Business Intelligence Framework

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Business Intelligence Framework – Business Intelligence (BI) is a very complex area of ​​science/industry that is difficult to understand and very easy to understand – especially for people who are not domain experts in the field. BI aims to transform complex data into information and knowledge to help knowledge workers and managers better understand, analyze and develop a competitive business strategy. Those working in the field know the difficulty of translating BI business requirements into the technical side and explaining why some technical implementations are too complex and require more resources to complete certain business needs.

In any BI-related project, it is important to identify the components and aspects of the BI environment that are affected by the project and may need to be changed. This is a very complex task, because at the beginning it is difficult to identify all the elements that make up a BI environment and to identify the relationships and dependencies between them.

Business Intelligence Framework

To support the process of identifying elements of the BI environment that may be affected or changed by the project in question, I* developed a Comprehensive Business Intelligence Framework (HBIF) as part of a project at Staffordshire University. HBIF is the result of more than 12 months of work involving more than 130 business intelligence and data warehouse experts from 27 different countries.

Business Intelligence Framework Process Infographic

This is to explain the complexity of BI in one picture, while at the same time enabling an immediate understanding of the BI environment for those working outside the field.

Figure 1 shows HBIF, which consists of two views: layers and views. In the vertical view, HBIF is divided into three layers: the source layer, the data warehouse layer, and the presentation layer. Vertical data-based layering is a well-established method with a foundation in the theoretical foundations of BI (Inmon, Kimball, etc.). The three-layer approach enables us to identify components and aspects within a specific layer of data when working with a BI environment. For example, it can identify related concepts, applications, hardware, data types, and users at the data source level. It follows the typical data journey in a BI environment.

I extend this traditional vertical, data-driven three-layer separation with a horizontal presentation of the BI environment/ecosystem. This enables us to see the layers in the wider context of the BI environment. For example, the resource manager for a BI project needs to understand the hardware, application, and project needs of the project in order to plan. Each perspective must be clearly defined to support optimal procurement and supply. This framework enables an overview of the resources required at different stages, for example the implementation of the storage layer or the presentation layer. The structure of the framework can support users with different needs. For example, IT management may only be interested in a high-level view while implementation teams, and in particular, teams dealing with hardware infrastructure and those delivering applications, may use in the framework to focus only on performance. Their field of interest and project expertise.

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This framework can be used as a stand-alone representation of the overall BI environment and also provides a basis for exploring the broader BI environment.

A Framework For Industrial Artificial Intelligence

To learn more about the HBIF development process, about any view, layer or element, please check

Despite the fact that the development of the framework is my own work, it would not be possible without the great support of effective business decision-making processes that rely on quality information. It is a reality in today’s competitive business environment that requires agile access to a data warehouse, organized in a way that improves business performance and provides fast, accurate and relevant insights into data. The BI architecture emerged to meet these requirements, with the data warehouse as the backbone of these processes.

In this post, we explain the definition, relationship, and differences between data warehousing and business intelligence, and provide a BI architecture diagram that visually explains the relationship between these terms and the framework in which they work. But first, let’s start with some basic definitions.

What is BI architecture? Business intelligence architecture is a term used to describe the standards and policies for organizing data with the help of computer-based techniques and technologies that create business intelligence systems for online data. visualization, reporting, and analysis. One of the components of BI architecture is data warehouse tools. Organizing, storing, cleaning and retrieving data should be done in a central storage system, ie data warehouse, which is considered a fundamental part of business intelligence. But how are they connected? Before we answer this question, let’s first explain in more detail what data warehouse models are. What is a data warehouse? A data warehouse is a central repository for businesses to store and analyze large 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, DWH is a data management system where organizations store current and historical information from sales, marketing, finance, customer service, etc. It accelerates BI processes by giving organizations the tools to create queries and answer their most pressing analytical questions. Through this, companies can optimize their performance and build strategies based on detailed insights rather than pure intuition. When trying to understand DWH and its value in a business environment, it is important 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 discuss some of the differences in appearance to help you put the value of a barn into perspective. Database vs Data Warehouse The first and most important difference between the two is the fact that databases record data and transactions, usually in table format. Users can access, manipulate and restore at will. The ultimate goal of a database is to provide a secure and organized way to store and access user information. Warehouses, on the other hand, store large amounts of data from many different sources and store them for analysis purposes. Providing environmental businesses need to ask questions and communicate their most important strategies. The second difference, which is also one of the most important, is the way data is processed. On the one hand, databases use online transaction processing (OLTP) to perform many simple transactions such as inserting, replacing, and updating, etc. In addition, OLTP responds quickly to user requests and can process data in real time. Data warehouses, on the other hand, use online analytical processing (OLAP) to quickly analyze large volumes of big data. The main difference between the two is that while OLTP can collect data that happened just a few seconds ago, OLAP can process and analyze data a thousand times faster. On that note, the third and final difference between the two is that databases are usually limited to one use case, i.e. keep real-time data about every item sold on your website. It can process many simple and detailed queries in a short time. On the other hand, a DWH is “subject oriented” and can obtain summarized data for complex questions that are later used for analysis and reporting. These are just three of the many differences between the two. We won’t talk about them any more because it would defeat the real purpose of this blog. However, you can check it in more detail in this article. Types of Data Warehouse Now that you understand the basic concepts of data warehousing, let’s look at some of the key types you need to know. Types: Enterprise Data Warehouse (EDW): As the name suggests, an EDW provides a centralized system for companies to store and manage information from multiple sources. It helps in decision making from a tactical and strategic point of view. Operational Information Store (ODS): ODS is a complement to the EDW we described above. It is a central database that is updated in real time and is used for operational reporting when the EDW does not include business reporting requirements. Data Mart: This is a subset of a DWH specifically designed for a specific business area or team. such as sales, human resources or marketing. It’s focused on topic, meaning users can quickly find the insights they need. Without further ado, let’s see how BI and DWH connect. What is data warehouse and business intelligence? Data warehousing and business intelligence are terms used to describe the process of storing all company data in internal or external databases from various sources with a focus on analysis and generating actionable insights online. BI tools. There are many discussions on the topic of BI and DW. Some say that the data warehouse concept has been “relabeled” as business intelligence. So, they mean the same thing. Some say that they are completely different and can be considered two separate categories of software. While others will tell you that data warehousing is one of them

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