Posted: August 1st, 2022

Developing a Data Management Strategy for Stimpy

Developing a Data Management Strategy for Stimpy
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Developing a Data Management Strategy for Stimpy
Abstract
Data storage is one of the significant components that enterprises depend on to conduct their operations. Most companies today have invested in sophisticated data storage technologies capable of collecting, analyzing, and providing data that can support the business strategy and operation processes. Old Stuff is Cool (OSIC), which is investing in Stimpy, a multi-national chain of a fast-food restaurant that operates on the franchising model considers the data warehouse technology as their data storage solution. The data warehouse technology has undergone developments such as the incorporation of cloud-based storage services, multidimensional database, and relational storage technology that make it the best storage technology to support Stimpy operations. The main organization’s purpose of considering the data warehouse is its usability, which includes the ability to support decision making through three queries techniques that include the update, strategic, and tactical query. The three queries are database operations that acquire, analyze, and provide information used for both short term and long term decision making. The cloud-based data warehouse provides further benefits to the organization through its features that include flexibility, scalability, fast, and high data security. Another important usability concept of the data warehouse is the data quality that incorporates data profiling, standardization, cleansing, enrichment, matching, and monitoring. Data quality enables the organization to understand different aspects regarding its operation, such as customer behavior and loyalty, market segmentation, compliance measures, product segmentation. The architecture of the data warehouse system proposed for Stimpy is based on a star schema that incorporates the operational stage system, the data staging area, the data presentation area, and the data access tools.
Introduction
Stimpy is a multi-national chain of fast-food restaurants that operate on the franchising model. The company has experienced high competition that has led to a decline in investments. However, the company has an opportunity to introduce a data management technology that would be used to revitalize its brand. The franchising company, Old Stuff is Cool (OSIC) is investing in Stimpy and considers using a data management strategy to improve the reporting system of Stimpy. The data management technology identified by OSIC that would be used in its operation at Stimpy is the data warehouse technology. The data warehouse is defined by Lester, 2003, as “a subject-oriented, integrated, time-variant, non-volatile collection of data in support of management’s decision-making process.” The definition provides two major processes are involved with the data warehouse; data collection and data management from heterogeneous sources. The data collected in the data warehouse is separate from the database, and the design of the data warehouse is based on the production of business-oriented day-to-day reports. This paper provides the data warehouse technology strategy based on the requirements of Stimpy business operations. The major aspects presented in the paper include the major developments in data warehousing and cloud solutions; the use of data warehousing within the organization and quality of data; and the analysis, design, and building a current data warehousing technologies.
Developments in Data Warehousing and Cloud Solutions
Data storage is one of the significant components that enterprises depend on to conduct their operations. There have been developments in data storage with respect to the data warehouse and cloud solutions. In the data warehouse, storage of data can be achieved through the multidimensional database technology that organizes stored data around the dimensionality of the data. An example of the multidimensional database used in the data warehouse includes Essbase that has the capability of storing data in independent data mart for the end-user to access directly. The major advantage of the multidimensional database is that it offers fast response times to users (Watson, 2001). Another data storage technology for a data warehouse that has been developed is the relational database technology. The storage models applied in the relational storage technology include the star schema data model, which is a structured star model that includes a fact table in the middle of the star and points of the star hosts dimension tables. The fact table hosts numerical data recorded for each transaction. Information stored in the fact table includes retail details such as the number of units sold and sales amount; communication information such as call lengths, and the average number of calls; and banking and insurance information. The dimension tables in the star schema data model provide measures that can be used to provide further analysis of data provided in the fact table. Examples of data provided in the dimension tables include product name, category, customer name, staff name, tax, and store name. The data stored in the dimension tables can be acquired daily, weekly, monthly, and annually.
Cloud solutions are one of the widely used data storage technology. Cloud data storage has experienced several developments in the past two decades. Cloud-based warehouse is a cloud computing model, whereby cloud computing providers use the internet to provide data storage and management services. The cloud storage eliminates the costs and just-time capacity associated with managing and buying data storage facilities for most organizations. Some of the major types of cloud storage solutions that have been developed include object storage such as Simple Storage Service (S3) that are characterized by their ability to take advantage of object storage’s vast scalability and metadata. File storage is another type of cloud storage that has been developed, such as Amazon elastic File System (EFS), that offers services such as storing huge content repositories and media stores (Amazon Web Services, 2020). The modern cloud-based data warehousing is considered to be cost-effective and suitable for storing a large amount of data. The capabilities of cloud solutions enhance the operations of the organizations, especially during peak hours, by providing instant data analysis critical for decision making. The cloud-based data storage allows the user to pay for only services required, which is essential during off-peak seasons where the organization does not encounter mass data income (Cabot Technology Solutions, 2019). Some of the cloud data warehousing providers that can be considered by Old Stuff is Cool (OSIC) for Stimpy data management systems include Amazon Redshift, Azure SQL Data Warehouse, and Azure Databricks.
Use of Data within Organizations
Data is essential in the business operations of the organization, especially for decision making. Data warehousing’s main goal is supporting the organization’s decision-making process. Data warehousing within an organization can be used in acquiring answers to queries that face the company and its customers. The type of queries that are used by the data warehousing to support decision making within an organization includes the tactical query, which is an operation based-database that is used to provide the immediate best cause of action (Pathak, Singh, & Oberoi, 2013). The strategic query is another type used in the data warehouse, which is used by organizations to acquire information necessary to support long term business decisions. The strategic query helps the organization to find an answer regarding what has happened, the reasons for it happening, and what will take place next. The strategic query analysis large quantities of comprehensive data provided in the data warehouse through a range of analyses such as table scans, sub-queries, and multi-way joins to deliver the best possible approach for decision making. Another type of query is the update query, which is applied in analyzing huge amounts of data in the data warehouse in a well-organized way based on the requirements of the company to support the decision-making process.
When considering the data quality, organizations depend on the data quality solutions to assemble data from different sources, with the data being eligible for supporting the business operations of the organization. Some of the aspects of data quality that are useful for organizations include profiling, which is a process of examining whether the existing data sources meet the quality standards required by the business operations of the organization. Profiling helps the organization to identify issues that may require instantaneous consideration, which enables the organization to avoid the processing of unacceptable data sources, hence improving the decision-making process (Elahi, 2020). Another aspect of data quality that is essential to an organization is cleansing. Cleansing is used by the organization to ensure required rules and regulations are properly met within the data sets. Standardization is another data quality technique that is utilized by organizations to parses and restructures data into a common format that provides more consistent data for easy interpretation and decision support.
Another important aspect of quality data used by organizations is matching, which entails consolidating data records into identified groups and linking records across the data sets. The organizations use data matching in marketing decision making through information related to customer profiling, such as gender, age, and locations (Manjunath, Hegadi, & Ravikumar, 2010). The organizations also use data enrichment, another type of data quality, to understand their buyer behavior and loyalty potential. Data enrichment technique provides enhanced customer data by combining and adding additional pieces of data from other sources such as demographic, geocoding, gendering, email, and phone number verification, and full-name parsing to construct understandable customer information (Manjunath, Hegadi, & Ravikumar, 2010). Lastly, data monitoring, which provides real-time monitoring process capable of detecting when the data exceeds pre-set limits, is another data quality aspect. Organizations use data monitoring to identify and act upon issues related to data decline, data governance, and compliance measures.
Therefore, the major benefits of the data warehouse that Stimpy will encounter include better decision making on business strategy and operations that are backed up by solid facts and statistics. The organization can utilize the data warehouse on business processes such as management of finance, inventory, sales management, market segmentation, and staff management. Stimpy will also benefit from a large amount of data provided from more avenues that can be processed within a short time to acquire understandable high-quality data for business operations (Cabot Technology Solutions, 2019). The organization will also have access to consistent quality data that will increase company-wide accuracy. Stimpy will be able to save costs associated with data analysis compared to the traditional model in place. Another benefit for the organization will be achieved through the cloud-based data warehouse that is more fats, scalable, flexible, and offers high data security.
Data Warehousing System
The design that would be used in the data warehouse is the star schema design that incorporates four major separate and distinct components. The data warehouse components will include the operational source system, also known as the load manager, which performs the process of extracting and loading data into the warehouse (Leonard, 2011). The data loading and extraction process include the transformation of data into the right structure for entering the data warehouse. The physical architecture of the data warehouse begins with the second component, which is the warehouse manager also known as the data staging layer, is the first stop of data from transformations conducted by the load manager. The data enters the data staging layer through the ETL, a tool that conduct the extraction, transformations, and loading data from data sources, before continuing to the actual data house. The ETL systems mostly run on Windows or Linux operating systems, however, UNIX-based operating system is also used for some ETL.
The third component of the architecture is the data presentation layer, also known as the query manager. The data presentation area involves data that is in fact tables and dimensions for scheduling the execution of queries (Lester, 2003). The last component of the data warehouse architecture is the presentation layer, also known as the end-user access tool, which provides users with data in the best format required in making decisions effectively. The presentation layer is categorized into different groups that include data reporting, query tools, OLAP tools, and data mining tools, and application development tools (Guru99, 2020). The organization applies each tool based on the data needed within the business operation context.
Conclusion
The data warehouse system that is being considered by OSIC to be applied in Stimpy business strategy and operations will transform the data collection and analysis that is essential in archiving the business goal. The implementation of the data warehouse system is established on different factors, such as developments that have occurred in data warehouse and usability. Major developments in the data warehouse that are essential for Stimpy include multidimensional database and relational storage technology, and cloud solutions. Considering the business operation requirements of Stimpy, the data warehouse system will be a key technology for centralizing and analyzing data due to varies application ways. Stimpy can apply the data warehouse system to support decision making through three queries techniques that include the update, strategic, and tactical query. The three queries are database operations that acquire, analyze, and provide information used for both short term and long term decision making. The information provided by data warehouse can be used in licensing Stimpy know-how, procedures, intellectual property, brand, rights in selling their branded products and services to franchisees. The data warehouse also provides data security protecting the business data from both internal and external attacks that might be used to jeopardize the operations of the organization. The architecture of the data warehouse is designed to cover four major aspects required by Stimpy data centralization and analysis. The design involves data extraction, analysis, management, and reporting process, which offers the organization enough information for conducting its business processes.

References
Amazon Web Services, 2020. Cloud Storage. Available at: https://aws.amazon.com/what-is-cloud-storage/. Accessed on 16 Mar. 2020.
Cabot Technology Solutions, 2019. Getting Ready for Modernization: Traditional or Modern Warehousing. Available at: https://www.cabotsolutions.com/traditional-and-modern-warehouse-using-cloud. Accessed on 16 Mar. 2020.
Elahi, E., 2020. What Does Data Quality Mean for Your Data Warehouse? Data Ladder. Available at: https://dataladder.com/what-does-data-quality-mean-for-your-data-warehouse/. Accessed on 16 Mar. 2020.
Guru99, 2020. What Is Data Warehousing? Types, Definition & Example. Available at: https://www.guru99.com/data-warehousing.html. Accessed on 16 Mar. 2020.
Lester, M., 2003. Factors in the Design and Development of a Data Warehouse for Academic Data. Electronic Theses and Dissertations. Paper 759. https://dc.etsu.edu/etd/759
Leonard, E., 2011. Design and Implementation of an Enterprise Data Warehouse. Master’s Theses (2009 – ). Paper 119. http://epublications.marquette.edu/theses_open/119
Manjunath, T., Hegadi, R., & Ravikumar, G., 2010. Analysis of Data Quality Aspects in Data Warehouse Systems. International Journal of Computer Science and Information Technologies. Vol. 2 (1), 2010, 477-485
Pathak, M., Singh, S., & Oberoi, S., 2013. Impact of Data Warehousing and Data Mining in Decision Making. International Journal of Computer Science and Information Technologies. Vol. 4 (6), 2013, 995-999
Watson, H., 2001. Recent Developments in Data Warehousing. Communications of the Association for Information Systems. Vol.8, Article 1. Available at: http://aisel.aisnet.org/cais/vol8/iss1/1. Accessed on 16 Mar. 2020.

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