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
Knowledge is very important because if a project is based or awarded on competitive bidding then a contractor may not know the information about possible design flaws, submit a low bid, and recoup profits when changes are required.
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 3 percent decline in sales causing a 21 percent decline in profits can be attributed to the identification of the accounting concept of operating leverage. Operating leverage is what business managers apply to boost small changes in revenue into sizable changes in profitability. Fixed cost is the force managers use to attain disproportionate changes between revenue and profitability. Therefore, when all costs are fixed every sales dollar contributes one dollar toward the potential profitability of a project. Once sales dollars cover fixed costs, each additional sales dollar represents pure profit. A small change in sales volume can significantly affect profitability (Edmonds, Tsay, & Olds, 2011). So, therefore, if sales volume increases,
Data warehouse developer is responsible for maintaining the practices of modeling, dimensional data, relational structures, and other reporting techniques. Candidates possessing an inner desire for long term employment opportunities in a team oriented fast paced wonderful environment. They need to be qualified with Information Systems, Computer Science and other related fields. It would be preferable to possess minimum of 7 years’ experience with warehouse design and analysis experience with complete knowledge about data modeling and warehouse methodologies. Job requirements for Albertsons Data warehouse developer jobs-
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
quality we can predicate from it. The systems that fail are those who rely on
The four key processes in the data quality management model are analysis, warehousing, collection and application of data (AHIMA 2)
... different layers such as ETL stage, SIF, BDW and how data is processed to generate reports according to the requirement. The processing of information from raw data to different processing stages culminating in coherent information is fascinating.
The three main criteria that are most important in a quality system are as follows:
First problem identified in ERP implementation is, difficulties in transferring data from previous application. The biggest problem in ERP implementation is management of data transition from old system to new ERP system. Programmers find that transferring data are the most tedious work to do. Conversion of the data requires tremendous patience to deal with it as it is a complex task to do. The ERP system first need to fit with the company requirement so that the data can be transferred easily and smoothly and also, data need to be formatted to meet the standard of the new system. The conversion can be done manually or by an automated program. If it is done by using an automated program, programmers need to make sure that the formatted data do not jeopardize the file system integrity. There are two types of data migration; static data migration and dynamic data migration. Static data migration is data that change infrequently like engineering master files and price book. The migration need to make early on so that later can focus on more challenging tasks of dynamic data migration. Next is dynamic data migration, it refers to volatile data which include shop work orders and account receivable open items. Due to the changeable nature of this type of the data, it should be migrated as late as possible. Late migration ensures that the system includes the most up to date data.
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.
Wholesalers acts as a lesion between manufacturers of commodities and other industries that are interesting in selling the same products. Along this distribution chain wholesalers usually purchase goods in large quantities and in turn sells them to retailers who ultimately supplies goods and services to consumers. Due to the available space at wholesale locations they are able to store products for distribution to retailers which reduces retailers storage costs. Wholesalers are able to store goods in large quantities which allow retailers to purchase in small quantities. Due to this option retailers are able to only purchase what is needed at that given point (Kotler & Keller, 2012). Additionally, because wholesalers are able to purchase goods
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.
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.
Curtis G. & D. Cobham (2002: 4th edition) Business Information Systems: Analysis, Design and Practice. Essex: Pearson Education Limited