Data Warehouse Report: Best Practices, Implementation, and Maintenance

This report delves into the crucial role of data warehouses as the backbone of business intelligence, emphasizing their significance in data analysis and management. It outlines best practices for data warehouse design, including using the smallest data type possible, utilizing surrogate keys, and implementing the star schema for optimal performance and ease of understanding. The report further details the step-by-step process of implementing a data warehouse, from determining business objectives to constructing a conceptual data model and planning data transformations. Finally, it discusses essential maintenance techniques and strategies such as periodic updates, data cleansing, and user training to ensure the data warehouse remains efficient and secure over time. The report is well-researched and referenced.

Running head: DATABASE WAREHOUSE
Database Warehouse
Name of the Student
Name of the University
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Business Intelligence Solutions in Conjunction with Data Warehouses:
In the current context the data warehouse is crucial for the business intelligence as it is
the backbone of business intelligence (Laursen & Thorlund, 2016). For a proper type of data
analysis management of the data must be done in a proper way and for this reason data
warehouse is very much important. Data warehouse plays an important role for managing the
data in a proper way so that these data can be analyzed properly as per the business requirements.
In this way data warehouses becomes crucial for the business intelligence procedures.
Best Practices for Designing a Data Warehouse:
For creating a proper type of data warehouse it is crucial to follow the finest practices for
designing the data warehouse. In this way it can be ensured that the designed data warehouse is
capable of becoming a data repository for the utilization in business intelligence purposes.
Important practices are discussed below.
 Using Smallest Type of Data Possible: For creating a proper data warehouse, ready for
the business intelligence use it is very much important use smallest data type possible
(Scabora et al., 2016). By using the smallest type of data, the data storage and semantic
model can be optimized easily. In this way the performance of the ETL tools and the
SQL server analysis services can be also affected greatly which are important in this case. Utilization of Surrogate Key: Surrogate keys are important database keys used for
relating fact tables with the dimension tables. These keys are mainly assigned at the time
of record loading process into the dimension table. These key are very much useful for
tracking the history of dimension record. Compared with the business keys surrogate

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keys are having better performance which are important for business intelligence. Thus
this practice must be followed. Implementing Star Schema: Implementation of the star schema is one of the most
important practice that must be followed at the time of data warehouse implementation.
The star schema design must be followed whenever possible as it is very much easy to
understand (Khnaisser et al., 2015). Also, it requires very few joins so that a meaningful
query can be produced. Also, in most of the cases the star schema provides a better
schema compared with the normalized model. This model also works very fine with the
services of SQL Server Analysis. For this reasons it is one of the best practices for the
developing the data warehouse.
Steps for Implementing a Data Warehouse:
There are several of important steps which must be followed at the time of implementing
the data warehouse. These steps are discussed in the following section.
1. In the first step the objective of the business must be determined. By the determination of
organization’s business objective the data warehouse can be developed as per the
requirement of the organization which is important in this case (Jukic, Vrbsky &
Nestorov, 2016).
2. The second step deals with collection and analysis of the information. This step is
important to identify how the peoples gathers information and processes information.
3. The third step of creating a new data warehouse is identification of core business
processes. This step is crucial so that data warehouse can be implemented properly.
4. The fourth step of initiating the data warehouse in a proper way is the construction of
conceptual data model (Khojah & Mannino, 2017).

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5. The fifth step and one of the most important step for implementing the data warehouse in
a proper way is tracing the sources of data and planning the transformations of data. This
is important here because it can assist to identify where the critical information resides
and how these information can be moved to the data warehouses.
6. Next important step for implementing the data warehouse is the setting down the tracking
duration. Tracking duration is important to determine how the data will be archived with
the increased amount of time.
7. The last step of initiating the data warehouse is implementation of the developed plan. In
this way the project can be scheduled properly and can be completed in an appropriate
manner.
Data Warehouse Maintenance Techniques and Strategies:
Some important steps for proper maintenance of the data warehouses are discussed in the
following section.
1. In the first step the data warehouse must be updated periodically and in this aspect it can
be done by removing the duplicating entries.
2. Web log of the database must be lean and all the summarized data must be deleted
properly on a consistent basis.
3. The warehouse must be expanded as per the requirement of the organization (Rahman,
2016).
4. The data warehouse must be transformed by combining multiple of fields into a single
type of field.
5. Users of the ware house must be trained properly for the using of the data warehouse. It
will ensure the data house remains safe.

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References:
Jukic, N., Vrbsky, S., & Nestorov, S. (2016). Database systems: Introduction to databases and
data warehouses. Prospect Press.
Khnaisser, C., Lavoie, L., Diab, H., & Ethier, J. F. (2015, September). Data warehouse design
methods review: trends, challenges and future directions for the healthcare domain.
In East European Conference on Advances in Databases and Information Systems (pp.
76-87). Springer, Cham.
Khojah, M., & Mannino, M. (2017). Mastering Data Warehouse Maturity Concepts Using a
Serious Game: Design and Implementation of Emerge2Maturity. In Proceedings of the
EDSIG Conference ISSN (Vol. 2473, p. 3857).
Laursen, G. H., & Thorlund, J. (2016). Business analytics for managers: Taking business
intelligence beyond reporting. John Wiley & Sons.
Rahman, N. (2016). Enterprise data warehouse governance best practices. International Journal
of Knowledge-Based Organizations (IJKBO), 6(2), 21-37.
Scabora, L. C., Brito, J. J., Ciferri, R. R., & Ciferri, C. D. D. A. (2016, April). Physical data
warehouse design on NoSQL databases. In Proceedings of the 18th International
Conference on Enterprise Information Systems (pp. 111-118). SCITEPRESS-Science and
Technology Publications, Lda.