Although decision-making used to be associated with large companies, modern technological advances have made these tools more accessible to everyone, paving the way for their wider use. Today, they are available to all companies, regardless of their size: small, medium, or large. However, their management can be complex and demanding due to the volume and variety of data coming from multiple sources (CRM, ERP, websites, etc.). This is where a data warehouse comes to the rescue. A true pillar of business intelligence, this digital data warehouse offers companies a number of advantages, such as the ability to collect relevant data and conduct in-depth research to make effective decisions. In this article, you can learn more about why the data warehouse has become a critical step in the success of today's businesses.

Data Warehouse Definition

A centralized digital storage system, called a data warehouse, collects, consolidates, and organizes large amounts of data from internal and external sources. Its primary purpose is to facilitate data analysis and query, enabling users to make informed strategic decisions. The data warehouse is specifically designed to store current and historical data optimized for analytical queries and aggregation, as opposed to operational databases used for real-time operations. It is a fundamental tool for data analytics and business intelligence in organizations.

Different Types of Data Warehouses

A database management system is undoubtedly the best-known data warehouse. Although different types of DBMSs can be used, relational databases are the most common. Due to their properties and characteristics, they can be used for data warehousing and transaction management tasks. The data warehouse can also be connected to other software, such as those mentioned above. Obviously, this can affect cost, but it can also provide better management capabilities (an integrated feature is easier to use).

In General, There Are Four Types of Data Warehouses:

1.    Traditional DBMSs (database management systems). Not all, but most of them are relational. The vendors of most technologies are IBM, Microsoft, Oracle, and SAP.

2.    Some analytic DBMSs. This type of data warehouse is not a traditional DBMS. They are "extensible" to meet specific data warehouse workloads. Examples are columnar databases, such as those produced by SAP and HP.

3.    Application-based. These are designed to provide off-the-shelf data warehouse services. Typically, an analytical or relational database is pre-installed and customized, and hardware and software are mixed. The server's memory and storage capacity is sufficient. The vendor installs it, and customers only have to connect and activate it according to the vendor's requirements. These can be found in Teradata, IBM, and Oracle catalogs.

4.    Cloud-based data warehouses. SDBs that are accessible over the Internet but not installed on-premises. Thus, the cloud offers data storage as a service without the need to install hardware or an SDB on the customer's premises. IBM, Amazon, and Microsoft are among the experts in this field.

At least there are many different data warehouse vendors in the market. There are a number of specialized vendors in the industry offering database and cloud analytics services that may appeal to a particular segment of customers, although they are dominated by competitors working with standard DBMSs. There are also a number of hybrid solutions, such as those from Pivotal and Actian, which offer different features for storing and accessing structured and unstructured data.

Benefits of Data Warehousing

Aggregation of Information: to provide a holistic view of the entire business, a data warehouse collects all information from different sources (e.g., inventory, CRM, financial data, etc.) and brings it together in one place.

Data Quality: Data can contain errors, duplications, and inconsistencies. To ensure data quality, accuracy, and validity of the information processed, the data warehouse has data cleansing procedures in place.

Transformation for Analysis: for practical Analysis, the original data must be transformed. Data warehouses facilitate subsequent analysis operations by enabling data transformation with appropriate organization.

Optimize Data Processing: large amounts of data can be centrally stored in data warehouses. This speeds up access to information and simplifies data management, facilitating rapid response.

Advanced Analytics: data warehouses support complex queries and advanced analytics. They help identify trends and patterns and provide critical information for decision-making by allowing you to dig deeper into the data.

Checklists and Reports: data warehouses allow you to create dynamic checklists and reports. This facilitates the dissemination of information to a wide range of stakeholders across the organization, helping to accelerate insights and inform decisions.

Data Visualization: Data visualization is a key element for quickly interpreting complex information. Data warehouses contain tools for creating visual representations, such as tables and graphs, that facilitate intuitive data analysis.

Summary

Well-designed data warehouses generate high-quality data and respond directly to queries. They allow end users to reduce the amount of information and focus on a specific area. Data warehouses provide data in a consistent and ready-to-view format, making them critical to accelerating decision-making.

Data warehouses also provide customers with extensive data sets and analytical capabilities, enabling data-driven decision-making and superior insights across all business disciplines.