Data warehousing is undoubtedly essential for a company that wants to maximize its results and is now gaining an analytical advantage. If you have studied data analytics, you have probably heard about data warehouses and the benefits they can bring to your business.
If you’re still not convinced, you need to learn more about data warehouses. So we’ve outlined the key concepts and shown you how they can impact your business performance.
Data Warehouse: The Single Reliable Source Of Information
The most basic concept of a data warehouse is one of the most important from a business perspective. It is a special type of database optimized for online analytical processing (OLAP).
DWH is created by pooling all of a company’s data sources for analytical purposes.
This means that a properly implemented data warehouse brings all data sources together in one place. This will allow marketing, sales and production teams to work with the same data, as the implemented data warehouse becomes a single source of reliable information for the company’s business decisions and forecasts.
Having a data warehouse as the single source of reliable information is a concept that ensures that everyone in the company makes business decisions based on the same data.
After all, there’s nothing more frustrating than when the numbers provided by the marketing department don’t match the numbers in the hands of the sales department, right?
Internal data is only valuable for decision-making if it is credible to all stakeholders in the company. This is why it is so important to understand the value of aggregating data in a data warehouse.
The Modern Data Warehouse: 8 Concepts To Ensure Quality Performance
A data warehouse is undoubtedly essential for a company that wants to multiply its results and now has an analytical edge.
The functions and applications that define data warehouses are numerous and are linked to the architecture, the type of data stored, the way it is used, and the way data is extracted, loaded and transformed.
If all these steps are properly implemented and controlled, excellent results can be achieved.
Let us look at some of the following basic and very advanced concepts that will certainly help you to better understand the data warehouse.
1. Integration
A data warehouse integrates data from different business sources. For example, source A and source B may identify product X in different ways, but there is only one way to identify the product in the data warehouse.
2. Facts And Actions
In a data warehouse, an action can be a property of a computation and a set of actions can be an event table. In practice, for example, a sales order table and other production orders are event tables.
3. Time Dependence
Historical data is stored in the data warehouse to explain trends in the data over time. For example, even if the price of a product changes, the data warehouse stores all historical changes in the unit price of that product.
4. Dimensions
These are the characteristics of the data warehouse that allow events and activities to be classified, which in turn allows activities to be analyzed and reported. The dimensions can be, for example, the company’s customers, data, suppliers or products.
5. Data Mart
A data mart is a subset of a data warehouse, for example, a data warehouse that focuses on a specific business area or segment. For example, there may be one data warehouse for production, one for sales, one for maintenance, and one for quality.
6. ETL
ETL is a variation of the ETL (Extract, Load, Transform) process that extracts raw data from various business sources and loads it into a data warehouse. The raw data is then transformed as required for use in analytical processes.
7. ELT
The ELT approach allows for faster implementation than the ETL process, even if the data is disordered after the change. The transformation is performed after the upload operation, thus avoiding the migration delay that occurs in this process. The ELT separates the conversion and upload phases, ensuring that a coding error (or other error in the conversion phase) does not slow down the migration effort. In addition, by leveraging the processing power and size of the data store, ELT avoids server scaling issues, enabling large-scale transformation (or scalable data processing). ELT also interoperates with cloud storage solutions and supports structured, unstructured, semi-structured and raw data types.
8. Consistent Data
Once data is entered into the data warehouse, it does not change.
All of these concepts are used in the deployment of a modern data warehouse to automate business processes and achieve a single goal: to deliver high-quality results across the enterprise.
The data warehouse actually contributes to this by ensuring the integrity and quality of the data it contains.
High Quality Data = High Quality Results
A data warehouse can only be an asset to a company if the data it holds is reliable and of high quality. No one will generate ideas and make decisions based on unreliable information. At least, they shouldn’t.
All the data warehouse concepts we have discussed ensure that certain processes, such as data cleansing, data transformation and integration of different business areas, are done in one place.
In this way, the company’s data analysis provides important information for decision making in all areas.
Do you know how important investing in data warehouse consulting services is to the success of your business?
Do You Understand The Importance Of Investing In Data Warehouse Consulting To Help Your Business Grow?
A data warehouse consultant uses the best and most advanced data warehouse tools available on the market and has a team of professionals who are experts in the field.
Whether you are a data science expert or a beginner, one thing is for sure: data warehouses can take your business to the next level.
Would you like to know how to create a data warehouse to fuel your business growth and success?
Start by contacting ExistBi’s Data Warehouse Consultant and let them create a data-driven strategy that will help your business achieve irreversible growth.
