Data Warehouse: Definition, Types & Characteristics

What is Data Warehouse?

A data warehouse is a crucial concept in the world of information technology and business analysis.

Have you ever wondered how large companies can efficiently manage their data and generate profound business insights? This is one of the crucial roles of a data warehouse.

A data warehouse is a centralized data storage system specifically designed to collect, store, and manage data from various sources.

They are also used in decision-making to achieve business analysis goals.

In general, the data collected in a data warehouse has been processed and transformed into an easily accessible format.

Here are some definitions of it by experts:

• According to Inmon: It is a collection of data that serves to support managerial decisions, particularly those oriented toward subjects (subject-oriented).
• According to Labe and Potineni: It is a database utilized in all business activities to help understand and improve performance.
• According to Feri and Dominicus: It is a collection of data in a large-scale storage that is extracted from various sources and processed into a multi-dimensional storage format.

Data Warehouse Concepts

Now that you've grasped the fundamental notion of what is data warehouse, then delving into the intricacies of its underlying basic concepts becomes paramount. 

These foundational principles serve as pivotal milestones in the intricate process of gathering, constructing, and overseeing data to get a robust and effective data management system.

Let's explore in detail the four fundamental data warehouse concepts below.

1. Load Manager

The load manager (front-end component) is responsible for all operations related to the process of loading data. Its tasks include data extraction, data preparation through transformation, and storing the prepared data.

2. Warehouse Manager

As the name suggests, the warehouse manager is responsible for managing data. Its operations include data analysis to ensure consistency, index and view creation, denormalization and data aggregation, data transformation, data merging from various sources, and data archiving and storage.

3. Query Manager

The query manager is one of the backend components responsible for all operations related to user request management. This process includes scheduling the execution of queries submitted by users and performing query operations on the appropriate tables.

4. End-User Access Tools

In this concept, there are five End-User Access tools that you need to understand:

• Data Reporting: Tools for creating reports based on data.
• Query Tools: Tools for posing questions and querying data.
• Application Development: Tools for developing applications based on the data.
• Executive Information System (EIS): Tools that assist executives in decision-making by presenting essential information concisely.
• Online Analytical Processing (OLAP) and Data Mining: Tools for in-depth data analysis to discover patterns within the data.

The Advantage of A Data Warehouse

A data warehouse plays a pivotal role within a company's information technology ecosystem, serving a crucial function in aiding organizations in their decision-making processes.

 Let's delve into a comprehensive discussion about its functions in a business context:

1. Data Consolidation

One of the primary functions is the aggregation of data from various dispersed sources within an organization. 

This process encompasses operational data, data from different applications, and even external data sources. 

By integrating all this data into one centralized and consistent source, you can create a unified data repository.

2. ETL Processing (Extraction, Transformation, Loading)

This warehouse serves as a platform for ETL processes, which include data extraction from its sources, subsequent transformation into a suitable format, and loading. In essence, this ensures that the data stored within the warehouse is prepared for analysis.

3. Accurate Business Analysis

Decision-makers gain access to data from various well-integrated sources thereby mitigating the risk of making decisions based on inaccurate data. 

With more accurate analysis, companies can even plan strategies and actions with greater confidence.

4. Enhanced Data Security

Data warehouses provide a high level of security by consolidating data in one location. This simplifies the implementation of multi-level security systems to safeguard data from misuse.

In addition, companies can grant limited data access based on employee roles and responsibilities, minimizing data breach risks.

5. Support for Business Analysis

This warehouse of data is the primary platform for business analysis, offering various tools for conducting various types of analysis, including OLAP (Online Analytical Processing) and data mining.

6. Fast Data Query Access

Data warehouses are designed to provide fast and efficient data access. With indexed data, users can retrieve information swiftly without burdening operational data sources.

7. Decision Support

One of the core functions is to support decision-making. It means that the data available can be used to make more informed strategic and operational decisions.

8. Optimizing ROI (Return on Investment):

A significant benefit is its role in helping companies optimize their Return-On-Investment (ROI) efforts. 

In the business world, ROI is a key factor in evaluating the performance and success of an investment. ROI is the ratio of net profit gained from an investment to the investment cost. 

In this context, it functions by gathering, managing, and analyzing data from various sources, including operational and historical data. 

With organized and integrated data at their disposal, companies can calculate ROI for various initiatives and investments more accurately.

Data Warehouse Characteristics

As a centralized collection of data, this platform possesses several distinct characteristics, such as:

1. Intergrated

All data within this platform originates from various sources, necessitating integration into a standardized measurement unit. 

Furthermore, the data stored must first be transformed into a simple and universally acceptable format within the data repository. 

This allows data from diverse sources such as mainframes, relational databases, and flat files to be combined to create consistency in decision-making.

2. Subject-Oriented

One of the characteristics is its subject-oriented nature. This means that a warehouse of data always focuses on specific topics or subjects rather than the operational processes of an organization. 

For example, it can provide information on topics such as sales, distribution, or marketing, enabling users to conduct in-depth analyses when making decisions.

3. Non-Volatile

This characteristic implies that data once entered will not be deleted when new data is added. Instead, data can only be read and updated periodically. This allows for deep historical data analysis and understanding of developments over time.

4. Time-Variant

This warehouse of data has a broad time horizon for storing historical data. Each primary key within the warehouse typically has a time element, either explicitly or implicitly, allowing users to track changes over time, and facilitating the analysis of historical trends and patterns.

Components of Data Warehouse

As a fully integrated data storage center, there are several components of a data warehouse that you should be aware of, including:

• Warehouse: The primary component that serves as the repository for databases and is processed transactionally.
• Warehouse Management: Responsible for overseeing data operations for analysis and efficient archiving. 
• AccessTools: Users utilize access tools such as OLAP (online analytical processing), query and reporting tools, data mining, and application development tools.
• Metadata: Summarizes information related to the content based on structure and location. 
• ETL tools: Collect and move raw data from various sources into a single database, as well as store the data.

Types of Data Warehouse

A data warehouse serves as the foundational cornerstone for a company's data analysis. In this regard, they are divided into three main types:

1. Enterprise Data Warehouse

It is a central repository that integrates data from various functional areas within an organization seamlessly. 

Its goal is to store data from various sources and organize it for easy access by the entire organization.

This type typically comes equipped with automated procedures for data extraction, transformation, and analysis. 

2. Operational Data Store (ODS)

This type, also known as the Operational Decision Support System, is used when Online Transaction Processing (OLTP) systems are unable to meet the needs of the company. 

ODS can be updated in real-time, mitigating data redundancy, and is often used to store information such as employee data.

3. Data Mart

A data mart is a component designed specifically for particular business purposes, such as profitability analysis or sales analysis. In general, data marts can be categorized into three types:

• Dependent: They extract data from operational sources, external sources, or both to create an integrated data center.
• Independent: Typically not dependent on the central warehouse or other data marts and is used by smaller groups within the company.
• Hybrid: Hybrid data marts are used when input from different sources is part of the warehouse of data.