Starbucks is a company that is specialized in offering a range of products including coffee, handcrafted beverages, merchandise, and fresh food. As an enterprise, they require a proper data management to enable them serve their customers efficiently. Data on sales, customer views, customer information, market analytics, products, and production needs a proper storage and retrieval system hence the use of data warehousing. To make informed decisions, the management at all the levels within the company requires data analysis to make those decisions.
The Coffee Company has a website for buying their coffee products, as well as gifts, and explores the coffee world by learning more about its origin. The data warehouse stores are built using SQL Server 2000 that stores information about the occurrences on the website. Business Desk reports enable processing of data imported from the website. Several steps are involved in exporting these data. The first step is reporting that is provided through Business Desk. These data includes weblog data, user profile information, campaign information, catalog information and transaction data (Microsoft, 2000).
Data cubes are prepared by running the report processing tasks. The Business Desk is secured and can only be accessed by Starbucks Corporate networks and allows only Secure Socket connections. Reports that resides on the data warehouse server can be accessed and viewed through from the business application.
The Business plan on Swot Starbucks Coffee Company Products
SWOT Analysis StarbucksStrengthso Starbucks Corporation is a very profitable organisation, earning in excess of $600 million in 2004. The company generated revenue of more than $5000 million in the same year. o It is a global coffee brand built upon a reputation for fine products and services. It has almost 9000 cafes in almost 40 countries. o Starbucks was one of the Fortune Top 100 Companies to ...
To plan the data warehouse at Starbucks, three aspects of the site must be taken into consideration. The storage, processing, and bandwidth requirements are the elements needed to deploy the data warehouse. The storage requirements consider the amount of space required for web log files. The number of servers, web log file sizes per server per day and total log file sizes must be known in advance for the data warehouse planning. After sometime, these accumulated preferably three months, archiving should be done on old data to ensure that the business users will be able to view and run historical data. Since the data is imported from the website, processing time is of great importance to the success of the warehouse. Therefore, time to import web log files and processing time of web log files into analysis cubes is necessary for planning purposes. Lastly, consideration of bandwidth requirements is done before deploying the data warehouse. For example, the data bandwidth used will be for moving the web log files. Also considered is the bandwidth required for actual running of the reports.The process of creating a data warehouse is procedural. It begins by building a business model followed by definition of the requirements of each model. Identification of data sources is carried out after business modeling. The process of building the data warehouse is done after the selection of data warehouse tools (Vincent, 2007).
Data collection through asking the question about the performance of the company will help identify data to appear on the data warehouse. Reports from time reporting system, accounting packages, and customer relationship management application are other important sources. Designers of the data warehouse have to find a way of harmonizing these data with the knowledge of how people process information within the company.
In making the decisions, the data within the system are retrieved for analysis. This process is known as extraction. It is defined as the process of retrieving data from a source for use in the data warehouse environment. The extracted data can then be transformed and finally loaded into storage. The primary internal data sources for a data warehouse in Starbucks is the transaction processing application. Data extraction methods are of two types that include full logical and physical extraction method and depend on the business requirements, performance and source system. In logical extraction method, there are two subdivisions, complete extraction, and incremental extraction. Full removal is where the data is completely extracted from the system source files. No additional information is necessary on the site. The second data extraction method is the physical extraction method. Physical extraction is of two types, online and offline extraction. Online mining, extraction is directly from the source files. The process of extraction can directly connect either source tables’ or the intermediate data store. The latter, offline extraction, is where data is sourced outside the source files.
The Business plan on Data Warehouse Information Database Analysis
... the data warehouse. This phase includes 1. Automating and scheduling the data extraction process 2.Automating and scheduling the data conversion process 3. Automating and scheduling the data load process ... specifications, and mapping the source data to target data warehouse database design. This phase covers 1.Defining the possible source systems 2. Determining file layouts 3. Reviewing ...
Conclusion
For a leading international company like Starbucks, planning, building and maintenance of a data warehouse are very critical and requires technical expertise. The building process requires cooperation from IT and business people in order to come up with a successful data warehouse. For implementation purposes, it requires coordination by all stakeholders to highlight all the requirements, needs, and tasks. Breaking down of the data collected enables incorporation of all the requirement to appear on the data warehouse.
References
BIBLIOGRAPHY l 1033 Microsoft. (2000).
Starbucks technical deployment guide. Microsoft.
Vincent. (2007).
Building a Data Warehouse. Apress.
