data as a corporate resource • Database administration: concentrates on administering databases and the software that manages them

21. Getting Corporate Data into Shape: The Role of Data Administration cont. • Data administration is broader: • To determine what data is being used outside the organizational unit that creates it • Whenever data crosses organizational boundaries, its definition and format need to be standardized • Data dictionaries are the main tools by which data administrators control standard data definitions

22. Getting Corporate Data into Shape:ERP (Enterprise Resource Planning) • To bring order to the data mess, data administration has four main functions: • Clean up the data definitions • Control shared data • Manage data distribution, and • Maintain data quality • Interestingly, many companies really did not take these four jobs seriously until the mid 1990s, when they needed consistent data to install a company-wide ERP package • ERP provided the means to consolidate data to give management a corporate-wide view of operations • E.g. MeadWestVaco (Chapter 1)

23. MonsantoCase Example: Managing Corporate Data / ERP • Monsanto case study to illustrate one company’s success in getting its corporate data in shape • Monsanto is a provider of agricultural products, pharmaceuticals, food ingredients, and chemicals. 50% revenues outside USA, it is decentralized • Monsanto established three large enterprise wide IT projects: • To redevelop operational and financial transaction systems using SAP • To develop a knowledge-management architecture, including data warehousing, and • To link transaction and decision support systems via common master data, known as enterprise reference data (ERD)

25. MonsantoCase Example: Managing Corporate Data / ERPcont. • Monsanto is too large and complex to operate SAP as a single installation • They have created a distributed SAP architecture • With separate instances of SAP for reference data, finance, and operations in each business unit • The master reference data integrates these distributed components • To convert SAP data to knowledge, Monsanto uses data warehouses • The sole source of master data is the ERD, but the data can be distributed wherever they are needed • To get corporate data in shape, Monsanto created a department called ERD Stewardship to set data standards and enforce quality—hence its nickname, “the data police.” • Independent of MIS

26. MonsantoCase Example: Managing Corporate Data / ERPcont. • Another newly created function is entity specialists = managers with the greatest stake in the quality of data • Also, data managers who now adhere to the new ERD rules • This has led to a cultural change: The idea of “tweaking” a system to fix a local discrepancy, formerly common, can now cause a major disruption in operations or a bad decision based on faulty data • Getting the data in shape was a huge undertaking, but it has made the company more flexible • Monsanto is already reaping bottom-line benefits from better integration and greater flexibility

27. Managing Information • Once enterprises get their data into shape, that data can more easily be turned into information “Information is power.” “We are in the Information Age.” • These and similar statements would lead you to believe that managing information is a key corporate activity • Technology = infrastructure; • Asset = information that runs on that infrastructure • It also raises a number of management issues

28. Managing InformationFour Types of Information • In Figure 7-3 we looked at a matrix representing the full scope of data information resources: • Internal record-based information, such as those found in databases • Which we discussed in detail but there are others: • Internal document-based information, such as reports, opinions, e-mails and proposals. Pertains to concepts: ideas, thoughts, etc. • External/record-based information,such as acquisition from external databases. • External/document-based: WWW

31. Managing InformationFour Types of Information cont. • Internal record-based information was the original focus of IS departments because it is the type of information that computer applications generate and manage easily • External record-based = accessible via Internet or public databases • Including subscription • Until recently = little attention to internal and external document-based information because it was so difficult to manipulate in computers • Intranets changed this • Documents = integral part of information on these sites • Responsibility = now on IS, even if just for technical issues • Four areas were responsibility of different areas but now IS is likely to be involved in some way

33. Managing InformationData Warehouses • Data warehouse: Houses data used to make decisions • This data is obtained periodically from transaction databases • The warehouse provides a snapshot of a situation at a specific time • Data warehouses differ from operational databases in that they do not house data used to process daily transactions • Operational databases have the latest data • Data warehouses = not so ‘time critical’ • Like ERP systems, they, too, spurred getting record-based data into shape • The most common data warehoused are customer data, used to discover how to more effectively market to current customers as well as non-customers with the same characteristics

34. Managing InformationData Warehousescont. • The simplest (MIS) tools generate perforated reports or permit ad hoc queries • Warehouses are reaching beyond reporting internal data • They are being combined with purchased data, such as demographic data, late breaking news and even weather reports, to uncover trends or correlations that competitors might not spot • To give a company a competitive edge

35. Managing InformationData Warehousescont. Key Concepts: • Metadata: The part of the warehouse that defines the data. Metadata means “data about data.” • Metadata explains the meaning of each data element, how each element relates to each other, etc. • It sets the standard – without it data from different legacy systems cannot be reconciled, so the data will not be “clean” • Quality data: Is the cleaning process to adhere to metadata standards • The older the data the more suspect its quality • Data marts: Is a subset of data pulled off the warehouse for a specific group of users • In the early 1990s, one huge warehouse was envisaged, but proved un-practical due to long search times and large cost factors

36. Managing InformationData Warehousescont. 5 Steps in a Data Warehousing Project: • Define the business uses of the data • Create the data model for the warehouse • i.e. defining the relationships between the data elements • Cleanse the data • Select the user tools • Consider the users point of view by selecting the tools they will use & then training them on tool use • Monitorusage and system performance • Data warehouses are seen as strategic assets that can yield new insights into customer behavior, internal operations, product mixes and the like • But to gain the benefits, companies must take the step of reconciling data from numerous legacy systems • = Make sure the data is ‘right’

37. OWENS & MINORCase Example: Data Warehousing • Illustrates numerous ways O&M are using its data for competitive advantage • Includes us of • ERP • Data warehousing • Web • Not only for internal use but as the basis for new revenue-generating services to customers and suppliers • Shows how innovative companies can use advanced information management technologies

38. OWENS & MINORCase Example: Data Warehousing cont. • This distributor of name-brand medical and surgical supplies uses ERP, data warehousing, and the Web • Not only for internal use of data • But as the basis for new revenue-generating services to customers and suppliers • It is using its data for competitive advantage • It augmented its ERP system to automate order forecasting, which: • Improved inventory turns • Lowered ordering rates from five-times-a-week to once-a-week, and • Improved customer service • It implemented an Internet-based inventory management system so that customers could order over the Internet, even using handheld devices • It even offered access to its data warehouse and decision support software to customers and suppliers who use the data to run their businesses

39. OWENS & MINORCase Example: Data Warehousing cont. • Delivering this information over the Web has: • Strengthened its relationships with trading partners • Given it a market-leading feature to entice new customers, and • Turned the data warehouse into a new source of revenue • When the system was rolled out, it was the first “e-business intelligence application” in the medical and surgical supply distribution industry • As a result, O&M has become an important “infomediary” in its industry

40. Managing InformationDocument Management • Even in today’s Internet-rich world, paper still plays a major role in most enterprises • There is also a need to move seamlessly between digital and printed versions of documents; hence, the importance of document management • The field of electronic document management (EDM) uses new technologies to manage information resources that do not fit easily into traditional databases • EDM addresses organizing and managing conceptual, descriptive, and ambiguous multimedia content. • Applying technology to process traditional documents makes a major change in what documents can accomplish in organizations

41. Managing InformationDocument Management cont. • It is hard to think of anything more pervasive and fundamental to an organization than documents • The impact of applying emerging technologies to document management is potentially significant • EDM contributes to business process redesign • Numerous EDM applications generate value. The ‘Big 3’ are: • To improve the publishing process • To support organizational processes • To support communications among people and groups • The concept of just-in-time (printing, publishing and forms processing) pervades the design philosophy in all three areas

42. Document Management:Improving the Publishing Process • Technology enables a major restructuring of the process of publishing and distributing paper documents • Traditional Process – designed primarily for high volume and high quality documents – shown in Figure 7-6 • Process has inefficiencies: • Infrequent long print run requires storing documents which become obsolete between runs • 60% of the total cost of delivering theses documents is in storage & transportation

44. Document Management:Improving the Publishing Process cont. • Figure 7-7 shows the steps in the revised publishing/distribution process using newer technologies • Documents are stored electronically, shipped over a network, and printed when they are needed • The major benefits result from reducing obsolescence, eliminating warehouse costs & reducing or eliminating delivery time

46. HICSS PERSONAL PROCEEDINGSCase Example – EDM: Improving the Publishing Process Hawaii International Conference – System Sciences: • Decided to produce a paper book of abstracts, with a CD ROM of the full papers • Many participants wanted to see the full papers at the conference • A month before the proceedings participants can use a Web site to choose 20 papers they would like to have in their personal paper proceedings • Additional papers can be printed individually using a “print on demand”service • For a ‘nominal’ fee

47. Document Management:Supporting Communication Among People and Groups • The value of documents is that they transfer information across time and space • Internet can help but often still rely on ‘paper’ documents • EDM can be used to facilitate such communications among people and groups

48. TAPIOLA INSURANCE GROUP Case Example – EDM: Supporting Communications Among People and Groups • Tapiola Group offered 150 kinds of insurance policies with 300 different insurance policy forms • All preprinted by an outside print shop • Reprinting new forms often took weeks • Which represents possible loss of revenue

49. TAPIOLA INSURANCE GROUP Case Example – EDM: Supporting Communications Among People and Groups cont. Document Processing Goals • Investigate alternate way to print policies & statements • Goals: • Reduce costs • Stop using preprinted forms • Give Tapiola marketing people new ways to advertise insurance products • To make Tapiola “the most personal insurance company in Finland”

50. TAPIOLA INSURANCE GROUP Case Example – EDM: Supporting Communications Among People and Groups cont. Centralized Solution • Switched to plain paper printers from Rank Xerox • Products for electronic document processing – document can included text, data, image & graphics Conversion of the output equipment took 15 months = reduce 300 preprinted forms to 4