Business Information Systems PDF

“Business information systems” The role of data mining in supporting modern business --------------------------------------------------- The primary role of data mining in business intelligence is to identify patterns, trends, and relationships within datasets that are not e...

“Business information systems” The role of data mining in supporting modern business --------------------------------------------------- The primary role of data mining in business intelligence is to identify patterns, trends, and relationships within datasets that are not easily recognizable. This process of pattern discovery offers valuable insights to data analysts that further help in making productive business decisions. Data mining is important as it enables organizations to make data-driven decisions. By uncovering hidden patterns and relationships, businesses can optimize operations, identify market trends, predict customer behavior, and improve overall performance. What is the purpose of data mining in modern businesses? Data mining is the process of searching and analyzing a large batch of raw data in order to identify patterns and extract useful information. Companies use data mining software to learn more about their customers. It can help them to develop more effective marketing strategies, increase sales, and decrease costs. -------------------------------------- Key words (DATA, INFORMATION , KNOWLEDGE & DATA MINING) Data : The raw material of information or factual information (such as measurements or statistics) used as a basis for reasoning, discussion, or calculation. Data is a collection of information gathered by observations, measurements, research or analysis. They may consist of facts, numbers, names, figures or even description of things. Data, as a general concept, refers to the fact that some existing information or knowledge is represented or coded in some form suitable for better usage or processing. Data is organized in the form of graphs, charts or tables. The data is classified into majorly four categories:  Nominal data.  Ordinal data.  Discrete data.  Continuous data. Information : Information is an abstract concept that refers to something which has the power to inform. At the most fundamental level, it pertains to the interpretation (perhaps formally) of that which may be sensed, or their abstractions. Knowledge : Knowledge is the understanding, awareness, or familiarity gained through experience, education, or learning. It encompasses facts, information, and skills acquired over time. The concept of Knowledge is a familiarity, awareness, or understanding of someone or something, such as facts, information, descriptions, or skills, which is acquired through experience or education by perceiving, discovering, or learning. Knowledge can refer to a theoretical or practical understanding of a subject. Data Mart Data mart is a simple form of data warehouse. Data mart is a simple form of a data warehouse that is focused on a single subject or line of business, such as sales, finance, or marketing A data mart is a data storage system that contains information specific to an organization's business unit. It contains a small and selected part of the data that the company stores in a larger storage system. Companies use a data mart to analyze department-specific information more efficiently. Data mining :  Data mining is the process of sorting through large data sets to identify patterns and relationships that can help solve business problems through data analysis. Data mining is a process by which great amounts of data are analyzed and investigated. Data mining techniques and tools help enterprises to predict future trends and make more informed business decisions. Data mining is a crucial part of any successful analytics initiative. Businesses can use the knowledge discovery process to increase customer trust, find new sources of revenue, and keep customers coming back. Effective data mining aids in various aspects of business planning and operations management. Businesses use data mining for different types of insights. These can vary depending on the size of the business, its industry, and its operations. In many cases, firms rely on data mining to get insights into customer behavior or preferences. The data mining process includes projects such as data cleaning and exploratory analysis, but it is not just those practices. Data mining specialists clean and prepare the data, create models, test those models against hypotheses, and publish those models for analytics or business intelligence projects. Data mining is used to explore increasingly large databases and to improve market segmentation. By analysing the relationships between parameters such as customer age, gender, tastes, etc., it is possible to guess their behaviour in order to direct personalised loyalty campaigns, The 4 stages of data mining :  takes place in four main stages:  Data Pre-processing  Exploratory Data Analysis   Data Selection  Knowledge Discovery. Top-10 data mining techniques: Classification. Classification is a technique used to categorize data into predefined classes or categories based on the features or attributes of the data instances.  Regression  Clustering  Association Rule  Anomaly Detection  Time Series Analysis  Neural Networks  Decision Trees. the basic steps of data mining, follow the list below.  Cleaning of Incomplete Data  Integration of Data  Reduction of Data  Transformation of Data  Data Mining  Pattern Analysis  Sharing Final Report -------------------------------------------------------------------