Electronic commerce (e-commerce) has made numerous innovative changes to the functional and structural frameworks of businesses by adopting advanced technologies in the age of global digitalization. In particular, customer-centered companies focus on aspects such as personalization, communication, and added value, which become ineffective without the implementation of business intelligence (BI). A virtual online retail company, XYZ, features an e-wallet service. It holds credit, which can be potentially used to purchase products on the site. It includes order cancellation refunds as credit and gift cards. In both scenarios, credit points expire after a period of six months. The e-commerce store needs to implement data warehousing solutions in order to report and analyze its e-wallet service. The business case includes improved business operations, faster data processing, and effective customer relationship management. The individuals involved in the integration of this solution will be top management, finance department heads, as well as external intelligence groups, specialized in data storage and business intelligence. Therefore, creating a data warehouse has a high return on investment (ROI) if the cost of the external experts is significantly lower than the projected results of meeting the company’s objectives. They include CRM optimization, wallet liabilities review, and credit payment system assessment.
The solution of the case consists of developing and implementing a dimensional model to answer some of the questions the company needs the answers to. A successful data warehouse will give XYZ an opportunity to analyze the daily balance of credit and make predictions about credit expiration rates and outcomes in a specific month. The dimensional model will be able to address the requirements of XYZ by using the company’s transactional database to make business process assumptions. All the credit and debit purchases will be captured through grain definition. The model will include various dimensions to provide context and descriptive attributes of transactions. The calculation of definitions, dimensions, and additive facts will be integral to XYZ’s credit system review and future updates. The assessment of data will also provide the company’s executives and department heads with vital information about customer behaviors and cancellation rates. The original qualities of the solution include the utilization of a star schema data mart system as well as the creation of a DimWallet and FactWallet. They will contain the necessary data about the dimensions of a particular customer (for instance, their name, contacts, customer ID, and other) and transaction facts. The main assumption of the model is the immediate availability of all the required information due to pre-existing transactional databases. The lack of existing data mining frameworks and qualified employees who could maintain the dimensional system presents various constraints.
Implementation of the system will consist of different stages, including research, planning, design, prototype, feedback, development, testing, setup, and maintenance (Yasser and Razvan, 2016). A standardized solution for the exchange of big data volumes is the creation of a centralized system, which is assumed to be available at XYZ. The model presented in the solution is developed using GitHub. The changes will include only financial, BI, and executive departments having access to a web interface and MySQL (Structured Query Language)/My ODBC (Open DataBase Connectivity) databases connected to a SQL server due to cybersecurity concerns (Yasser and Razvan, 2016). Other senior managers will be able to export open-access data using portable document format (PDF). FactWallet and DimWallet will include general information about XYZ’s transactions/customers with an option to search through them. Streamlining will help to connect the company’s existing business intelligence with new financial, customized data for review and updates.
Reference List
Yasser, A. and Razvan, D. Z. (2016) ‘Implementing business intelligence system – Case study’, Database Systems Journal, 7(1), pp. 35–44.