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The Role Of Data Warehousing In Your Business Intelligence Architecture

Data warehousing and business intelligence by datapine

Effective decision-making processes in business are dependent upon high-quality information. That’s a fact in today’s competitive business environment that requires agile access to a data storage warehouse, organized in a manner that will improve business performance, and deliver fast, accurate, and relevant data insights. BI architecture has emerged to meet those requirements, with data warehousing as the backbone of these processes.

In this post, we will explain the definition, connection, and differences between data warehousing and business intelligence, and provide a BI architecture diagram that will visually explain the correlation of these terms, and the framework on which they operate. But first, let’s start with basic definitions.

What Is BI Architecture?

Business intelligence architecture is a term used to describe standards and policies for organizing data with the help of computer-based techniques and technologies that create business intelligence systems used for online data visualization, reporting, and analysis.

One of the BI architecture components is data warehousing tools. Organizing, storing, cleaning, and extracting the data must be carried out by a central repository system, namely a data warehouse, which is considered the fundamental component of business intelligence. 

But how exactly are they connected? Before we answer that question, let’s first define in more detail what data warehouse models are all about. 

What Is Data Warehousing?

A data warehouse is a central repository for businesses to store and analyze massive amounts of data from multiple sources. Data warehousing is considered a key element of the business intelligence process, providing organizations with the tools to make informed decisions.  

In other words, a DWH is a system for data management where organizations store current and historical information from sales, marketing, finance, customer service, and more. It facilitates the BI processes by providing organizations with the means to generate queries and answer their most pressing analytical questions. Through that, companies can optimize their performance and build strategies based on accurate insights instead of pure intuition. 

When trying to understand DWH and its value in a business environment, it is essential to distinguish it from a database. While the two are similar and can be considered valuable for data storage and management, they are different. Below we will discuss some apparent differences to help you put the value of a warehouse into perspective.

Database vs. Data Warehouse 

The first and most crucial difference between the two is the fact that databases record data and transactions, usually in a table format, which users can access, manipulate and retrieve at their will. The end goal of a database is to provide users with a secure and organized way to store and access their information. Warehouses, on the other hand, store massive amounts of data from multiple disparate sources and stores them for analytical purposes. Providing businesses with the environment they need to make queries and inform their most important strategies. 

The second difference, which is also among the most significant ones, is the way they process the data. On the one hand, databases use OnLine Transactional Processing (OLTP) to perform a number of simple transactions, such as insert, replace, and update, among others. In addition, OLTP responds to users' requests immediately, making it possible to process data in real-time. On the other hand, data warehouses use OnLine Analytical Processing (OLAP) to analyze massive amounts of big data quickly. The main difference between the two is that while OLTP can gather data that happened just a few seconds ago, OLAP can process and analyze the data a thousand times faster.

On that same note, a third and last difference between the two is that databases are typically limited to a single use case, for example, store real-time data about each item sold on your website. It can process a huge number of simple and detailed queries in a short time. Conversely, a DWH is “subject-oriented” and can retrieve summarized data for complex queries that are later used for analysis and reporting. 

These are just three of the various differences between the two. We will not dive any deeper into them because we would stray away from the actual purpose of this blog. However, you can check them in more detail in this article

Data Warehousing Types

Now that you understand the main data warehouse concepts, let’s look at some key types that you need to know. 


  • Enterprise Data Warehouse (EDW): As its name suggests, an EDW provides a centralized system for enterprises to store and manage information from a wide number of sources. It assists decision-making from a tactical and strategic point of view. 
  • Operational Data Store (ODS): An ODS complements the EDW we just described above. It is a central database that updates in real-time, and it is used for operational reporting when the EDW doesn’t cover the business’s reporting requirements. 
  • Data Mart: It is a subset of a DWH designed especially for a specific business area or team, such as sales, HR, or marketing. It is subject-oriented, meaning users can find the insights they need very quickly. 

Without further ado, let’s look at how BI and DWH are connected. 

What Is Data Warehousing And Business Intelligence?

Data warehousing and business intelligence are terms used to describe the process of storing all the company’s data in internal or external databases from various sources with a focus on analysis and generating actionable insights through online BI tools.

There are many discussions surrounding the topic of BI and DW. Some say that the concept of data warehouse was “relabeled” as business intelligence; hence, they mean the same. Others say they are entirely different and can be considered two separate software categories. While others will tell you that a data warehouse is one of the multiple tools that support the BI process. For the purpose of this article, we will consider the last statement as the truth. 

Rather you consider them separate or interchangeable concepts; one without the other wouldn’t function. So, to help get all of this confusion out of the way, here we will explain the premises that surround their framework by using a BI architecture diagram to understand how the data warehouse enhances the BI processes fully.

BI Architecture Framework In Modern Business

There are various components and layers that business intelligence architecture consists of. Each of those components has its own purpose that we will discuss in more detail while concentrating on data warehousing. But first, let’s first see what exactly these components are made of.

A solid BI architecture framework consists of:

  1. Collection of data: The first step is related to the collection of relevant data from various external and internal sources which can be databases, ERP- or CRM systems, flat files, or APIs, just to name a few.  
  2. Data integration: At this stage, the data collected is integrated into a centralized system, often with the help of ETL processes. Here the data is also cleaned and prepared for analysis.  
  3. Storage of data: This is where a DWH comes into the picture. A warehouse is a place in which structured data is stored. It makes it available for querying and analysis. 
  4. Data analysis: After the information is processed, stored, and cleaned it is ready to be analyzed. With the help of the right tool, the data is visualized and used for strategic decision-making. 
  5. Distribution of data: The data, now in the form of graphs and charts, is distributed in different formats. This can be online reporting, dashboarding, or embedding solutions. 
  6. Reaction based on insights: The final stage of the architecture is to extract actionable insights from the data and use them to make improved decisions to ensure company growth. 
The BI architecture diagram depicting the framework: 1. Collecting data, 2. Integrating data, 3. Storing, 4. Analyzing, 5. Distributing the data, 6. Reacting

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We can see in our diagram above how the process flows through various layers, and now we will focus on the BI architecture and its components in detail.

1. Collection of data

The first step in creating a stable architecture starts with gathering data from various data sources such as CRM, ERP, databases, files, or APIs, depending on the requirements and resources of a company. Modern BI software offers a lot of different, fast, and easy data connectors to make this process smooth and easy by using smart ETL engines in the background. They enable communication between scattered departments and systems that would otherwise stay disparate. From a business point of view, this is a crucial element in creating a successful data-driven decision culture that can eliminate errors, increase productivity, and streamline operations. You have to collect data in order to be able to manipulate it.

2. Data integration

When data is collected through scattered systems, the next step continues in extracting data and loading it into a BI data warehouse architecture. This is called ETL (Extract-Transform-Load).

ETL process is conducted by 3 main steps: 1. Extract, 2. Transform, 3. Load

With an increasing amount of data generated today and the overload of IT departments and professionals, ETL as a service comes as a natural answer to solve complex data requests in various industries. The process is simple; data is pulled from external sources (from step 1) while ensuring that these sources aren’t negatively impacted by performance or other issues. Secondly, data conformed to the demanded standard. In other words, this (transform) step ensures data is clean and prepared for the final stage: loading into a data depository.

3. Data storage

Now we approach the data warehousing and business intelligence concepts. While both terms are often used interchangeably, there are certain differences that we will focus on to get a more clear picture of this topic.

The differences between data warehousing and business intelligence based on the goals, output, audience, and tools.

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The main differences, as we can also see in the visual, between business intelligence and data warehousing are indicated in these main questions:

a) What is the goal?

Business intelligence and data warehousing architecture have different goals. While they are connected and cannot function without each other, as mentioned earlier, BI is mainly focused on generating business insights, whether operational or strategic efficiency such as product positioning and pricing to goals, profitability, sales performance, forecasting, strategic directions, and priorities on a broader level. The point is to access, explore, and analyze measurable aspects of a business. On the other hand, a data warehouse (DWH) has significance in storing all the company’s data (from one or several sources) in a single place. In a nutshell, BI systems and software make use of a DWH while a DWH acts as a foundation for BI.

b) What is the output?

The output data of both terms also vary. While BI outputs information through data visualization, online dashboards, and reports, the data warehouse outlines data in dimensions and fact tables for upstream applications (or BI tools). Data cleansing, metadata management, data distribution, storage management, recovery, and backup planning are processes conducted in a DWH while BI makes use of tools that focus on statistics, visualization, and data mining, including self service business intelligence.

The output difference is closely interlaced with the people that can work with either BI or data warehouse. But let’s see this through our next major aspect.

c) What is the audience?

To expand on our previous point, the people involved in managing the data are quite different. C-level executives or managers use modern BI tools in the form of a real-time dashboard since they need to derive factual intelligence, create effective sales reports, or forecast the strategic development of the department or company. CEOs or sales managers cannot manage data warehouses since it’s not their area of expertise; they need a tool that will translate the heavy IT data into insights that an average user can fully understand. That’s where BI creates a solid bridge between DWH and BI. On the other hand, a data warehouse is usually dealt with by data (warehouse) engineers and back-end developers. They are the technical chain in a BI architecture framework that designs, develops, and maintains systems for future data analysis and report generation that a company might need.

d) What are the tools?

With the expansion of data processed and created in our digital age, the tools and software needed to perform analysis expanded and developed in recent years in ways we could not have imagined. In this context, the need for utilizing a proper tool, a stable business intelligence dashboard, and a data warehouse increased exponentially. In such an environment, the data warehouse processes can be managed with a product such as Amazon Redshift while full support for BI insights is needed to effectively generate and develop sustainable business acumen with tools such as datapine. Visualization of data is the core element that enables managers, professionals, and business users to perform analysis on their own, without the need for heavy IT support or work.

Now that we have expounded what is data warehousing and business intelligence management, we continue with our next step: analyzing the BI architecture layers needed for establishing sustainable business development.

4. Analysis of data

In this step of our compact architecture of business intelligence, we will focus on the analysis of data after it’s handled, processed, and cleaned in former steps with the help of data warehouse(s). The ubiquitous need for successful analysis for empowering businesses of all sizes to grow and profit is done through BI application tools. Especially when it comes to ad hoc analysis that enables freedom, usability, and flexibility in performing analysis and helping answer critical questions swiftly and accurately.

Drag and drop interface by datapine's dashboard tool

This visual above represents the power of a modern, easy-to-use BI user interface. Modern BI tools like datapine empower business users to create queries via drag and drop and build stunning data visualizations with a few clicks, even without profound technological knowledge. This simplifies the process of creating dashboards, or an analytical report, and generates actionable insights needed for improving the operational and strategic efficiency of a business. The data warehouse design works behind this process and makes the overall architecture possible.

Paired with this, technical users also have the opportunity to build their own queries with the use of an intuitive SQL box. This feature allows them to write their own queries with flexibility as they can manually enter code, perform cross-database queries, and easily modify data series.

When it comes to self-service business intelligence it's all about taking the data analysis process to the next level in a user-friendly way. For this purpose, BI software offers analytical features that allow users to navigate through their data efficiently. Tools such as datapine offer a range of options such as: 

  • Conditional formatting: This feature is part of the drag-and-drop interface and it allows you to highlight variances to discover hidden trends and patterns in your data.  Formatting can be made based on custom criteria and displayed with different color scales just like you would on an Excel spreadsheet.
  • Predictive analytics: By carefully analyzing current and historical data, a predictive analytics feature can find relationships, trends, and patterns and generate accurate forecasts about future performance. Through this, companies can stay on top of any issues, optimize their activities, and prepare their production to cover demand. 
  • Tooltip: The tooltip feature allows users to add additional information to their charts in order to complement the analysis. It is triggered once the user hovers over a chart with the mouse and a small snippet will display with the extra information. This can be the definition of a KPI or other relevant notes. 
  • Drill downs: The drill down feature allows you to visualize lower levels of hierarchical data all in one chart. For example, imagine you have a chart displaying sales by category. A drill down would allow you to click on a specific category and see sales by-products in that category. This way, you don’t need to generate multiple charts to visualize your data, you can easily explore it all in one visual.  

5. Data distribution

Data distribution comes as one of the most important processes when it comes to sharing information and providing stakeholders with indispensable insights to obtain sustainable business development. Distribution is usually performed in 3 ways:

a) Reporting via automated e-mails: Created reports can be shared with selected recipients on a defined schedule. The dashboards will be automatically updated on a daily, weekly, or monthly basis which eliminates manual work and enables up-to-date information.

b) Dashboarding: Another reporting option is to directly share a dashboard in a secure viewer environment. The users you share with cannot make edits or change the content but can use assigned filters to manipulate data and interact with the KPI dashboard. Another option is to share via a public URL that enables users to access the dashboards even if they’re outside of your organization, as shown in the picture below:

Sharing options with datapine's dashboards

c) Embedding & white label BI: This form of data distribution allows you to integrate a BI system with all its features into your own application. This way, you get all the benefits of business intelligence (interactive filters, user role management, live monitoring, etc.) without the need to invest in developing a tool of your own. Paired with this, a white-labeling option allows you to customize the embedded dashboard with the colors, logo, and font of the company for an extra professional look. Like this, embedding BI offers high value to an organization by making reports more interactive and accessible for internal and external users.

Another way to look at data distribution is through who is consuming it. When dealing with sensitive business information, it is of utmost importance to ensure security across the board. For this purpose, permissions are granted to different consumers based on their role as data consumption differs from user to user. For instance, more technical analytical roles deal with data warehousing, while more business-driven ones deal with BI. Some of these roles include: 

  • BI Architect: The one in charge of the entire design and development of a BI system. He or she determines the platforms, processes, and procedures for the central data warehouse. The BI architect needs to carefully analyze the organization's requirements to plan an efficient framework. 
  • BI Developer: The developer is the person that works at “ground level”. It is responsible for building, creating, or improving BI-driven solutions at a technical level.  In the case of our BI architecture stages, the developer is responsible for performing ETL and other tasks related to the general management and maintenance of databases. 
  • Data Analyst: A BI data analyst is responsible for an efficient data management process. He or she is capable of drilling down into the data and finding improvement opportunities as well as relevant insights that will serve as a foundation for strategic decision-making. 
  • Business User: Usually, with no technical knowledge, the average business user relies on interactive visualizations such as dashboards to measure their overall performance. These business dashboards are usually shared between different departments to support collaborative discussions toward general company goals. 

6. Reactions based on generated insights

The final stage, where the BI architecture expounds its power, is the fundamental part of any business: creating data-driven decisions. Without the backbones of data warehousing and business intelligence, the final stage wouldn’t be possible and businesses won’t be able to progress. CEOs, managers, professionals, coworkers, and all the interested stakeholders can have the power of data to generate valid, accurate, data-based decisions that will help them move forward. Let’s see this through one of our dashboard examples: the management KPI dashboard.

Management KPI dashboard example

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This dashboard is the final product of how data warehouse and business intelligence work together. The processes behind this visualization include the whole architecture which we have described, but it would not be possible to achieve without a firm data warehouse solution. Ultimately, this enables a high-level manager to get a comprehension of the strategic development and potential decisions for creating and maintaining a stable business.

On this particular dashboard, you can see the total revenue, as well as on a customer level, adding also the costs. The targets are also set so that the dashboard immediately calculates if they have been met or if additional adjustments are needed from a management point of view. As revenue is one of the most important factors when evaluating if the business is growing, this management dashboard ensures all the essential data is visualized and the user can easily interact with each section, on a continual basis, making the decision processes more cohesive and, ultimately, more profitable.

Analytical tools like the dashboard above, facilitate the way most organizations deal with their data. Thanks to 24/7 access to real-time insights, you don’t need to spend hours manually updating reports or even checking them on a daily bases. Modern BI tools such as datapine offer AI-powered data alerts that notify you as soon as an anomaly occurs or a goal is met. All you need to do is set predefined rules and the tool will send you a notification if something needs your attention. It does this by using neural networks and machine learning technologies to learn from patterns and trends in the data. This way, you can focus your attention on growing your company. 

Importance Of A Smart BI Architecture

We cannot end this journey without explaining the importance of implementing a smart BI architecture into your organization. While we made the value its value clear throughout its components, it is also important to mention some of its main benefits. A strong BI architecture serves as a blueprint for collecting, organizing, and efficiently managing business data that is then turned into insights for improved decision-making. Let’s look at some points in more detail. 

  • Correct use of data: While many organizations want to leverage the power of data-driven processes, not all of them succeed. This is mostly because the data being collected comes in different formats and applications that are hard to manage and organize. In fact, a shocking 95% of businesses cite the need to manage unstructured data as a problem. That said, a well-implemented BI framework leaves all of these issues in the past as it provides an organized management system for the data. 
  • Take the weight from the IT department: For decades, analytical tasks have been delegated to the IT department. Such tasks include generating performance reports with data that supports managers and employees in making strategic decisions. With markets becoming more and more competitive, the need for daily analysis has left IT employees overwhelmed with work and with not enough time to cover the demand.  Having a smart BI architecture system implemented will significantly relieve the IT department of the tedious task of generating reports. Leaving them enough time to focus on other important issues such as cybersecurity and the correct functioning of the company’s system. 
  • Increased efficiency: Expanding on the point above, implementing the right BI architecture into your business will not only relieve the IT department from time-consuming reporting tasks but will also increase the overall efficiency of the organization. A BI system allows employees to easily automate their reports to have access to real-time data on the go. This will empower them to integrate data into their strategic process instead of waiting hours or days for it to be delivered to them in the form of a static report. This is especially true considering that in 2019, 64% of users reported that BI data and analytics helped improve their efficiency and productivity.
  • Save money: The opposite of a BI framework is most likely data spread around various systems managed differently by each department. Naturally, this means a lack of synergy between the different activities and departments as well as a lack of efficiency and more costs to the company. On the contrary, BI applications save organizations money and time by providing centralized access to company data. In the long run, every relevant stakeholder will be connected with each other and a collaborative environment will be implemented throughout the organization. 

The Importance Of Security In Business Intelligence & Data Warehousing

If you’ve ever worked with data, analytics, and reporting before, you are probably aware of the importance of security and privacy. Organizations gather massive amounts of sensitive information from their customers and internal operations. This information is constantly subjected to security concerns as the risk of cyber-attacks and data breaches becomes increasingly more widespread. The consequences of one of these attacks can be detrimental to the success of an organization from a financial and reputational perspective but also from a legal perspective, as multiple regulations for data protection are in place worldwide. 

Most of the concerns regarding security lie in the storage process, especially in the cloud. With cloud data warehousing becoming increasingly popular by the day, it is important to be aware of any security issues beforehand. In fact, according to a report by Yellowbrick in 2021, 57% of IT executives who don’t migrate to a cloud DWH cite security concerns as one of the main reasons. 

Among some of the most common security concerns encountered in DWH management, we have unauthorized access, which means a person with no permission to access the system managed to get in. This can result from weak passwords, out-of-date technology, or a lack of governance practices by the company. Some other common threats include theft of a company device such as a laptop or hardware, hacking through a phishing scam, malware attacks, and even insider threats where a person with authorized access purposely damages or steals the data. 

Now, this all sounds really scary and dangerous. However, companies and technology developers have been aware of these issues for a long time and are putting in place multiple security measures to prevent any of these threats from happening. These measures include setting precise access controls for authorized users, encryption, and training, among other things. Let’s discuss some of these data warehousing best practices in more detail below. 

  • Access: This is setting up strict restrictions such as user accounts and passwords to ensure only authorized people can access the DWH. Each user's access level will depend on their work responsibilities and be limited to the information they need to perform their jobs. The access is often controlled by authentication mechanisms such as a two-factor or biometric. This also helps in providing a level of accountability for any manipulation or changes in the data. 
  • Encryption: One of the most common security measures is encrypting the data in transport and rest. Some believe encryption can affect the DWH performance in some capacity and choose to invest in other security measures or even take the risk of a cyberattack. However, considering how sophisticated these attacks are becoming, encryption has become fundamental.
  • Data masking: This technology protects sensitive information by generating a “fake” but realistic data version in the same format. This way, the sensitive data is hidden, ensuring that it remains confidential. Masking is used for security but also testing and training. 
  • Staying compliant: As mentioned above, there are many regulations, such as HIPAA, GDPR, SOX, and many more, that regulate and protect user data and how companies can manage it. These regulations force organizations of all sizes to put in place strict security measures to ensure data remains private and protected. 

Data Warehousing And Business Intelligence: Solutions For A Forward-Looking Business

We have explained these terms and how they complement the BI architecture. These processes are important to consider in today’s competitive business environment since they bring the best data management practice that can only bring positive results.

Although the terms have been used as synonyms in recent years, today, they function on diverse levels, but the perspective is the same: analyze, clean, monitor, and evaluate the data in the finest and most productive way possible.

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