Information Systems

Information Systems

Data, information and knowledge • Data are individual facts which, by themselves, are not very meaningful • e.g. the account was debited by $50 • Information is the organisation of one or more pieces of data to answer a complex question • e.g. a debit of $50put the account into the red • Knowledge is the use of information to establish useful patterns • e.g. don’t draw a cheque for more than you have in your account

Evolution of IS • IS evolution in most organisations has been: • from automation to augmentation and • from bottom to top • from simple to complex • from local to global • from individual to collaborative • specialisation

From automation to augmentation I • Many computer applications automate the handling of routine transactions these are calledTransaction Processing Systems (TPS) • TPS generate huge amounts of data • Usually, TPS allow users or managers to query the system, finding useful information • who made the sale at 1.47 on register 3 • how many fridges did we sell today • TPS are one of the simplest types of IS • IS that collect data from manufacturing or scientific testing are often quite simple as well

From automation to augmentation II • Originally most organisations wanted to use IS to improve worker efficiency e.g. office automation • The benefits have NOT exceeded the costs • Many of the benefits of IS have been intangible • higher quality, more professional, worker satisfaction • Many tasks, such as planning and decision making were not amenable to automation so later IS were intend to help or augment planners and decision makers rather than to take over their role

From bottom to top • The flow of IS through many organisations was from line workers to managers to executives • The large number of line workers suggested potentially large benefits from automation • The data collected by TPS etc was needed by managers but in a refined form. • This led to Management Information Systems MIS and Decision Support systems (DSS) for modelling decisions and Executive Information Systems (EIS).

From simple to complex • The tasks carried out by line workers are often quite routine while those of managers are more complex. The tasks carried out by executives can be highly complex and virtually unique • To support these tasks, individual IS often became highly specialised - suiting only a few users • More complex IS often needed new technologies to be effective e.g. OLAP, visualisation tools. Some made use of Expert Systems and many are now using intelligent agents to carry out very complex analyses in conjunction with users

From local to global • As IS developed, so did the Internet, becoming the World Wide Web. • The Web offered organisations a “standard interface - the browser - which could be used to provide TPS, MIS, EIS etc to all sorts of users at virtually any location • Many organisations are currently struggling with the process of migrating applications to the Web

From individual to collaborative • More complex IS using the Web allowed more than one person to share an application and the data files. This gave rise to the idea of Computer Supported Collaborative Work (CSCW) • CSCW systems often use a “blackboard architecture” to allow users to share ideas • This is particularly useful for tasks like collaborative design e.g. engineering equipment, but also for any other collaborative task

Specialisation • As IS have evolved, developers have found new places, new niche markets that could be served • Geographical Information Systems (GIS) allow users to carry out complex analyses of data that has a strong geographical component - mapped • Many early IS simply stored and retrieved information. These Information Retrieval Systems were often found in libraries • These have become more specialised such as Medical Information Systems, Legal databases, graphical databases e.g. video libraries

Knowledge systems • Technologies, like expert systems and agents do more than capture data which they present as meaningful information • They try to capture what people “know” • how to organise a shipping schedule • when to invest in the local share market • why a specific medication suits a specific disease • These systems capture knowledge and provide “intelligent” assistance to users. They are often called knowledge based systems

Knowledge management • It is important to capture knowledge in an organisation because • it is very valuable • it can be lost otherwise • it makes it easier to find and apply • it can be applied by lower level employees • home loan applications • pipeline maintenance

Knowledge portals • As more and more organisational data is placed on the Web and Intranets, it is becoming more and more difficult to retrieve that information • Knowledge based systems can be built which: • know what information is relevant to a task, • who should have access to it and • where that information is kept • Web based systems which provide this type of assistance are known as knowledge portals

Diversification • All of the trends we have seen, occurred in parallel • more intelligent systems for more complex tasks by more people • This has led to the specialisation of IS to many tasks. This, in turn, means that specialised IS are found everywhere • airline booking systems • stock exchange systems • banking and insurance systems • hospital administration • defence systems

Analysis and indexing • Search, retrieval, linking, navigation • Scalability, efficiency, and effectiveness

How “good” is an IS • Despite all the care taken to build them, many IS fail • They may • not provide the functions that were required • be too slow • be too hard to learn and too hard to use • To evaluate an IS we use software metrics • things that we know we can measure to see if the IS is up to standard

Speed - throughput • IS work in two basic modes - batch processing and interactively. • Batch processing was the predominant method, historically, and is still used today. e.g. on Thursday night, the payroll system • calculates everyone’s pay and • deposits it to their bank account or • prints a cheque • In batch mode we are concerned with how many transactions the IS can process in a given time - this is called the throughput

Speed - response time • In interactive mode, a user enters data and the IS processes it immediately. The user, particularly when working with a customer, expects the system to carry out EACH transaction quickly. • The time it takes to finish a transaction (or part of a transaction) is called the response time. • E.g. the user enters a customer account number and presses Enter. The IS finds the customers record and displays the person’s name and details in 1.3 seconds. This is the response time. • Many users mean a good throughput is needed, too

Ease of use and navigation • It is difficult to measure ease of use • standard tasks could be timed - benchmarking • user perception questionnaires can be used • One of the factors affecting ease of use is navigation. An IS has good navigation when a user finds it easy to • carry out the steps in a common task or • move from one task to another • This is particularly important in Web based applications and Web sites, where navigation can be very flexible

Information retrieval (IR) and search engines • IR systems, like library catalogues, are often used for non-specific searching i.e finding items with a common feature rather than just a specific item • A system which finds ALL of the items that are related to a query has good recall • A system which finds only the MOST relevant items to a particular query has good precision • These metrics are not widely used in the general IS literature BUT they are quite significant in the field of search engines - particularly Web engines

Information capture and representation I • TPS capture data when line workers enter it • Higher level systems, like MIS & EIS, can also use data entry operators but it is more efficient to share the data between TPS, MIS, EIS etc. • But some MIS/EIS need data from 2 or more TPS • This poses some problems - incompatibility of OS databases, data formats e.g. filed lengths names etc • This led to the development of databases & EDW

Information capture and representation II • Some data, like geographical data on maps, are more “generic”, so specialists produce this data and sell it to IS developers/users e.g. GIS users • Other data, like sound graphic and and video data cannot be represented the same as text so they need special tools to capture the data and to store it e.g. mp3, wav, jpg, mpg etc. • Databases needed to be enhanced to store these other data formats e.g real estate databases • Many highly specialised databases e.g. scientific and statistical data

Information capture and representation III • The recent development of Business Intelligence has led to a need for totally new types of databases • Multi-dimensional databases (MDDBs) do not store “records” like relational databases. Instead they store hypercubes of numerical data that can be quickly searched by OLAP and data mining systems • These systems tell managers about unexpected values, problem trends and identify previously unknown relationships in a company’s data

Information privacy, integrity, security and preservation • IS make information widely available to people, and that is good, but it raises questions about • who has access to my information - privacy • is the information about me correct- integrity • can the info be lost or tampered with - security • who should keep info about me, for how long, is it properly stored or can it be lost • You will look at these social issues in much more depth later

Information Systems