Success Factors: Defining “Good” solutions from the viewpoints of Warehouse Architects
In today's fast paced world, data warehouse plays an important role in all sectors including financial, IT, retail and many more. Over the past decade data warehouse has been used to derive business solutions and increase productivity for potential success. After analysing various success factors to define a good solution the main factors noted and discussed in this review are those involving the system process, information quality and organisational impacts. Success of a warehouse architect depends on many organizational factors which include data warehouse, ETL, data modelling, security and business intelligence. By taking into account all these factors
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In many ways this can be considered one of the most crucial factors of success, because if the information provided was not of ideal quality the decisions and consequences of the data processing will be unfruitful. Unless data can be confidently be said to be of high quality, it is not possible to measure it using other success factors. Quality entails being able to understand the data, any information obtained is useless unless the architect can draw thoughts and conclusions on the basis the data is accurate and …show more content…
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.
ETL refers to Extract Transform and Load. Data is extracted from relational databases, flat files and also from non-relational databases including information management systems, virtual storages and other external sources. Once the data is extracted it is then processed to meet the needs of the client. During the process the data is cleaned and reaches the load by ensuring it meets the need of the server. It is crucial the ETL designer has discussed the process with the client organisation to ensure the solutions aimed are liable to increase
Lowe’s is a home improvement warehouse that was founded in 1946 as a single store and since has grown to become the second largest in the world. As technology has evolved, Lowe’s has made many advances incorporating new systems and devices to stay competitive. The purpose of this paper is to evaluate the information technology management systems used at Lowe’s. It will look at Porter’s Five Force Model, supply chain management; data base management system, five agent-based technologies, e-commerce and system development lifecycle. Furthermore, it will look at business continuity planning, emerging trends and security vulnerabilities relates to the organization to remain competitive.
The data warehouse is part of the data provisioning function. It could be described as a big depository. Data warehouse must provide interfaces that accept transaction data from different types of transaction processing systems and move them into the warehouse environment. During this process that data are tested and validated to assure that only high quality data are accepted.
A data warehouse is different in several ways. They are used by management for making decisions, following trends, and pulling reports. They are typically used offline, have minimal users and are enormous: gigabytes to terabytes. They contain decades of data, which are read only, and added to but never updated. The data in the data warehouse is time sensitive - each row in the warehouse is time stamped so that trending of data versus time can be done. The kinds of queries that are run against data warehouses are difficult. These are decisions support databases that are used to make strategic decisions about the organization.
The four key processes in the data quality management model are analysis, warehousing, collection and application of data (AHIMA 2)
Data marts are classified in two categories. Dependent data marts draw data from a center data warehouse, sometimes known as enterprise data warehouse, where as independent data marts draw data from operational systems or external sources. For the purpose of this presentation, we are promoting the use of dependent data marts because of the benefits of the Extraction-Transformation-and Loading (ETL) process is much similar in that the data is already clean, summarized, and loaded into the data warehouse. The ETL process consists of mainly identification of the subset of relevant data and moving it to the data mart. With independent data marts on the other hand, the ELT process has to be managed in its entirety, meaning that the data has to be staged every time to be normalized, integrated, and in dimensional formats. (Ariyachandra & Watson, 2012) For these reasons, I am advocating building dependent data marts to maximize performance and to increa...
The market segment for this technology is huge and is estimated over $50 billion. As of now over 90 percent of the big companies already have data warehouse or constructing one. It has been reported that 62 data warehousing projects has shown an average return of 321 percent, with an average payback of 2.73 years. It is also said that expenditures on data warehousing technology has expected to reach nearly $500 billion
Aligning Warehouse Operational objectives/visions to SPP business objectives/visions “to maximize the effective use of warehousing resources whilst satisfying customers’ requirements and expectations”.
The last decade can be marked as a period of significant changes in the business world. Being accustomed to utilize computers as a powerful tool with its office applications such as Microsoft Word and Excel. In the 1990s office workers first faced the opportunity to share information using the Internet (McNurlin, 2009). However, the situation became even more different with the transition to the third millennium. With a further development of information technologies, the majority of big enterprises had to reconstitute their business processes and to make the transition to the Internet economy. Enterprise resource planning (ERP), supply-chain management (SCM), customer relationship management (CRM) software and the variety of other information systems became essential components of the new economy. It can be expected, that all these complex solutions were designed to bring great benefits for different sides of the corporate activity, in particular, decisions made by top-managers are expected to become nearer to the ideal, customer service is to be improved and collaboration more prolific. Nevertheless, to ensure the desired results it should be taken into account that the key concept of these reorganizations is an information or a data, dealing with which can be a serious issue, and wide utilizing of the data warehouses in contemporary organizations confirms this fact.
The area of this research only will focus on the warehouse of PKT Logistics Group (Penang) Sdn Bhd. Moreover, the respondents only focus on 50 workers or employees that work in PKT logistics Group Sdn Bhd. The questionnaire will distribute to the employees that work in PKT Logistics (Penang) Group Sdn Bhd.
Warehouse Stephen Tindall was founded in 1982 on the north coast of Auckland in New Zealand, which has opened the first store. Small in the case of Wal-Mart 's success in the USA, like the warehouse at low prices, "people want to do more of what 'was offering. Clark compared to their attention by the retail trade, cost accounting, inventory management and distribution systems, however, (2000), the warehouse were similar to Wal-Mart 's core competencies.
Asset – Equipment that is utilized, but not consumed, in the production of goods or services supporting the program mission. An asset is a resource controlled by the enterprise as a result of past events and from which future economic benefits are expected to flow to the enterprise.
For the past couple of decades the majority of businesses have wanted to construct a data-driven organization or company. Furthermore, companies around the world are considering harnessing data as a basis of competitive advantage over other companies. As a result, business intelligence and data science use are popular in many organizations today. The increase in adoption of these data systems is in response to the heavy rise in communications abilities the world over. Which, in turn ,has increased the need for data products. Indeed, the Data Scientist profession is emerging to be one of the better-paying professions due to the urgent need of their labor. This paper is going to discuss what business intelligence is all about and explain data science that is usually confused to be similar to business intelligence. I will tackle a brief overview of data scientists and their role in organizations.
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.
As data remains one of the most important aspects of every business, companies are gradually placing lots of importance on the quality of data used. Databases use different formats or styles. This can make the data collected to be extremely clumsy and sometimes unintelligible.
The dynamics of our society bring many challenges and opportunities to the business world. Within the last decade, hundreds of jobs have emerged particularly in the technology sector to help keep up with the ever-changing world and to compete on a larger and better scale than the competition. Two key job markets and the basis of this research paper are business intelligence or BI and data mining or DM. These two fields play a very important role in small to large companies and are becoming higher desired sectors within the back offices of the workplace. This paper will explore what the meaning of BI and DM really is, how they are used and what we can expect as workers and learners of the technology and business fields for the future.