There is no possible way that business intelligence would come to life without the data warehouse. At least not the business intelligence as we know it today.
In its essence, business intelligence is the capability to answer sophisticated questions about the data and use them to make informed business decisions. And to do this properly, you need a data warehouse, which does not only provide a safe way to centralize and store all data but it also gives a quick way to find the answers a user needs when they need them.
After all, when you consider that various experts estimate that humanity will have produced 175 zettabytes of data by 2025, this should be quite an interesting topic.
Keep reading to learn more about the role of data warehouses in business intelligence.
Where does all of the data go?
Most of the data humans produce goes in the data warehouses. Businesses use data warehouses to handle transactions, get a better understanding of their data, and keep all of it organized properly.
Long story short, data warehouses compile huge amounts of information and make it usable for organizations of all sizes and types.
In turn, this makes them very important for business intelligence systems. So, having a better understanding of how these warehouses work means having a better understanding of how one can maximize the potential of business intelligence.
A data warehouse is a data management system designed to store large amounts of data, which is later used in processing and analysis.
For instance, one can think of a data warehouse as a large warehouse where trucks unload their data. These trucks are the sources of data.
Once that is done, the data is then sorted into rows and rows of properly organized shelves that make it easy to find exactly what a user might be looking for later.
The groundbreaking feature of data warehouses, according to many experts, is the ability to store integrated granular historical data.
To make this piece of information easier to understand, look at it like this. A data warehouse excels at storing data that is:
- Integrated – combining data from many databases and data sources
- Granular – the data is highly detailed and can be used in many ways
- Historical – it can host a continuous record of data over a period of many years
Moreover, it is possible to store the data in three different ways: on-premise data warehouses, cloud data warehouses, and hybrid data warehouses.
On-premise data warehouses run on physical servers that a company owns and manages.
Cloud data warehouses are 100% online. The space on servers is paid for and a third party manages the servers.
Hybrid data warehouses are a combination of both on-premise and cloud warehouses. Businesses that make the transition to the cloud use this option for a certain period of time.
Finally, it is worth noting that data warehouses use a specific approach to process data called online analytical processing (OLAP), which is specifically designed for complex queries.
Look at it this way. A user goes to their data warehouse to ask a question about the relationship between one set of data and another. However, they shouldn’t roam the warehouse alone with no sense of direction regarding the location of the desired piece of information.
OLAP is a method of organizing and moving among the rows and rows of shelves to quickly find the needed information. This is great for business intelligence since the questions users ask about the data are rarely simple. Data warehouses use OLAP and OLAP makes finding answers to these complex questions very easy.
As a result, they’ve become a cornerstone for many successful business intelligence systems.
Useful data warehouse tools
To build a data warehouse, you need ETL to move data into that storage. To do this, the first step is to extract data from a range of different sources.
Then you should apply transformations to get everything into a user-friendly format. Finally, a user loads the data into a data warehouse.
To accomplish this process, one can turn to the Python programming language. The community around Python has created a wide array of Python ETL tools which give a user a lot of control over the process.
Let’s go through various categories and see what tools may be of great use for users once they start building data warehouses:
- Workflow management systems (WMS)
- Data processing tools like Spark and petl
- ETL frameworks such as Bonobo and Mara
- Other tools, including ETLAlchemy, Riko, and odo
Data warehouses are the foundation of data storage and that is why they matter in business intelligence. Business intelligence relies on complex queries and comparing numerous sets of data to inform everything from everyday decisions to organization-wide shifts in focus.
To make use of this, business intelligence is made of three overarching activities:
- Data wrangling
- Data storage
- Data analysis
Data wrangling is usually facilitated by extract, transform, load (ETL) technologies, and data analysis is done using business intelligence tools.
The link that holds this process together are data warehouses, which serve as the facilitator of data storage using OLAP.
How do data warehouses work?
Data warehouses are moderately complex systems but can be seen as encompassing three core aspects: storage, software, and labor. When making the decision to implement a data warehouse, a user needs to take into account the investment required for all three.
Within each storage, data is organized into tables and columns and within each column, you can define a description of the data, such as integer, data field, or string. Tables can be organized inside schemas, which can be seen as folders. When data is ingested, it is stored in various tables described by the schema and query tools use the schema to determine which data tables to access and analyze.
It is also worth noting that businesses use a combination of a database, a data lake, and a data warehouse to store and analyze data. As the data volume increases, it may become a bit difficult to store and manage everything, and this practice will make it convenient.
Hopefully, you now have a better understanding of what data warehouses are and what their roles are when it comes to business intelligence. Now you have a better grasp of this topic and know what you can get if you implement data warehouses with business intelligence properly.