Systems Analysis: History, Concepts And Theories

Systems depth psychology History, Concepts And Theories matchless could unarguably suggest that system of ruless exist, in various forms, since the dawn of time. From the Solar System, to our planets ecosystem, to the valet societies where the great unwashed gathered into teams to hunt animals or farm the land to be fitting to survive, solely ar prime examples of thousand years oldish systems. By exposition, a system could be described as all(prenominal) entity, conceptual or physical, which consists of interdependent parts (Ackoff, 1960).As the clement-control conduct systems (i.e. society, organizations, information systems and pedigree systems) started to create increasingly much complex, various issues appeared like an increase of costs, harder nourishment and to a greater extent administrative complexities. The need to overcome and solve all these problems led to the appearance of the field of Systems analytic thinking.Systems abstract History, Concepts Theori esThe analysis, as defined in the Oxford Dictionary, is the detachment of a substance into parts for study and interpretation detailed examination. Subsequently, Systems Analysis could be described as the early bear upon in the knowledge of a new system, or the evaluation of an old one, where the analysts try to go over a presumptuousness situation, identify the main problems that need to be solved, return them up into sub-problems if needed, and finally recommend the practiced efficient and costless mien to solve them (Yeates et al, 1994 Silver et al, 1989 Bingham et al, 1978). Plato once said the beginning is the virtually Copernican part of work. Nowadays, Platos words are proved far from wrong in the case of takeing or evaluating a system. The first steps of work on a new project are probably the most master(prenominal) ones to guarantee any fair chance of success. This is the main yard wherefore many organizations, companies and governments prefer to spend a sig nificant list of money in the early stages of growing, in order to be able to minimize the risk of potential disaster later on (Daniels et al, 1981). subsequently all, the sooner a mistake is identified, the sooner it forget be fixed, delivery a lot of labour, time and money.There are many types of human-controlled systems, as previously mentioned, ranging from large-scale, complex human societies (whose boundaries are unremarkably non so easy to define as they constantly interact with otherwise societies near them), to small-scale figurer information systems (whose boundaries are easier to define). Although each key write and researcher tried to describe his let concept of what analysis is and why it is critical to apply it in the organic evolution process, their thoughts and views share many everyday elements. Depending on the type of system they concentrated on, various definitions where assumption.To begin with, Systems Analysis is the process of investigating a sy stems boundaries, users, processes, inputs and divulgeputs with the aim of suggesting to a greater extent than efficient and frugal ways to solve the problems in question (Silver et al, 1989). Another, much general soupcon is that Systems Analysis refers to an orderly, structured process for identifying and solving problems (Gore et al, 1983). Finally, according to George Marshall in his book Systems Analysis and tendency Alternative Structured Approaches, Systems Analysis is the process of defining precisely what a computer system should do (Marshall, 1986).Igor Hawryszkiewycz describes in his book Introduction to Systems Analysis and Design that analysis is mainly apply in order to effectively understand the structure of a system and what its requirements are (Hawryszkiewycz, 1994). John Bingham in his book A Handbook of Systems Analysis describes the analysis in six steps the project selection, the feasibility study, the definition phase, the design phase, the implementat ion phase and finally the evaluation phase (Bingham et al, 1978). possibly one of the most straightforward explanations of Systems Analysis main objective is that it aims to translate user inevitably into specifications for programmers (Marshall, 1989). To achieve this, the systems analyst has to complete five tasks and responsibilities to plan, investigate, understand, chronicle and communicate with the end of the team (Yeates et al, 1994). Firstly and probably most importantly too, the analyst must study the feasibility of the system. This means that he has to good search and decide if it is humanly assertable to develop the system and how a good deal movement in time and money it will cost to do so. Second step is to discuss with the systems target group and find out their needs in order to be able to understand and prolong the requirements. Third step is to research the existing data, human recourses and available computer procedures to find out the limitations, techniq ues or methods that will be utilize in the later stages of knowledge. Usually from this phase and on the analyst works close up together with the designers, programmers and testers in order to establish a successful chat between the team and share feedback with them (Parkin, 1980 Daniels et al, 1981 Open University, 1982).Although the human-controllable systems are in human race ages now, Systems Analysis as a scientific field is quite more recent, and its roots can be traced back a few decades. onward computers became mainstream, the first analysts where using more traditional courtes to analyze and solve the given problems. They followed two main steps firstly they analyzed the projects requirements and secondly they condition these requirements. Although this practice was logical and theoretically correct, it depended too much on the human factor, which means it was prone to mistakes. Among the disadvantages of the traditional approach was that it required vast amounts of indite documentation, many times there was a lack of communication between the analysts and the designers and last but not least it was very time consuming. every(prenominal) these negatives caused a great number of system information projects to face difficulties during the analysis phase in the 1970s (Yeates et al, 1994).Researchers in the field of Systems Analysis, in an effort to overcome all the problems caused by the traditional approach, focused their attention to develop new, more efficient methods of analysis. The result of the above efforts was a structured approach to analysis (Yourdon, 1976 DeMarco, 1979 Bansler, 1993). This approach, as described in the book Systems Analysis and Design by Don Yeates et al, follows three general principles modeling, partitioning and iteration. Modeling is the lengthened use of models, diagrams, data flow charts and other graphic representations, which aim to picture a non-confusing, realistic image of the system to the rest of the d evelopment team. sectionalization is the method of dividing the system in question to sub-systems with the aim of making them more understandable to the rest of the team. Moreover, partitioning helps the analyst to decide which part of the alone problem every member of the team will be given to solve. Iteration is the method of constantly repeating the analysis stage, as many times as needed, in order to reach the best possible solution. The need for iteration arises from the fact that it is rare for a system to be represented correctly the first time, as many repetitions are usually needed, in order to achieve a standard of accuracy (Yeates et al, 1994). side by side(p) the appearance of a more structured and formal way of analyzing a system, researchers were trying to come up with various models, which held a central mathematical function for Systems Analysis. These models, if strictly followed, would significantly enhance the development process. The early software product dev elopment models though, such as the Waterfall Model (Royce, 1970), did not allow a lot of room for feedback and transmutes because of their linear structure. In contrast to these early models, circumscribed approaches like the V-Model (German Ministry of Defense, 1992) and the Spiral Model (Boehm, 1988) gave the analyst the flexibility to interact with the rest of the team level(p) in the later development phases. This is particularly important for the sole reason that, as discussed earlier, Systems Analysis is one of the most authoritative phases during a systems creation. By providing the team the option to interact with the analyst on the go, it can minimize the time needed for system revision and most importantly save a lot of time and money.Although return of investment and risk minimization are two of the most salient reasons of why Systems Analysis is so important, there are a lot more benefits to be gained in the long term. The efficiency of the project team is greatly enhanced as goals are reached faster and the available resources are used more wisely. Furthermore, errors are recognized earlier which translates to less time invested in testing during the final phases of a project, which in its own turn leads over again to more profit (Silver et al, 1989).It is very important also to mention that forthwith systems become so complex that usually consist of multiple subsystems, each one playing a key role in the safe and sound process. These subsystems coexist and highly depend on each other. Any change that may occur in any of them could affect multiple other subsystems of the safe and sound. It is critical for the analysts to spend a considerable amount of time and effort to understand the system as one single entity and identify all of its problems. Only after a thorough study of the system they will be able to really understand its purpose and support the development team in creating a system that will be safe, half-hardy and effective (H awryszkiewycz, 1994).In 1994, a study by the Standish Group provides a better understanding of how valuable the correct application of analysis during a systems development phase is. The company studied eight thousand software projects undertaken by 350 different companies in order to see how successful the development process was. The results were disastrous as around 31% of the projects were cancelled earlier they make it to the production phase (Standish, 1994). When in further study, these companies were asked about the reasons for these failures, more than 54% answered that it was various problems during the analysis phase (Standish, 1995).Systems analysis though is not a cure-all medicine. Like all approaches in system development, it has its own disadvantages and limitations. Some company, for example, could have so many internal problems, financial or not, that a single Systems Analysis, even the best one possible, could not be enough to save it from bankruptcy. Another dis advantage is that Systems Analysis costs a lot of money and time (Silver et al, 1989). Although, it has been proven many times that projects that went through a thorough System Analysis phase had greater chances to become successful, its still not a guarantee. As the human factor never stops to play a vital role in the whole development process, there is always the risk that something is mistakenly overlooked (i.e. a bug in the code that escapes the final testing phase), which leads to a deformity product in the production line, which in turn leads to less sales or even stigmatize the organizations good name.ConclusionSystems Analysis has become a necessity, a highly important and integral tool that development teams, governments and companies use to enhance their productivity and raise their profit margins (Silver et al, 1989). As people say money makes the world go round, and this is especially true nowadays that organizations put even more effort to identify and satisfy their needs in the most effective and efficient way. Systems and projects become even more sophisticated, even more advanced, even more complex and even more critical for the safety of the users. Developers must be able to adapt in this ever-changing environment if they want to survive in the highly belligerent world of today. For all these reasons, Systems Analysis continues to play a key role, and researchers are trying constantly to find new ways to make it even more efficient in the future.