Posted: May 9th, 2022

Business Intelligence Paper

Business Intelligence Paper
A data warehouse is a system that involves the process of collecting data from multiple sources within the organization and outside the organization and storing the data in one place for analysis, reporting, and business decision making. Business intelligence continues to be one of the major concepts utilized in a data warehouse. Business intelligence involves processes and tools applied to storing an organization’s data in internal and external databases (Durcevic, 2019). This paper provides an analysis of how business intelligence solutions are utilized in conjunction with data warehouses. The sections covered include the best practices followed in designing a data warehouse capable of serving as a repository for the use of business intelligence. The paper also provides the steps of implementing a data warehouse and data warehouse maintenance techniques and strategies.
Best Practices That Should Be Followed When Designing a Data Warehouse
When designing a data warehouse that will be able to serve as a repository for the use of business intelligence, various best practices should be taken into consideration. The first best practice that should be considered is the impact of data sources. The data sources format and their kind is essential in making decisions regarding the design of the data warehouse architecture. The best practices that should be followed when designing a data warehouse with regard to the impact of data include conducting a detailed discovery and analysis of data sources, their types, and their formats before designing the warehouse architecture to ensure the business intelligence is considered in the design. The data sources analysis will also help in deciding the ETL framework that fits the utilization of business intelligence in conjunction with a data warehouse.
Another data warehouse best practice is the choice of the data warehouse. In designing a data warehouse system, the business should consider whether to use a cloud-based data warehouse or an on-premise system. Considering the incorporation of business intelligence, the best practice in terms of the type of data warehouse that should be considered in the cloud data warehouse. The cloud data warehouse offers various advantages, including high security and a pay-as-you-use model (Hevo Data Inc., 2020). The architecture consideration is another data warehouse best practice. To design a high-performing data warehouse capable of serving as a repository for the use of business intelligence requires consideration of various factors, including the data model, processing abilities, and the ELT or ETL model.
Steps for Implementing a Data Warehouse
The steps of implementing a data warehouse are provided in a seven-step procedure (Scott, 1999).
Step 1: Determining Business Objectives – The phase involves analysis and developing objectives based on a proper mix of administrative, production, sales, and support personnel. The key performance indicators of the organization are considered during this phase to develop a numeric measure of the organization’s activities.
Step 2: Collecting and Analyzing Information – This phase involves gathering the performance information from various data sources used by the organization in decision making. Information can be obtained from analytical and reports, such as for accounts, administration, and CRM. The phase also involves understanding how people gather and process information in the organization.
Step 3: Identifying Core Business Processes – The step involves the identification of entities that interrelate in the creation of the performance indicators. The performance indicators for a specific business process that correlate and interrelate with each other are also identified.
Step 4: Constructing a Conceptual Data Model – The creation of a conceptual model of the data is conducted at this following the identification of the business processes and their interrelations. The subjects to be expressed as fact tables and dimensions that will relate to the fact table are selected.
Step 5: Locating Data Sources and Planning Data Transformations – This phase involve identifying where the critical information needed by the organization is situated and how to move it into the data warehouse structure.
Step 6: Setting Tracking Duration – With a data warehouse consuming a large amount of storage, the organization at this stage determines the means to archive the data from time to time, with virtual storage considered the best option to archive the data.
Step 7: Implementing the Plan – The last phase of implanting a data warehouse is the implementation of the plan to allow the data warehouse system to offer to consolidate and consistent data about the organization’s operation to decision-makers.
Data Warehouse Maintenance Techniques and Strategies
The data warehouse maintenance can be conducted by adding new metrics. Adding new metrics is essential as the organization changes over time, such as launching new products that require the tracking of new predictive models. Adding new metrics will ensure new data structures are captured, and their purpose is clear to decision-makers (Flores-Lovo III, 2019). Another strategy of data warehouse maintenance is by deprecating old metrics. As the organization grows and new metrics added, the old one may become inaccurate or no longer worth analyzing. The old metrics, when not handled, may result in multiple places from which the same data can be queried. Deprecating old metrics strategy removes multiple data ensuring the data being queried is the right one.

References

Durcevic, S. (2019). The Role Of Data Warehousing In Your Business Intelligence Architecture. The Datapine. Retrieved from https://www.datapine.com/blog/data-warehousing-and-business-intelligence-architecture/#:~:text=Data%20warehousing%20and%20business%20intelligence%20are%20terms%20used%20to%20describe,insights%20through%20online%20BI%20tools.
Flores-Lovo III, J. (2019). Best practices for data warehouse maintenance. Stitch Data. Retrieved from https://www.stitchdata.com/blog/best-practices-for-data-warehouse-maintenance/
Hevo Data Inc. (2020). Data Warehouse Best Practices: 6 Factors to Consider in 2020. Retrieved from https://hevodata.com/blog/data-warehouse-best-practices/
Scott, M. (1999). 7 Steps to Data Warehousing. ITPro Today. Retrieved from https://www.itprotoday.com/sql-server/7-steps-data-warehousing

Check Price Discount

Study Notes & Homework Samples: “Please describe how you have prepared for your intended major »An enterprise technical infrastructure security plan