How Data Warehousing Can Help Businesses

A space where a company or organization stores its data. Today, a Data Warehouse is a key resource for companies looking to move forward and improve their processes and results. In this article, we share some of the key aspects you need to know.

The concept of a Data Warehouse is closely linked to data science and Big Data. It refers to the place where professionals and companies store data and information that has previously been collected. All of this is kept in a highly secure environment to protect this sensitive material from potential cyberattacks that could cause significant damage to the owner of the data.

With this in mind, it is useful to start by explaining exactly what this term refers to: What is a Data Warehouse? What are its objectives? How is the system managed? 

What is a Data Warehouse and what does it involve?

The term itself highlights the essence of this resource: data storage. A Data Warehouse involves collecting, searching for and documenting data and information that may be relevant to companies, organizations, entities or other institutions. It is therefore an area closely linked to data analysis and Big Data, offering excellent professional opportunities for those looking to develop their careers in this field.

We could therefore say that a Data Warehouse consists of:

• Professionals specializing in data analytics — data scientists.  
• Systems for tracking all kinds of documents, information and data that are initially collected. 
• An analysis phase for the material that has been collected. 
• Applying the information that may be useful and leveraging the company's business intelligence to support new launches, improve existing products or achieve other business objectives.

How does a Data Warehouse work?

The way data storage works is essentially outlined in the previous section. A Data Warehouse contains storage systems and software where information is stored. This information may be structured by subject, source or department. The system architect determines the most appropriate approach in each case.

The data obtained may be:

1. Structured. 
2. Semi-structured. 
3. Unstructured. 

The architecture of a Data Warehouse system can generally be divided into the following levels:

1. Bottom tier, the database server where information from many different sources is stored.  
2. Middle tier, where data is transformed into more complex structures and relationships between the collected data points begin to emerge. 
3. Top tier: this contains the tools used for more advanced analysis, report queries and other data-related functions that provide valuable information for the business. 

A Data Warehouse consists of four main elements: metadata, which is data about your data and specifies aspects such as its source, how to access it and how it is structured; middleware, which provides access tools for retrieving stored information; ETL functions, which extract and transform data to facilitate analysis and loading; and a central database, which acts as the main data repository.

Differences between a Data Lake and a Data Warehouse 

Alongside the Data Warehouse, there is another concept that is sometimes confused with it: the Data Lake. What are the main differences between a Data Lake and a Data Warehouse? 

1. Data types and structure. Data Warehouses generally contain organized and structured information, while Data Lakes tend to contain less structured information because data is ingested directly from its source. 
2. Data Lakes primarily aim to improve cost efficiency and reduce the cost of data management. They do not need to conform to a specific structure, which simplifies the initial stages of the process. Data Warehouses, on the other hand, are more complex. 
3. Different user profiles. While Data Lakes are generally managed by data engineers and data scientists, Data Warehouses are used by data analysts and business analysts. These professionals require the information to have already been processed to a certain extent. 
4. Data engineers use Data Lakes to store incoming data. Data Warehouses are primarily configured for analysts to read information, although they may add information when necessary.
5. Size. Data Warehouses are generally smaller because they contain more selectively curated information. Data Lakes are significantly larger because they retain all data that could potentially become relevant to the company.

Advantages of a Data Warehouse

There are several advantages to having a Data Warehouse. Below are some of the benefits that best reflect the capabilities of this system:

1. It provides consistent information across a range of cross-functional activities.  
2. It helps integrate many different sources of data and information quickly and easily. 
3. It reduces response times when specific reports or analyses need to be prepared.  
4. Information obtained from multiple sources can be accessed from a single location. 
5. It offers significant capacity for storing historical data, which is particularly useful for professionals who need to carry out retrospective studies and comparisons across different periods. 

Examples of Data Warehouses

There are many examples of companies using Data Warehouses in their day-to-day operations. One example is the retail sector, where companies often store distribution and marketing data to understand where their products are located. The stored information also helps them improve services, track promotions and better understand customers' purchasing behavior.

Another interesting example can be found in insurance and banking. In these industries, a single figure can represent millions in profits or losses, which is why both Big Data and specific tools such as Data Warehouses are essential in the financial sector and the wider economy.

Finally, to provide a broader picture of the many applications of data storage, it is important to mention healthcare. In order to provide the best treatments and effective support, predictive reports are essential for determining the most appropriate solution for each individual patient.

Both this approach and other methods closely related to advances in data management and strategic data storage for leveraging business intelligence are covered in several programs offered by EAE Business School. These include the Master in Big Data & Analytics and the Global Master in Business Analytics and Data Strategy. Students interested in these programs can contact the academic team for further information.