What is a data warehouse?

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This blog has been expertly reviewed by Andrea Rosales, Lead Data Scientist at Colibri Digital. 

Every industry is creating, storing, and analysing more data than ever. But how we use that information makes all the difference. To get the most impactful insights, many organisations use a data warehouse.  

At its most basic, a data warehouse acts as secure storage for vast amounts of information. What sets it apart from other storage solutions is its role in business intelligence (BI). With the data gold rush hotting up, it’s now essential for businesses to get data integration right to maintain a competitive edge. 

The importance of data warehousing is highlighted by its rapid expansion. As an industry, data warehouse services increased in value by 14% in 2023 alone. This surge reflects the increasing reliance on data analytics across various sectors and its wider adoption. 

So, what exactly is a data warehouse, and should you be using one? In this blog, we’ll investigate how data warehousing impacts the decision-making process, how it’s set up and how to use one. Finally, we’ll see how it could help transform your business's approach to data management and analysis. 

How data warehousing works 

Andrea Rosales, Lead Data Scientist at Colibri Digital, said: “Data warehouses have become an invaluable asset for modern businesses. They’re much more than a standard database. Instead, they collect many disparate data sources together, acting as a central repository for the enormous amount of business data modern operational systems create. The beauty of this is that companies can then use the information within their data platform for analytical processing.” 

There are a few key steps that data takes on its way to warehouse storage:  

  1. The process starts by gathering large volumes of data from various sources. This raw data is not always ready for analysis right away.  
  2. Next, the data is cleaned. Data engineers check it for errors, inconsistencies and repetition. This process ensures the data's quality and reliability which is crucial for trustworthy decision-making. 
  3. Once clean, the data is transformed into a usable format. This step is key to making the information compatible with the data warehouse's structure and requirements.  
  4. Now, the data is ready for its final destination – the data warehouse. In the warehouse, it's stored ready to be used by decision-makers, dashboards and business applications.  

What is a data warehouse used for? 

A data warehouse’s primary purpose is to facilitate reporting and analysis of historical data and trends across an organisation. By comparing and analysing data gathered from multiple, varied sources, businesses can gain a clearer understanding of their performance over time. 

As the above steps show, ensuring the data is of the highest quality and stored correctly is vital. Once in place, the data warehouse can help with things like:  

  • BI decision support 
  • Predictive analytics 
  • Forecasting 
  • General data analysis and reporting. 

By its nature, a data warehouse is tailored specifically for querying and analysing historical data. It allows you to sift through years of big data, drawing connections and insights that can positively impact your decision-making. 

Where is a data warehouse stored? 

Data warehouses can be located in different environments. Traditionally, a company might use on-premise infrastructure to store its data sets., but nowadays, it’s increasingly common for companies to avoid upfront costs and maintenance tasks by using cloud technologies.  

Popular cloud data warehousing providers include: 

Each provider offers unique benefits that may fit different business needs. For more information, speak to a digital transformation expert. 

How data warehouses compare to other data sources 

A data warehouse aggregates and stores data from various sources into a well-organised system that’s perfect for efficient querying and analysis. For businesses, it provides a source for BI and analytics. 

When it comes to data, you may hear many similar terms. Understanding the unique features of a data warehouse and how it compares to other data sources is key to using its full potential. For example, a data warehouse differs from a database, a data mart, and a data lake — all of which sound similar but serve their own purposes. 

Data warehouse vs a database 

A database is a transactional system focused on managing real-time data. A typical relational database is designed to keep information updated, structured, and secure. Businesses might use a single operational database or combine several into a data management system. 

While data warehouses primarily handle structured data, they can also store semi-structured and unstructured data. For example, some data warehouses offer support for data formats like JSON, and they may integrate with data lakes or other systems to handle unstructured data. 

Data warehouse vs a data mart 

Data warehouses are also often confused with data marts. However, it’s more accurate to think of a data mart as a niche version of a data warehouse. A data mart gathers information from limited sources, concentrating on a single subject area.  

This specialisation makes data marts quicker and more straightforward to use. Businesses often use them to provide narrower insights for specific departments, for example. 

Data warehouse vs a data lake 

Finally, data lakes also differ from data warehouses. In many ways, they are a different approach to data management altogether. A data lake is a vast pool of raw, unstructured or semi-structured data stored in its original format.  

A lake is designed to handle all types of data. It might be structured, semi-structured, or entirely without a defined schema or purpose. This makes data lakes ideal for storing massive amounts of data regardless of format, offering flexibility in data storage and access. 

What are the benefits of a data warehouse? 

A data warehouse brings many advantages to businesses that are optimising data management and analysis:  

  • Brings data together, avoiding the need to connect to numerous data stores. 
  • Offers a comprehensive view of historical trends for strategic planning and forecasting.  
  • Helps ensure data quality, consistency, and accuracy.  
  • Brings uniformity in naming conventions, codes, languages, and anything else the company stores. 

What are the challenges of a data warehouse? 

Here are some things to consider: 

  • Data warehouses are designed for structured data and can struggle with unstructured data like images, text, and IoT information. 
  • Data warehouses typically use SQL, not other, more flexible languages that app developers, data scientists, or machine learning models could use. 
  • Data warehouses can use proprietary formats, leading to incompatibilities and vendor lock-in.  

The key to a successful data warehouse deployment is designing it to serve your intended business functions. 

Data warehouse use cases 

Data warehouses are pivotal for harnessing the power of information. They take records and help end users transform them into actionable insights.  

But what functions can they be used for? The following are examples that illustrate the versatility of data warehouses in various sectors: 

  • Data warehouses are the heart of BI. They consolidate data from multiple sources, enabling companies to analyse information and make data-driven decisions. 
  • By integrating data from various points, data warehouses offer a unified view of customer interactions. This helps tailor marketing strategies and improve customer service. 
  • Data warehouses ease the tracking and analysis of supply chains. Companies can use them to optimise inventory levels and enhance efficiency. 
  • Data insights are helpful in financial analysis, budgeting and forecasting. By aggregating financial data, data warehouses spot trends and anomalies for ongoing quality control. 
  • By analysing historical data, data warehouses help predict future risks and plan strategies to counter them. This could be used for proactive maintenance or new business functions. 

Data warehouses are an essential tool in each of these use cases. When well-implemented and used with quality data, they enable any organisation to make the most of their information. 

How Nasstar and Colibri Digital can help 

Data warehouses open up the world of business intelligence. They provide a robust single source of truth for any organisation’s data. From customer and sales to more general data, a warehouse collects this data and keeps it secure and ready for analysis.  

At Colibri Digital (part of the Nasstar Group), we understand the complexities of data management. Our expert team can help you build platforms that centralise your data into a structured, streamlined, reliable repository. This way, your information is not just stored but also governed and secured — particularly in cloud computing environments.  

Speak to a specialist to learn more about unlocking your data’s full potential. 

Frequently Asked Questions (FAQs) 

What is a data warehouse in simple terms? 

A data warehouse is a storage solution where businesses can save significant amounts of information from various sources. An enterprise data warehouse is designed to extract, transform and analyse operational data, enabling informed business decisions. 

What is the difference between a data centre and a data warehouse? 

A data centre is a physical facility housing computing resources like servers and networking equipment. In contrast, a data warehouse stores and manages large amounts of structured data for analysis and reporting. 

What is the difference between data storage and a data warehouse? 

Data storage means any method or device used to keep data. This could be a local hard drive or cloud storage, for instance. A data warehouse stores large volumes of structured data for analysis and decision-making, often using data from multiple data storage sources. 

How can a data warehouse be improved? 

Improving a data warehouse architecture involves data cleaning, using analytics tools for deeper insights and optimising data storage. This helps improve performance and helps business analysts and data scientists handle new data or larger volumes. 

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