What is a data warehouse? As the name suggests, it is a “data warehouse.” It is an organized and predefined repository that facilitates data storage and retrieval. The purpose of a data warehouse is not only to use data in business processes but also to analyze data and create knowledge from data.
More specifically, a data warehouse is one or more databases that store data according to a defined schema. The schema defines the structure of the content and how the different tables in the database are related. For example, a customer number is a unique identifier in each table.
It is important to understand that further searches in the data warehouse should be as simple and efficient as possible. Therefore, the schema and content should be defined, documented, and stored in advance. The principle of a ‘stored schema’ repository naturally means that a lot of time and thought has to be put into the use scenarios and the repository’s structure.
As a straightforward comparison, you can think of the data warehouse as a well-stored Excel spreadsheet. Data is grouped into columns and rows into which each entry must fit to make sense. The spreadsheet makes it easy to perform simple analyses such as summaries or pivot tables.
In reality, data warehouses are very complex. There are many problems with scaling, access, security, data quality, history, versioning, etc.
In short,This database in which data is used for analytical purposes such as visualization or business intelligence. In the following, we will discuss the advantages and disadvantages of a data warehouse and what features are essential for a data warehouse.
Advantages of a Data Warehouse
- Structured and centrally collected data
- Easy access and reuse
- Maintenance of data and processes
- Facilitate data-driven thinking through simple implementation
- Reduce overall search and reporting time
- Never replace operational systems and reporting.
- Develop a training plan for end users.
Disadvantages of Data Warehouse
- Failure to collect unstructured data
- Business rules usually need to be better defined and documented.
- New data sources are often not integrated seamlessly
- Often poor granular/temporal data analysis
- Time-consuming data warehouse implementation
What Are the Roles of a Data Warehouse?
The data warehouse concept is based on the idea that data is not only used for business purposes. Therefore, many people need to use a data warehouse:
BI Experts
One or more business intelligence expert is at the heart of a data warehouse. They know the architecture, documentation, and implementation of the data warehouse and organize the integration of new data sources. They are often also directly responsible for the preparation of reports etc.
Domain Experts
Although BI experts cover different areas, a data warehouse’s added value comes from specific areas, such as sales, marketing, or logistics. Therefore, the experts in these areas are responsible for integrating the correct data and performing the proper analyses.
Controlling
The company’s controlling department is one of the most common business information sources. It can be closely linked to a data warehouse.
Database Administration
Those who rely on SQL databases need IT technical support.
Cloud Solutions Engineers
If you rely on a cloud solution, you need to build and maintain it. Whether you’re a cloud solution engineer, data engineer, or data architect, your role is valuable because you keep the data flowing and the infrastructure running.
Data Analysts
In addition to the general BI specialist, analysts are often responsible for a data analytics area or central unit. It is essential to combine data expertise to get the most helpful information.
Visualization Experts
If a company is well versed in data and dashboards, it can outsource the visualization itself, i.e., the creation of the dashboard, to experts.
Data Analysts
Another type of customer is the analyse. If very high-resolution data is available, data mining can be used to gain new insights.
Using Data from A Data Warehouse
It can be used in a variety of ways. In most cases, its produced in visual or tabular form to provide company decision-makers with an overview of the status of processes. This descriptive and historical analysis can be seen in the following applications:
- Reporting: KPIs and tabular reports on important key figures.
- Dashboards: Visualization of the status and evolution of key data.
- Business intelligence: Advanced the analysis in the warehouse to identify business strengths and opportunities.
- Integration of data science: With a high resolution of the data, methods from the field of artificial intelligence are also possible.
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