Data mining and Data warehousing are used daily in a wide variety of contexts. In today’s corporate world, decisions must be made rapidly and with the maximum amount of knowledge. Data warehousing is the process in which data from multiple sources is combined and stored in one common database (Gutierrez). The fundamental concept of data warehousing is the distinction between data and information. Data is observable and recordable facts but only comes to have value when it is presented as information. Data warehousing begins with the information acquired from source databases such as transactional and operational systems. This information is then transformed, organized further and loaded into a central depository. Some data warehousing techniques and tools including data warehouse appliances, platforms, architectures, data stores (Data Warehousing, n.d.). A data warehouse appliance is a combination hardware and software product that is designed for analytical processing. Data warehousing includes retrieving data, analyzing, extracting, loading, transforming and managing the data. In the Figure 1.1, its shows the process for data mining and data warehousing. Figure 1.1 The structure of a data warehouse can be simple data sources going directly into a warehouse, which then is directly visible by the users, to data sources being filed through a staging area before going into the warehouse, and then being categorized for the viewing of the users by the usage of data marts. A staging area is the component data must run through before being gathered in the warehouse. In the staging area, the data is simplified before it is deposited into the warehouse. Since there may be so much data stored in a warehouse... ... middle of paper ... ...n the deployment phase, insight and usable information can be derived from data. Deployment can involve the integration of data mining models within applications, data warehouse infrastructure, or query and reporting tools. There is so much stored digital data in the world today, but we are starving for knowledge. The process’ of data warehousing and data mining brings a lot of benefits to businesses, society, governments as well as individuals. There are many everyday business uses of data warehousing and data mining and particular tools available. In the future, companies need to evaluate the costs associated with data warehousing and data mining and the resources needed along with any other considerations. For now, data warehousing and data mining technology will continue to bring profits to businesses that choose to utilize the technology.
Smith, W., & Jewett, D. (2009). Tableau software and teradata database the visual approach to the active data warehouse. In Retrieved from http://www.tableausoftware.com/learn/whitepapers
On the output side of the data warehouse, there must be interfaces that allow a user to ask for data from the devices (PCs or terminals) that he typically use. The information in the warehouse must be structured, so that users can easily obtain answers to their questions.
A data warehouse can be implemented through two-tier or three-tier architectures depending on the business requirements (Database Modeling & Design, 1998).
Ans: Data warehouse can be defined as the repository of data. It is a pool of data which helps in decision making. It contains historical data as well as current data. The data usually first structured in a form that can be easily read by analytical processing activities like OLAP, data mining etc. The benefits of data warehouse are: it helps in decision making; it provides the single version of truth though the data is collected from various sources which in turn will help to determine the current state of business and identify problems, enhanced system performance and accessing of data is very simple. As many applications are web based, even the users access data via web and the data from web can be put into the data warehouse; web accessibility is important with data warehouse.
Data mining refers to the use of data from big data source by using different tools in order to solve real time problems that include logistics management, scientific research, processing, financial decision making, retail, and medical research. The important use of data mining can increase the development in the information industry as well as providing benefits to the social cause
As the Big Data era advances, the significance of data is changing. In addition to supporting business decisions and transactions, data is often now the good being traded, as companies begin to grasp the seemingly boundless potential value inherent in the data itself. Decreasing storage costs combined with the ability to collect data passively (through technological progress) mean that many companies are finding it easier to justify preserving the data rather than discarding it once its primary function h...
At a physical level, data warehouses tend to be heavily indexed and partitioned to put the most used data on faster storage. There are other options available as well. Data warehouses are typically designed with specific questions in mind, but as data grows, the warehouse gains value because there are new questions that can be asked if only the organization is perceptive enough to see them. Those questions and their answers can lead to new opportunities for designing a competitive advantage.
In today’s society, technology has become more advanced than the human’s mind. Companies want to make sure that their information systems stay up-to-date with the rapidly growing technology. It is very important to senior-level executives and board of directions of companies that their systems can produce the right and best information for their company to result in a greater outcome and new organizational capabilities. Big data and data analytics are one of those important factors that contribute to a successful company and their updated software and information systems.
The main components of a data warehouse are the data source, data storage and the end users. The data is extracted from an external source and is passed through an ETL process, which converts the data into an output suitable for analysis. In order for this process to achieve the expected results the data staging process must be done correctly to ensure the needs of the organisation are met. By doing so the chances of a successful system are much higher.
Generally, data mining can be associated with classes and concepts. data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is the best analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among the dozen fields in large relational databases.
In this article Hormazi, Amir, Giles, Stacy mention the importance of Data mining and how it has now become an indispensable tool for every industry. Data mining allows people to access valuable information of large amounts of data collected to be organized to gain an upper hand versus other competitors. This tool allows insurances, financial and retail institutions to access information on their consumers at the speed of light by narrowing down specific information.
A data warehouse comprised of disparate data sources enables the “single version of truth” through shared data repositories and standards and also provides access to the data that will expand frequency and depth of data analysis. Due to these reasons, data warehouse is the foundation for business intelligence.
Prior to the start of the Information Age in the late 20th century, businesses had to collect data from non-automated sources. Businesses then lacked the computing resources necessary to properly analyze the data, and as a result, companies often made business d...
Data Science is a new interesting software technology, which is used to apply critical analysis, provide the ability to develop sophisticated models, for massive data sets and drive the business insights. Data science is an umbrella term used to describe how the scientific method can be applied to data in a business setting. Data science is also playing a growing and very important role in the development of artificial intelligence and machine learning. Although the differences exist, both data science and data analytics are important parts of the future of work and data. Data Analysts take direction from data scientists, as the former attempts to answer questions posed by the organization as a whole. Both terms should be embraced by companies that want to lead the way to technological change and successfully understanding the data that makes their organizations run. Company need both data scientists and data analytics in their project. Both are part of company’s
Data can be organized a specific way for each business to be able to get the best use. Employees can also access the system at the same time but in different ways. For example, the customer service team can pull up documents and keep track of complaints at the same time that the marketing team is in a