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The Role Of Data Warehousing In Your Business Intelligence Architecture

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.


Data Warehousing Types


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

Types:

  • 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.

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).


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.


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.


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.


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.


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.


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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