Data Warehouse in Business Intelligence (BIDW): All you need to know

Data Warehouse in Business Intelligence (BIDW): All you need to know

Table of Contents

For strategic planning and advancement, modern businesses now rely on detailed insights, exact and data-driven decision-making. So, Data Warehousing (DWH), and Business Intelligence (BI) are becoming increasingly significant. Furthermore, BI relies on techniques such as data warehousing to give accurate, fast, and dependable intelligence. Understanding the intimate relationship of Data Warehouse In Business Intelligence is critical for properly appreciating how BI architecture delivers the greatest value.

What is a Data Warehouse (DWH)?

A Data Warehouse is a type of data management system that stores massive amounts of data for future processing and analysis.

Consider it a big warehouse where trucks (i.e., source data) unload their cargo. That information is then categorized. After sorting the data, it is neatly put on rows and rows of shelves, making it easy to find the information you need later.

According to DW 2.0: The Architecture for the Next Generation of Data Warehousing, the ability to store “integrated granular historical data” was the most significant innovation data warehouses introduced at its beginning.

This means that data warehouses are particularly good at storing data that is:

  • Integrated: They combine data from numerous databases and information sources.
  • Granular: The data they save is extremely comprehensive and can be utilized in a variety of ways.
  • Historical: They can keep a continuous record of data for years and years.

This data can be stored in three ways: on-premise data warehouses, cloud data warehouses, and hybrid data warehouses.

On-premise data warehouses run on actual servers owned and managed by your firm.

Cloud data warehouses are completely online, and you pay for storage space on servers managed by another organization, such as Amazon Redshift.

Hybrid data warehouses are a mix of on-premise and cloud, and this option is used by enterprises that are gradually transitioning to the cloud.

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Cloud data Warehouse is the future of data storage.

Data Warehouse In Business Intelligence use a unique technique of data processing called Online Analytical Processing (OLAP), which is specifically built for sophisticated queries, because all of the data is stored in one location.

If you go to your data warehouse and ask a query about the relationship between one set of data and another, OLAP is a means of arranging and moving around the rows and rows of shelves to rapidly find that information.

This is useful for business intelligence since the questions you ask about your data to make decisions are rarely straightforward. Because data warehouses use OLAP, they make it very easy to find answers to these difficult problems. As a result, they’ve served as the cornerstone for numerous successful Business Intelligence systems

The relationship between a Data Warehouse And Business Intelligence solutions (BIDW)

Despite the fact that they serve the same purpose, Data Warehouse And Business Intelligence are separate terms that fall under the BI umbrella and are referred to combined as BIDW. Data Warehouse is a component of both data warehouses and Business Intelligence. However, the primary goals of Business Intelligence are data gathering, technology, and analysis. A Data Warehouse, on the other hand, primarily organizes and stores such data to assist Business Intelligence tasks. A data warehouse’s upkeep and deployment are so critical to Business intelligence.

Without the data warehouse, business intelligence as we know it today would not be possible.

Business Intelligence is defined as the capacity to answer complicated questions about your data and utilize the answers to make informed business decisions. To achieve this effectively, you’ll need a Data Warehouse In Business Intelligence, which not only provides a secure mechanism to organize and store all of your data but also a way to rapidly discover the answers you need when you need them.

And this is a significant role. It is anticipated that humanity will have produced 175 zettabytes of data by 2025. That equates to 175,000,000,000 terabytes.

Where does all of this data? Well, the majority of it is stored in data warehouses.

Companies utilize Data Warehouses to manage transactions, comprehend their data, and keep it all organized. In a nutshell, data warehouses make vast amounts of data more accessible to enterprises of all sizes and sorts.

As a result, they have become a critical component of data pipelines and Business Intelligence systems all around the world. Understanding how Data Warehouse In Business Intelligence works can also help you realize the full potential of business intelligence (it’s not as difficult as it appears).

The differences between Data Warehousing and Business Intelligence

There are important distinctions between Data Warehousing And Business Intelligence. However, before we get into the differences, it’s vital to recognize that they both exist in the same environment and are equally critical for an overall Business Intelligence Strategy. 

Some of the key distinctions between the two are listed below:


The basic goal of Business Intelligence is to evaluate data and offer actionable insights to decision-makers. In this context, a Data Warehouse is a centralized repository for collecting, analyzing, and storing data from numerous sources.


Through forecasting and predictive analytics, Business Intelligence aims to assist business users in making educated and data-backed business decisions. A data warehouse, on the other hand, is designed to store structured data in a central area so that Business Intelligence users may access a comprehensive view of the organization’s data.


Dashboards, reports, data graphics, charts, and graphs presenting insights and trends are examples of Business Intelligence output. Such outcomes enable business users to make sense of complex data. A Data Warehouse In Business Intelligence output is made up of data records kept in fact and dimension tables of data models.


Business Intelligence users are typically C-level executives, managers, or data analysts who want to do rapid data analysis for enhanced decision-making. On the other hand, data architects and engineers are typically in charge of managing and maintaining the Data Warehouse In Business Intelligence and giving business users access to data that is ready for analysis.


SAP, Power BI, Tableau, and Qlik are some popular Business Intelligence tools. On the other hand, well-known data warehouse providers include Azure Synapse, Google BigQuery, and Amazon Redshift.

How do Data Warehouses function?

Data Warehouses are fairly sophisticated systems, but they may be divided into three major components: storage, software, and manpower. When deciding whether to create a data warehouse, you must consider the expenditure required for all three.

Data Warehouse in Business Intelligence (BIDW): All you need to know

Storage is a straightforward option. As previously said, you can host your data warehouse on-premises, in the cloud, or in a hybrid environment. Some believe that on-premises hosting is on its way out.

Besides, because you are renting space on another server, cloud hosting is substantially less expensive and more flexible. You don’t need to do maintenance, you can scale up and down as needed, and new features are added every year. The ideal solution for enterprises shifting from on-premises to cloud hosting is hybrid hosting, which bridges the gap between these two techniques.

To get data into your Data Warehouse In Business Intelligence, you must utilize a form of software known as ETL software. The process of extract, transform, and load (ETL) involves extracting data, preparing it for use, and then loading it into a data warehouse.

Nowadays, we suggest and see many more businesses employing an ETL option known as extract, load, and transform. Companies will frequently extract data from source data, load it into a data lake, and then transform the data using data warehouses. ETL and ELT are both aided by tools such as Panoply.io and Stitch.

Of course, Data Warehouses do not run on their own. Because a Data Warehouse is a “full-fledged…architecture” that requires expertise to set up and administer, labor is an important aspect of keeping it going.

The goal of all of this work is to consolidate and organize data so that it may be better understood. This is where Business Intelligence software comes into play. They act as a layer on top of data warehouses, allowing you to query, analyze, and visualize your data.

How Is Data Analyzed Using a Data Warehouse?

Generally four stages of data sophistication: source data, data lakes, data warehouses, and data marts. Knowing when to invest in a data warehouse necessitates understanding each stage, but ultimately, the data warehouse stage is what unlocks the actual value of your data.

Data Warehouse in Business Intelligence (BIDW): All you need to know

Source data

Any specific set of data, such as databases, Excel spreadsheets, individual application reports, and so on, is considered source data. It is structured (i.e., organized) but segregated data that works well on its own but does not provide a comprehensive picture of your organization’s data.

Data lakes

Data lakes are gradually emerging as the next step for teams who need to concentrate their source data in one location. A Data lake is a centralized store for all unstructured (i.e., unorganized) data.

If a Data Warehouse is like backing up a truck and unloading the data into an ordered shelving system, data lakes are like backing up the truck and dumping all the data into, well, a lake. The term “Data lake,” coined by James Dixon, is defined as the natural raw condition of data that, for those with diving skills, offers a frontier to explore.

A Data lake’s disadvantage is that the data is not ready for analysis. It’s disorganized, there may be duplicates, and you’ll need to tell your diver exactly what you’re searching for in order to make sense of it. Even yet, the diver may not locate exactly what you require after all.

Data warehouses

A Data Warehouse, like a data lake, centralizes your data, but as we’ve seen, it’s well-organized and ready for analysis. It provides a single source of truth for all data, making it simpler to understand and traverse.

Data Warehouse In Business Intelligence can connect directly to source data, but increasingly, firms are using their data warehouse as a layer on top of their data lake. According to Dixon’s analogy, if a data lake is water/data in its natural, disorganized state, a data warehouse is where it is treated and prepared for consumption.

Data marts

For some tasks, using a Data Warehouse In Business Intelligence is akin to striking a fly with a sledgehammer. If, for example, the marketing team frequently returns to the warehouse to perform similar queries, you can build up a data mart.

Data marts are collections of curated data sets designed for specific use cases. To return to Dixon’s description, the marketing team does not need to visit the treatment center every time they require water. The data warehouse can be used to package data/water into “water bottles” that are ready to consume.

The data warehouse remains the backbone of this data storage ecosystem. It’s structured and reasonably simple to grasp (similar to source data), but it delivers a comprehensive, consolidated perspective (similar to a data lake), making it much easier to use that data however you need it. (like creating data marts).

Data Warehouse In Business Intelligence process enormous amounts of data using Online Analytical Processing (OLAP). It centralizes all data on a single platform. It is a data processing method used by DWHs to streamline complex queries. In layman’s words, it is a computing method that assists users in extracting and querying the necessary data for analysis.

For example, if someone in a Data Warehousing inquires about the relationship between two separate datasets, OLAP processing would be utilized to quickly navigate through the stored data to find, identify, and summarize the needed information. A data warehouse provides BI with the data it needs to analyze using OLAP.

How Do Data Warehouses and Business Intelligence Platforms Interact With One Another?

Business Intelligence systems analyze data, whereas Data Warehouses store it. When you get these two systems to work in tandem, you’ll be able to reap the full benefits of business intelligence.

Business intelligence tools perform the “data analysis” stage of business intelligence, but their name comes from the fact that they are the culmination of the other two steps: data wrangling and data storage.

First and foremost, business intelligence solutions interface with a wide range of sources, including your data warehouse. They then give a simple method for querying the data in order to examine it for trends and insights. Then, using dashboards and reports, they make it simple to visualize and exchange data.

These three stages, built on a solid Data Warehouse In Business Intelligence foundation, will make it easier to follow through on the core promise of Business Intelligence: empowering everyone in your organization to comprehend and act on data.

Benefits of Data Warehouse In Business Intelligence?

Behind any effective Business Intelligence system is a strong Data Warehouse In Business Intelligence. A Data warehouse exactly is a centralized platform for combining and storing data from many sources, as well as preparing this data for downstream business intelligence and analytics. Consider it a centralized repository that organizes and saves all data for Business Intelligence analyses.

Data Warehouse in Business Intelligence (BIDW): All you need to know

A data analytics data warehouse maintains historical and current data in a structured manner that is ideal for complicated queries. It is then linked to Business Intelligence tools, which provide reports with forecasts, trends, and other visualizations to fuel actionable insights.

In business analytics, data warehouse components include ETL (extract, transform, and load) tools, a DWH database, DWH access tools, and reporting layers. These technologies are available to help speed up the data science process and decrease or remove the requirement to write code to manage data pipelines.

ETL tools aid in the extraction of data from source systems, conversion to the appropriate format, and loading of the converted data into the Data Warehouse In Business Intelligence. The database module stores and manages structured data for reporting purposes. Users of business intelligence and data analytics can interact with the data in the DWH using the access tools. The reporting layer includes a business intelligence interface for analyzing and displaying data stored in the data warehouse.

When Should I Use a Data Warehouse for Business Intelligence?

Data Warehouse in Business Intelligence (BIDW): All you need to know

Having the correct data in your Data Warehouse In Business Intelligence, as well as the right business intelligence to leverage that data enables a variety of activities that can drive strategic decision-making. Among the options are:

Data mining

Data mining, also known as knowledge discovery, is a method that extracts usable data from a larger amount of raw data. This method aids in the discovery of trends, themes, or patterns in enormous amounts of big data.

Performance metrics

Metrics are used to assess a company’s, its personnel, or specific campaigns’ behavior, actions, and, yes, performance. While performance measurements are the result of analysis, those results can be collected and analyzed further. Performance metrics collect essential data within a range, allowing a hypothesis to be made, validated, or disproven in accordance with previously established corporate objectives.


Analysts and business teams query data in Data Warehouse And Business Intelligence to ensure its authenticity or accuracy. Successful BI enables businesses and organizations to ask and answer questions about their data while also having the necessary data in place to obtain reliable, quantitative information from those answers.

Statistical analysis

Statistical analysis is one of the components of data analysis. Statistical analysis in the context of Business Intelligence and data warehousing entails gathering and assessing data samples. A sample in statistics is a subset of a larger population of data. For the analysis to be as accurate as possible and lead to smart, strategic decisions, the data must be warehoused and integrated into your Data Warehouse In Business Intelligence.

Data visualization

Data visualization is the process of visually portraying data in order to improve comprehension and inform decisions. Charts, diagrams, data tables, and infographics can be used to answer questions and give statistical validation for decisions. Data presentation as a spreadsheet can be a tedious and dry experience, but visualizing data often helps bring information to life in a more interesting and effective way.

Data storytelling

Data storytelling is the process of converting data analytics into plain English in order to influence or inform a strategic business decision. Having the proper data warehouse and the most dependable business intelligence tools will make it easier to compile and the tales more compelling.

By this time, you should understand how Data Warehouse In Business Intelligence works to produce effective workflows. The antiquated ETL system is untenable in today’s competitive business world. Businesses save money and operate more rapidly to integrate innovative solutions by streamlining and simplifying the data preparation process.

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