These data is obtained from different operational sources and kept in separate physical store. A data warehouse is not only a relational database that contains historical data derived from transactional data but also it is an environment that includes all the operations and applications to manage the process of gathering data, and delivering it to business users such as extraction, transportation, transformation, and loading (ETL) solution, an online analytical processing (OLAP) engine, and client analysis tools. Data warehouses have no standard definition and the people who work on data warehouse subject have defined it in many ways. “The basic data warehouse architecture interposes between end-user desktops and production data sources a warehouse that we usually think of as a single, large system maintaining an approximation of an enterprise data model.” (O'Donnell, 2001) “A data warehouse is a copy of transaction data specifically structured for querying and reporting.” (Kimball et al. 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.
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.
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.
Mining different types of Knowledge in databases b. Handling noise and incomplete data c. Efficiency and scaling of data mining algorithms d. Handling relational and complex types of data e. Protection of data security, integrity and privacy 2. Data Mining and Big Data Comparison Feature Data Mining Big Data Focus It mainly focusses on lots of details of a data It mainly focusses on lots of relationships between data View It is a close up view of data It is the Big Picture of data Data It expresses what about the data It expresses Why of the data Volume It can be used for small data or big data It refers to large amount of data sets Definition It is a technique for analyzing data It is a concept than a precise term Data Types Structured data, relational and dimensional database. Structured, Semi-Structured and Unstructured data (in NoSQL) Analysis Mainly Statistical Analysis, focus on prediction and discovery of business factors on small scale. Mainly data analysis, focus on prediction and discovery of business factors on large scale.
In terms of sources, formats, modes and feeds – data influx happens in all shapes and sizes. Analytic tools therefore need to be smart enough to decipher all the diverse natures of data, assimilate them with advanced algorithm development, optimization and automation to bring it on a uniform, consumable format. Data governance and security: Increase in mobility and access to information has led to massive discussions around data governance, protection and security. Industries such as banking, healthcare, pharma, and defense are under strict compliance and regulatory mandates that make it a tough job to create a proper data protection framework. It is not enough to have an IT infrastructure and security in place.
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.
The amount of data collected from an organization determines the uses of Big Data concept in that organization. Mainly full functionalities of Big Data are achieved in organization where data resides in trillions of Exabyte’s. Database mainly stores data of one variety but with Big Data variety of data is stored in the repositories and move over search algorithms work evenly fine in all variety of data in real time scenarios. Velocity is one of the terms that makes Big Data the future of enterprise. Quick results is the requirement of the day today but preciseness of the data is also the main requirement.
Specifically, data warehouses extract, clean, transform, and manage large volumes of data from multiple, heterogeneous systems, creating a historical record of all customer interactions Constantly extracting knowledge about customers reduces the need for traditional marketing research tools such as customer surveys and focus groups. All the information such as product/services, buying pattern, geographic location everything is available to customer contact point in the organization. Other than transaction detail additional information can be help full for example, shipping status, product history, promotional packages. Brief outlines of organizational benefits with a data warehouse are: Accurate and faster access to information to facilitate responses to customer questions. data quality and filtering to eliminate bad and duplicate data Extract, manipulate and drill-down data quickly for profitability analysis, customer profiling, and retention modeling Advanced data consolidation and data analysis tools for higher level summary as well as detailed reports Calculate total present value and estimate
A central big data platform will help organization focus on what it needs. For examples some companies are focused on strengthening their customer relationships; others are focused on managing business and systemic risk, while some focused on improving operational efficiency. i.e. A centralized big data platform will be able to offer solutions to units, which have different needs, in an organization. integrating a variety of sources of data, including unstructured data, such as legal documentation, trusts, stock filings, corporate actions.