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What is a Data Warehouse? The Fundamentals and Benefits
That’s where data warehouses come in. Data warehouses help simplify the data management process, storing information collected from various sources all in one place.
In this article, we’ll explore the journey from raw data to data warehouses. We’ll take a look at what exactly a data warehouse is, why they’re useful, and how you can apply them to your business.
A data warehouse is a centralised storage system that aggregates data from multiple different sources into one consistent data store. It is a common data management technique used by businesses to streamline data analysis and reporting processes.
By consolidating data from various operational systems, such as transactional databases, customer data, and external sources, a data warehouse provides a unified view of the organisation's information.
Each data warehouse is purpose built to assist businesses with reporting, forecasting, predictive analytics and data visualisation. As they can store large amounts of data, they are ideal for keeping historical data which can be analysed and compared with current data to obtain valuable insights that can help with key decision making.
A notable advantage of data warehouses is their flexibility. They don’t impose any specific requirements for tools or hardware to store data. This flexible approach allows them to be tailored specifically to the needs of a business making them a truly bespoke data storage system.
A data warehouse works by extracting data from various operational systems. Once the data is extracted, it undergoes a transformation process to ensure consistency, quality, and compatibility. This involves cleaning and filtering the data, resolving inconsistencies or errors and standardising formats. Transformations may also include data enrichment, integration of external data, and the creation of derived data sets.
After transformation, the data is integrated within the data warehouse, meaning it is combined and consolidated into a unified format, and sorted into different tables depending on the data type and layout.
Data within the data warehouse can be analysed to help businesses find valuable insights that can help inform decision-making. This gives them the power to stay ahead of their competitors by predicting trends and market changes, knowing what products to focus on, shaping pricing strategies, and helping achieve customer satisfaction.
The design or architecture of a data warehouse is made up of tiers. It can be single tier, two-tier or three-tier architecture:
- A single-layer structure used to keep data space to a minimum, however it’s rarely ever used.
- Includes a staging area for all data sources before the data warehouse layer. This ensures all data is cleansed and formatted correctly before it is loaded directly into the data warehouse.
- This approach is most commonly used and consists of three tiers.
- The is the database for the warehouse that stores the cleansed and formatted data.
- The is an application layer which uses an Online Analytical Processing Server (OLAP) to perform multidimensional analysis on data.
- The is the front-end that the user accesses. Data visualisation tools are used to present analysis results.
There are a range of benefits to using a data warehouse. Benefits include:
- A unified and consistent view of data allows organisations to make informed decisions. Analysts and key decision makers can access comprehensive and reliable information, perform complex analysis, identify patterns and gain insights that drive strategic and operational decisions.
- Data warehouses keep data from across the business all in one place. This makes it much easier and cost-effective to manage data and carry out the day-to-day activities that rely on it.
- Data warehouses can hold vast amounts of data, which allows for historical data analysis. This can help businesses find valuable insights and trends that they may otherwise have missed.
- Data warehouses go through a rigorous data integration process, which helps ensure data consistency, accuracy and quality across different sources. This means organisations can have confidence in the integrity of the information they are using.
- They’re designed to handle large volumes of data and support complex queries efficiently and can be quickly accessed once data is loaded in. With optimised data structures and indexing techniques, they can deliver fast query response times, even when dealing with complex analytical operations on large data sources. They separate analytics processing from transactional databases which improves performance of both systems.
- A data warehouse is scalable and can easily be expanded to increase the amount of data it holds. As a business grows and their data storage needs increase, this scalability helps for future growth.
- Can be used by people of different skill levels as the data is structured. This empowers users at all levels of an organisation to access the information they need to monitor performance, identify opportunities, and optimise operations.
Things to consider when designing a data warehouse
When designing a data warehouse, organisations should understand their own business needs and what they want to use it for, however this sometimes won’t be made clear until a specific need emerges. Key things to remember when designing a data warehouse are:
- Data in a data warehouse must be structured meaning it needs to be processed before it can be loaded into it. This requires a business to commit time and resources to successfully complete.
- There is usually a constant influx of new data which needs to be added to a data warehouse to ensure it’s being utilised to its full potential. This takes time but ensures data is current and complete.
- Certain data types are not suitable for a data warehouse such as social data, video and audio content and documents. You’ll need alternative storage for data like this.
- Data warehouses are purpose built and need to be designed first for specific requirements. If these needs change later on, this may require the data warehouse to be modified.
A data warehouse needs to meet the requirements of the end user so they can get the most use out of it. Typically, they’ll be interested in data analysis and viewing data in aggregate.
When designing a data warehouse, anticipating the customer’s needs is important if they don’t fully know themselves what they’ll use it for. Having room for expansion is also a must to keep up with a business’s growth and need for more data storage.
What types of data are stored in data warehouses?
Certain types of data are well suited for storage in a data warehouse, such as:
- financial and transactional data
- operational data
- customer relationship data
- enterprise resource planning data
However, it’s quite common for some data not to be stored in a data warehouse due to pricing constraints in terms of volume and the resources required from database administrators. This usually applies to unstructured data, such as:
- social media data
- documents
- sensor data
This kind of data is typically managed by alternative methods, such as data lakes.
Data warehouse vs. data lake
Data warehouses and data lakes both store data, but they have different capabilities. Most organisations will use both for storing their data. Here’s a simple breakdown showing the differences between them:
| Function | Data Warehouse | Data Lake |
|---|---|---|
| 1 - Data Storage | The data is structured and has been cleaned and processed. It's redy for strategic analysis using preset business needs. | The data is raw and unstructured. It can be stored to use now or at a later data. |
| 2 - Users | Business-end users and managers wanting to quickly find insights related to KPIs as data is structured to answer preset questions. | Data scientists and engineers who prefer to study raw data to find new business insights. |
| 3 - Analysis | Ideal for data visualisations, business intelligence, and data analytics. | Ideal for predictive analytics, machine learning, data visualisations, business intelligence, and big data analytics. |
| 4 - Schema | Schema is defined before storage of data, meaning to takes longer to process data. However, once added, it's ready to be analysed. | Schema is defined following the storage of data, making it a faster process to capture and store it, but it can't be analysed at this stage. |
| 5 - Processing | ETL (Extract, Transform, Load). Data is extracted from the source, scrubbed, then structured to be used for analysis. | ETL (Extract, Transform, Load). Data is extracted from the source and stored. It can be structured when needed. |
| 6 - Cost | Data warehouses cost more and need more time to manage. | Lower cost and required less time to manage. |
Both data warehouses and data lakes are valuable storage solutions for business. Data warehouses are easier for anyone in the business to use as the data is structured to answer preset questions. Adapting the data warehouse to accommodate new questions or emerging data can be challenging, and this is where a data lake has its advantages. To enhance the lifecycle of their data, organisations should look into using a data lake alongside their data warehouse.
Schemas and data warehouses
Data warehouses use schemas, which are essentially a blueprint for how the data should be organised. The schemas help data warehouses to quickly and efficiently query large data sets. There are 3 basic models:
- Data tables are one-dimensional, where each table contains data describing a single attribute. This includes things like when the event occurred, location data such as country or city, and also range dimensions such as number of units sold and value.
- A more complex schema, however it’s easier to maintain and uses less storage space than the others. Data tables are multidimensional and are subdivided into additional tables. An example of how this works would be a table on transactions including a location attribute, which is connected to another table that contains additional information like city, street, building number and postcode.
- : Is essentially a cross between star and snowflake schema. It can contain both one and multidimensional data tables.
These schemas largely vary by how many dimension tables they contain. These distinctions directly impact the storage and querying efficiency of the data.
Data warehouse uses by sector
Data warehouses are used by many different industries. Here are just a few examples of how some sectors are using them:
| Sector | Data Warehouse uses |
|---|---|
| Support market research, performance and operational analysis. | |
| Healthcare | Helps with things like forecasting and strategy, generating patient reports, and sharing data with insurance companies. |
| Financial Services | analyse data patterns, customer trends and can also monitor movements in different markets. |
| Supports product promotions as well as decisions around sales and distribution. | |
| Retail | Can be used to track products, customer buying patterns, promotions and assist with pricing. |
| Travel and Leisure | Can be used by airlines for flight crew assignment, reviewing profitability of services and any promotions they may offer. |
| Helps with intelligence gathering. Government departments use them to maintain thing like tax records to ensure accuracy. |
Do I need a data warehouse?
Data warehouses provide a robust and scalable solution for managing and analysing data. You should consider a data warehouse if you:
- Have a large volume of data
- Have data coming in different formats and structures
- Need to combine data from different sources to analyse to find insights
- Would prefer to view multiple data sources in one central location that's easily accessible by your team
- Are looking to analyse historical data to identify trends and patterns
- Want to separate your analytical data from your transactional data to avoid risk of disruption when working in a live application database
- Would like the ability to use advanced reporting and data visualisation
- Need to enforce data governance policies
- Anticipate data growth and the need for scalability
A data warehouse's ability to help with all of the above makes them an extremely powerful tool for storing data, finding key insights, and supporting business related decisions. If you’re considering getting a data warehouse, you can contact our experienced team who will find out what your current business challenges are and offer you the best solutions available.
