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1st Concepts And Method Business Intelligence Systems
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Pdf) Gaining Competitive Advantage Through Business Analytics
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Communication in the workplace is not just about how well you work with others. It’s about building relationships, minimizing errors and working effectively together as a team. Encouraging good communication habits .This is a fact in today’s competitive business environment that requires agile access to a data warehouse, organized in a way that improves business performance, and delivers fast, accurate and relevant data insights. The BI architecture stands to meet those needs, with data warehousing as the backbone of these processes.
In this post, we will explain the definitions, connections, and differences between data warehousing and business intelligence, and provide a BI architecture diagram that visually explains the correlation of these terms, and the framework in which they operate. But first, let’s start with the 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 technologies and techniques that create business intelligence systems used for online data visualization, reporting, and analysis. One of the BI architecture components is data warehousing. Organizing, storing, cleaning, and extracting data must be accomplished by a central storage system, i.e. a data warehouse, which is considered a fundamental component of business intelligence.
But how are they actually connected? What is data storage and business intelligence? Data warehousing and business intelligence are terms used to describe the process of storing all of a company’s data from various sources in an internal or external database with a focus on generating actionable insights through analytics and online BI tools.
One does not work without the other, and we will now explain the premises surrounding their framework to fully understand how a data warehouse enhances BI processes using a BI architecture diagram. A modern business BI architecture framework has various components and layers. That includes business intelligence architecture.
Each of those components has its own purpose which we will discuss in more detail when we focus on data storage. But first, let’s first see what these components are made of.
A solid BI architecture framework includes the following: Collection of data: The first step deals with the collection of relevant data from various external and internal sources that can be databases, ERP- or CRM systems, flat files, or APIs, just to name a few. little Data integration: In this step, the collected data is integrated into a centralized system, often with the help of ETL processes.
Here the data is also cleaned and prepared for analysis. Storage of Data: This is where data storage comes into the picture. A warehouse is a place where structured data is stored.
This makes it available for querying and analysis. Data Analysis: After the information is processed, stored and cleaned it is ready for analysis. With the right tools, data is visualized and used for strategic decision making. Distribution of data: Data, now in the form of graphs and charts, are distributed in various formats. This could be online reporting, dashboarding, or embedding solutions. Insights-based feedback: The final step in the architecture process is to extract actionable insights from the data and use them to make improved decisions to ensure company growth. **Click to expand** We can see in our BI architecture diagram how the process flows through different layers, and now we will focus on each one.
Data Collection The first step in creating a stable architecture begins with data collection from various data sources such as CRM, ERP, databases, files, or APIs, depending on the company’s needs and resources. Modern BI tools provide many different, fast, and easy data connectors to make this process smooth and easy, using smart ETL engines in the background. They enable communication between dispersed departments and systems that would otherwise remain separate.
From a business perspective, this is a critical element in creating a successful data-driven decision culture that can eliminate errors, increase productivity, and streamline operations. You must collect data to be able to manipulate it.2. Data Integration When data is collected through scattered systems, the next step continues to extract the data and load it into the BI data warehouse. This process is called ETL (Extract-Transform-Load).
Today with increasing amount of data and overload on 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 drawn from external sources (from Step 1) while ensuring these sources are not negatively impacted by performance or other issues. Second, the data conforms to the demanded standard. In other words, this (transformation) step ensures that the data is clean and ready for the final step: loading it into the data depository.
. Data Warehousing Now let’s move on to 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 clearer picture of the subject. The main differences, as we can also see visually, between business intelligence and data warehousing are indicated in these key questions: a) What is the goal? Business intelligence and data warehousing have different goals.
While they are connected and cannot work without each other, as mentioned above, BI is primarily focused on generating business insights, whether operational or strategic efficiency such as product status and targets on pricing, profitability, sales performance, forecasting, strategic directions, and priorities. On a broader scale. The point is to access, explore, and analyze the measurable aspects of business. On the other hand, a data warehouse (DWH) is important for storing all of a company’s data (from one or more sources) in one place.
In essence, BI systems and tools use DWH while DWH serves as the foundation for BI. b) What is the output? The output data of both conditions also differ. While BI outputs information through data visualization, online dashboards, and reports, data warehousing maps data into 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 DWH while BI uses tools that focus on statistics, visualization, and data mining, including self-service business intelligence. The output difference is closely related to people who may work with BI or data warehouse. But let’s look at it through our other major aspect. c) What is the audience?
To expand on our previous point, the people involved in data management are quite different. C-level executives or managers use modern BI tools in the form of real-time dashboards because they need to gain factual intelligence, create effective sales reports, or forecast the strategic development of a department or company. CEOs or sales managers cannot manage data warehouses as it is not their area of expertise; They need a Business Intelligence, Analysis And Analytics.
