Benetfits by Data Warehouse

Real-time data warehousing creates some special issues that need to be solved by data warehouse management. These can create issues because of the extensive technicality that is involved for not only planning the system, but also managing problems as they arise. Two aspects of the BI system that need to be organized in order to elude any technical problems are: the architecture design and query workload balancing.

A data warehousing is defined as a collection of data designed to support management decision making. Data warehouses contains a wide variety of data that present a coherent picture of the business conditions at a single point in time. Development of a data warehouse includes development of the systems that extract data from operating systems plus the installation of the warehouse database system that provides managers flexible access to the data. The term data warehousing generally refer to the combination of many different databases across an entire enterprise. (webopidia)

As a vendor, industry will have an enormous volume of data and information. There’s total income and profit, sales per month, sales per year, inventory data, and many more. With these several numbers it is difficult to decide which one will help business to discover their new opportunities. This is the scenario where Key Performance Indicators (KPIs) comes into the picture. These are the factors which defines the business productivity and efficiency. These KPI will help business to tackle the critical business problems so stakeholder can determine the strengths and weaknesses of business and influence the future decisions. A data warehouse generates huge amount of data that can be productively operated. In today’s world, data warehousing is an uncompromising business with demanding customers. So defining the KPI’s is an important task which will display exactly what the customer

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 mix of the processes and technologies which change data into information. It includes 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.

As Wikipedia’s explanation, Business intelligence (BI) is the set of techniques and tools for the transformation of raw data into meaningful and useful information for business analysis purposes. It is a solution package, to integrate all the existing data of organizations efficiently, provide accurate report to support high level managers to make business strategic decision. BI is not a new concept, it was introduced in 1996 for the first time. As the development of BI is the ETL technologies, ETL stands for Extraction Transformation Loading. Data integration platforms focus on extracting and transforming various business data, to support the requirement of Business intelligence, Data Warehouse against the data

Business intelligence systems is complex combination of technology and analytical techniques facilitate business decision making (Chae & Olson, 2013). Additionally, BI provides relevant information on an enterprise and with regards to the market that it is in, incorporating customers, technologies, products, markets, suppliers and competition (Sangari & Razmi, 2015). A corporation generates a lot of operational and transactional data about products, services, sales, customers etc. stored across the various databases in the company we have to select the data that can be used to create intelligence after processing using extraction, transformation and loading (ETL) (Chae & Olson, 2013). Moreover, the data must generate business value, this can be done using data mining, analytical techniques to convert raw data into informational insights to predict future events (Chae & Olson, 2013). Lastly, BI enable to monitor and report company’s performance by the use of KPI’s across various domains by mapping with frameworks like Six Sigma to create dashboards and scoreboards with suggest corrective actions (Chae & Olson, 2013).

Data warehouse are multiple databases that work together. In other words, data warehouse integrates data from other databases. This will provide a better understanding to the data. Its primary goal is not to just store data, but to enhance the business, in this case, higher education institute, a means to make decisions that can influence their success. This is accomplished, by the data warehouse providing architecture and tools which organizes and understands the

Data warehousing is an efficient system which store the past as well as current data used for creating reports. Data warehousing system is used for decision making by analyzing the reports. A data warehouse is a relational database, which is designed for analysis and query. It helps an organization to consolidate and analyze data from different sources and make decision. A data warehouse environment consists of OLAP (On-Line Analytical Processing) engine, ETL (Extraction, Transformation and Loading) process, client analysis tools and other applications that manage gathering and delivering the data.

Data warehouse is aggregation of subject-oriented, integrated databases, which is designed to confirm DSS support. Now days these repository has become a focal point for DSS in organisation. These data repository used for online analytical Processing (OLAP), data mining and support queries. Decisions which are pending from a long time get resolved by analysing data warehouses. Another benefit of data warehouse is it improves the productivity by redesigning business process and work. It is challenging and technical undertaking because data comes from different sources and systems. There are some other organisational issues like sponsorship maintenance, scope avoidance and political issues. Because of these reasons data warehouse project get

Data are a vital organizational resource that needs to be managed like other important business assets. Today’s business enterprises cannot survive or succeed without quality data about their internal operations and external environment. This growth drives corporations to analyze every bit of information that is extracted from huge data warehouses for competitive advantage. This has turned the data storage and management function into a key strategic role of information age.

Making decisions is an important part of the business world. However, without the proper information and data, it is easy for decisions to be made poorly. For this reason, business intelligence has been simplifying the process of delivering information and data to key decision-makers ever since it was first developed.

Online analytical processing (OLAP) is generally used to provide analysis of complex data. Often it is applied with a data warehouse. A data warehouse is similar to a regular database but they have differences. According to Inmon a data warehouse is “a subject-oriented, integrated, non-volatile, time-variant collection of data in support of management’s decisions [3]. Data warehouses provide more detailed information than regular databases, one reason they are recommended for analytics. Another way of describing it is, “Data warehousing is a collection of decision support technologies, aimed at enabling the knowledge worker (executive, manager, analyst) to generate wiser and faster decisions. [4] When using OLAP tools with data warehousing it can give the decision maker a clear advantage, which could give insight into why it is so highly valued. These mechanisms can help increase efficiency and provide them with a powerful advantage. At the core of any OLAP system is an OLAP cube

A data warehouse (DW) can be acknowledged as one of the most complex information system modules available and it is a system that periodically retrieves and consolidates data from the sources into a dimensional or normalized data store. It is an integrated, subject-oriented, nonvolatile and a time-variant collection of data in support of management’s decisions (Inmon, 1993).

Other technology is needed to access and use the data in the data warehouse; these are applications for data mining, business intelligence, dashboards, etc.

First 3 chapters of the book are written in a way that beginners may get clear view of the basic concepts. First chapter described the need regarding strategic information, information crisis, and that the data warehousing is a better solution for information crisis. Features and components of Data warehouse, along with the concept and need of metadata is described. Various trends in data warehouse are mentioned by the author based on his own industrial experience. Areas like Continued growth in data warehousing