Data mining is the extraction of knowledge from the various databases that was previously unknown (Musan & Hunyadi, 2010). Data mining consists of using software that conglomerates artificial intelligence, statistical analysis, and systems management in the act of extracting facts and understanding from data stored in data warehouses, data marts, and through metadata (Giudici, 2005). Through algorithms and learning capabilities data mining software can analyze large amounts of data and give the management team intellectual and effective information to help them form their decisions. The intention for data mining is to analyze prevailing data and form new truths and new associations that were unknown prior to the analysis (Musan & Hunyadi, …show more content…
These visualization tools that are made through the management team’s queries, are synthesized with the reports. Charts, tables, and graphs can help management teams make important decisions by allowing them to see data in a quantitative view. The management team can use this new perspective to understand all areas of their query and, in turn, they can make better and more informed decisions.
How Business Intelligence Systems are used Business intelligence systems are the combination of operational data and analytical tools to aid managers to comprehend the “capabilities available in the firm, the industrial trends, future development in technologies and the market and even their competitor’s actions and the resulting implications” (Wang, 2014, p. 1083). There are many ways managers can use the information a business intelligence system gives them such as understand customer behavior, analyzing competition understanding investment options, and knowledge protection.
Customer Behavior The market place, even at a local level, is more competitive then it has been at any other time in history. Companies must put a major emphasis on gaining customer loyalty and understanding customer behavior in order to compete in today’s market place. Tuta et al. (2014) offer that intelligent technologies provide new options for manages to understand the market place they partake in and the competition in that market place. Customer Relationship Management systems are a new way
What is Business Intelligence (BI)? BI is the way that using modern data warehouse technology, online analytical processing, and data mining to analysis the data then achieve business value. As a tool, BI is used to deal with the existing data in the enterprise, and convert it into knowledge, analysis and conclusions, and then help the decision-maker to make a right and wise decision.
Today there is a new data management challenge that is an effective method for integrating enterprise applications. To learn from history and predict the future, plenty of companies are using Business Intelligence (BI) systems. Corporations have understood the significance of intensifying achievements of the objectives defined by their business strategies through business intelligence ideas.
Business intelligence is the ability of a business to be able to extract actionable insight from business as well as market data, which is used to make better decisions in business; and to improve the corporate performance of the business. Business intelligence must exist for businesses in the world today to survive(Electrosmard Ltd). Almost every business today worth its salt is looking for the appropriate business intelligence technology in order to survive in today’s fiercely competitive world. Business intelligence also helps companies and businesses to survive during hard economic times. During such periods, it is not a surprise to find companies still spending on the processes of business intelligence because without such solutions, there is no business at all. In any case, there is a business; it is most likely on the decline in terms of productivity and revenues. Business intelligence is not a onetime thing; it is an ongoing process. Business intelligence goes on as long as the business is still running; the business intelligence continues to exist too.
In the increasingly competitive global business environment, each organization needs to take advantage of every tool, opportunity, and advantage it can to achieve the best products and services, to gain and maintain market share, and keep stakeholders happy from investors and workers to supply chain and customers. The advance of data analysis has opened up new vistas to support decision making. Decision support systems (Sauter, 2010) have emerged that process various forms of data to build outcome models. These have been adopted in every segment of society, in the private and public sector, from political campaigns and the military to corporations and nonprofits alike. As a whole, the new set of tools involving the strategic use of data is called business intelligence. Within that general framework, the term analytics refers to the statistical, quantitative use of data to produce explanatory and predictive models for fact-based decision making. (Sauter, 2010).
Traditionally, business intelligence (BI) has been used as an umbrella term to describe the concepts and methods to improve business decision making by using fact-based decision support systems. BI also includes the underlying architectures, tools, databases, applications, and methodologies. BI’s major objectives are to enable interactive and easy access to diverse data, enable manipulation and transformation of these data, and provide business managers and analysts the ability to conduct appropriate analyses and perform the actions [Turban et al. 2008; Wixom et al. 2011]. Successful BI initiatives have been reported for major industries, from healthcare and airlines, to major IT and telecommunication firms [Anderson-Lehman et al. 2004; Carte et al. 2005; Turban et al. 2008].
Business Intelligence is the gathering and analysis of large amounts of information so as to gain insights that propagate strategic and tactical business decisions. Business Intelligence is the conglomeration of the processes and technologies which change data into information. It encompasses a wide category of technologies, including data warehousing, multidimensional analysis or online analytical processing, data mining and visualization, as well as basic queries and multiple types of analytical tools for reporting. These technologies allow business stakeholders to collect, store, access, and do the analysis of data to improve the business decision-making capabilities (Khan, 2005).
Both data mining and data analysis are a subset of Business Intelligence which also includes data management systems, data warehouses and Online analytic processing(OLAP). To manage the mountains of information, the data is put away in a warehouse of information accumulated from different sources, including corporate databases, compressed data from interior frameworks, and information from outer
Many other terms are being used to interpret data mining, such as knowledge mining from databases, knowledge extraction, data analysis, and data archaeology. Data mining is one of the provoking and significant areas of research. Data mining is implicit and non-trivial task of identifying the viable, novel, inherently efficient and perspicuous patterns of data. Figure 1 represents the data mining as part of KDD process. The hidden relationships and trends are not precisely distinct from reviewing the data. Data mining is a multi-level process involves extracting the data by retrieving and assembling them, data mining algorithms, evaluate the results and capture them. Data Mining is also revealed as necessary process where bright methods are used to extract the data patterns by passing through miscellaneous data mining
Data mining is the procedure of getting new patterns from large amount of data. Data mining is a procedure of finding of beneficial information and patterns from huge data. It is also called as knowledge discovery method, knowledge mining from data, knowledge extraction or data/ pattern analysis. The main goal from data mining is to get patterns that were already unknown. The useful of these patterns are found they can be used to make certain decisions for development of their businesses. Data mining aims to discover implicit, already unknown, and potentially useful information that is embedded in data.
Data mining generally is the process of analysing data from different perspectives and summarising it into useful information (Thuraisingham, 1999). It is also called the “Knowledge Discovery in Databases” process. It can be understand in the way of discovering interesting and useful patterns and relationships in large volumes of data. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for future use. (Han & Kamber, 2006)
Data mining is finding the routines and examples in large databases to guide choices about future exercises. It is normal that data mining tools to get the model with negligible information from the client to identify. Data mining is the utilization of automated data analysis techniques to discover already undetected connections among data things. It regularly determines the
This capability enables users to ask their own questions of the data, without relying on IT to create a report. In particular, the tools must have a robust semantic layer to allow users to navigate available data sources. These tools should include a disconnected analysis capability that enables users to access BI content and analyze data remotely without being connected to a server-based BI application. In addition, these tools should offer query governance and auditing capabilities to ensure that queries perform well.
The worldwide information landscape is changing rapidly due to innovation in technological solutions. Technological solutions play a crucial role in decision-making through analysis of a massive data or big data. The explosives changes in the supply and demand of the market require firms to respond proactively and spontaneous. Accordingly, enterprises managers should be able to collect, filter and analyze the enormous amount of data from many sources in real-time. Therefore, organizations turned to use technologies for decision-making, solve the complex and challenging business problems. The tools used to support decision-making has become very wide and filled with the buzzwords. In this report, we will outline the connection between the two types of software solutions that support the decision-making process; Decision Support Systems (DSS) and Business Intelligence (BI).
In every day live, the word ‘Mining’ refer to the process that discovered a small set of valuable pieces from a great deal of raw material as in mining process of gold from rocks or sand. According to [3] Data Mining, or Knowledge Discovery in Databases (KDD) as it is also known, is the process of extraction of implicit information that previously unknown and potentially useful from database. By using a number of different technical, such as clustering, data summarization, learning classification, finding dependency networks, analyzing changes, and detecting anomalies. Data Mining refers to a variety of techniques that can be used to analyses and observes database in order to find relationships or summarize the data in ways that can be put to use in different areas such as decision making, prediction and estimation and to do that there are a sequence of the process [2] . As show in figure (1.1)
From a practical perspective, Data Mining automates the whole process of categorizing and discovering new understandable relationship by using advanced tools and utilizing some basic understanding of statistics, machine learning and database systems. The useful accurate information we acquire after applying this process is reusable and utilized to take important steps towards increased revenue, reduced costs in retail, financial, communication, and marketing business organization. The wide range of applicability in heterogeneous domains which comprises of large volume of rich data makes Data Mining an important and challenging sector for the Data scientists.