1. INTRODUCTION Data Warehousing is a set of decision support technologies, which allows executives, managers, and analysts to make informed decisions, thereby better and faster. It provides basic planning tools for businessman and his workers organize, understand and use their data to make accurate decisions. Data Warehouse is a database used for analysis and to make reports in a business. It is known to be the database that is maintained individually from the company’s operational database.
1998, p.19) In simple words, Data Warehouse can be understood as an integrated data repository containing historical data of a corporation for supporting decision-making processes. A data warehouse provides a basis for online analytic processing and data mining for improving
This huge amount of data needs to be used either for business growth or scientific discoveries. The process of discovering the patterns and relationships in data using the analysis tools is called Data Mining. The simplest form of data mining is as follows: 1. Describing
On the other hand data mining is used to find the patterns set for big amount of data set. Data mining basically tries to aims at examining or explore the collection of tons of millions of data using database queries. Data mining is mainly focusing on the overall practices and traditional way of automatically searching within the large stores of data to uncover the patterns in the data and trends that usually go beyond from the simple analysis and statistical data. Data mining uses the sophisticated and organized mathematical algorithms to segment data set and predict the occurrence of future events. Queries are written and they can be processed on the data warehouse.
1. INTRODUCTION Data plays significant role in the growth of any organization. When harnessed, data provides valuable insights that help in making strategies for business growth. Traditional approaches for analyzing data can be traced back to manual analysis and computerized analysis through data mining. Data mining is the process of discovering interesting facts from data sources.
Assignment 3: Business Intelligence and Data Warehouses Student: Juan C. Lord Course: CIS11 Professor: Jodine Burchell Strayer University 8/30/2017 Business Intelligence and Data Warehouses We all know what a database and a database warehouse is but do we know the differences? Well typically, the online transaction-processing database is like a health system that keeps records of vast patients. This database is usually has one application. This type of Databases does not have analytics. A database that does perform analytics is a warehouse database.
Database is a significant field in computer science. Databases provide well-organized, consistent, convenient and safe multi-user storage and can access massive amounts of persistent data. Databases allow the user to extract only the data and amount they need. Database system are for different types of things such as university data to store student details, banks to store customer details and their account history and also online shopping to sell products. Database systems are currently experiencing an information explosion, used to manage huge amount of data all over the world, the database industry is very profitable one.
The reason I have chosen this organisation is because I work part time in a petrol station and I know both the pros and cons of using the Station Master software. What is big data? Big data is a term used to describe the large volume of data, both structured and unstructured, that inundates an organisation on a day to day basis. The importance of big data does not depend on the volume of data but rather, what data is analysed and what it is used for. When big data in an organisation is combined with analytics it can be used to reduce costs, determine the roots of failures in a short period of time and overall improve the efficiency of said organisation.
Spend data management principles and solutions being applied to multiple business areas: The link between spend data management and sourcing and supplier management is obvious. However, accurate spend data is an equally critical to other business objectives, including compliance management, inventory management, budgeting and planning, and product development and management. Renewed focus in these areas is fueling additional interest in spend data management. 3. E-sourcing users view spend data management applications and services as key to next round of savings.