Introduction to System Analysis

Introduction to system analysis means the process of observing systems for troubleshooting or development purposes. System analysis is applied to information technology, where computer-based systems require defined analysis according to their makeup and design. System analysis is conducted for the purpose of studying a system or its parts in order to identify its objectives. It is a problem-solving technique that improves the system and ensures that all the components of the system work efficiently to accomplish its purpose. Analysis specifies what the system should do.

Benefits of Systems Analysis

There are many reasons why you might want to analyze a system. These include learning to use systems that somebody else created, planning new systems, and reducing errors when problem-solving. Sometimes systems analysis is a necessity. For example, if you buy a company and want to hire your own staff, you might find out that you are now an owner of a series of systems you know nothing about. It might be impossible to use the systems until they are analyzed, especially if all of the original staff are gone. This can happen on a much smaller scale when you start a new job or move to a new department. If there isn't anyone there to explain how a system works, you might have to figure it out for yourself.

Installing a new system, whether it's a home entertainment center, a factory production line, or way of working in an office, requires proper planning. Without this planning and systems analysis, the change might not work. You need to understand how the current system works before you install a new one. Otherwise, there may be problems, and the project could be a failure.

Data Collection

Data collection is the process of gathering and measuring information on variables of interest, in an established systematic fashion that enables one to answer stated research questions, test hypotheses, and evaluate outcomes. Some of its uses are:

• To understand what exactly the user needs
• What data/information should be processed in the system, and what sort of interface design the users expect.

Techniques

Structured analysis and design technique (SADT) is a systems engineering and software engineering methodology for describing systems as a hierarchy of functions. SADT is a structured analysis modeling language, which uses two types of diagrams: activity models and data models.

• Structured Interview
• Unstructured Interview
• Direct Observation
• Questionnaires
• Statistical Sampling

Data Analysis

Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis.

Types of Data Analysis: Techniques and Methods

There are several types of data analysis techniques that exist based on business and technology. The major types of data analysis are:

• Text Analysis
• Statistical Analysis
• Diagnostic Analysis
• Predictive Analysis
• Prescriptive Analysis

Text Analysis

• Text Analysis is also referred to as Data Mining. It is a method to discover a pattern in large data sets using databases or data mining tools. It is used to transform raw data into business information. Business Intelligence tools are present in the market which are used to make strategic business decisions. Overall it offers a way to extract and examine data and deriving patterns and finally interpretation of the data.

Statistical Analysis

• Statistical Analysis shows "What happens?" by using past data in the form of dashboards. Statistical Analysis includes the collection, analysis, interpretation, presentation, and modeling of data. It analyses a set of data or a sample of data. There are two categories of this type of Analysis - Descriptive Analysis and Inferential Analysis.

Diagnostic Analysis

• Diagnostic Analysis shows "Why did it happen?" by finding the cause from the insight found in Statistical Analysis. This Analysis is useful to identify behavior patterns of data. If a new problem arrives in your business process, then you can look into this Analysis to find similar patterns of that problem. And it may have chances to use similar prescriptions for the new problems.

Predictive Analysis

• Predictive Analysis shows "what is likely to happen" by using previous data. The simplest example is if last year I bought two dresses based on my savings and if this year my salary is increasing double then I can buy four dresses. But of course, it's not easy like this because you have to think about other circumstances like the chances of prices of clothes are increased this year or maybe instead of dresses you want to buy a new bike, or you need to buy a house. So this Analysis makes predictions about future outcomes based on current or past data. Forecasting is just an estimate. Its accuracy is based on how much detailed information you have and how much you dig into it.

Prescriptive Analysis

• Prescriptive Analysis combines the insight from all previous Analyses to determine which action to take in a current problem or decision. Most data-driven companies are utilizing Prescriptive Analysis because the predictive and descriptive analysis is not enough to improve data performance. Based on current situations and problems, they analyze the data and make decisions.

Descriptive Analysis

• analyses complete data or a sample of summarized numerical data. It shows mean and deviation for continuous data whereas percentage and frequency for categorical data.