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This book will give you a competitive advantage by helping you to quickly learn how to design and build BI system with Microsoft BI tools. This book starts with designing a data warehouse with dimensional modeling, and then looks at creating data models based on SSAS multidimensional and Tabular technologies. Nowadays, Business Intelligence BI is one of the hot topics in most of the job markets around the world. Most companies are establishing or planning to establish a Business Intelligence system and a data warehouse DW.
Knowledge related to the BI and data warehouse are in great demand in the job market. This chapter gives you an understanding of what Business Intelligence and data warehouse is, what the main components of the BI system are, and what the steps to create the data warehouse are.
This chapter focuses on the designing of the data warehouse, which is the core of a BI system. The following chapters are about other BI components such as visualization, data integration, data governance, and so on.
A data warehouse is a database designed for analysis, and this definition indicates that designing a data warehouse is different from modeling a transactional database.
Designing the data warehouse is also called dimensional modeling. In this chapter, you will learn about the concepts of dimensional modeling. Business Intelligence is an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance. As the definition states, the main purpose of a BI system is to help decision makers to make proper decisions based on the results of data analysis provided by the BI system.
Nowadays, there are many operational systems in each industry. Businesses use multiple operational systems to simplify, standardize, and automate their everyday jobs and requirements. Each of these systems may have their own database, some of which may work with SQL Server, some with Oracle.
Some of the legacy systems may work with legacy databases or even file operations. There are also systems that work through the Web via web services and XML. Operational systems are very useful in helping with day-to-day business operations such as the process of hiring a person in the human resources department, and sale operations through a retail store and handling financial transactions.
The rising number of operational systems also adds another requirement, which is the integration of systems together. Business owners and decision makers not only need integrated data but also require an analysis of the integrated data.
As an example, it is a common requirement for the decision makers of an organization to compare their hiring rate with the level of service provided by a business and the customer satisfaction based on that level of service. As you can see, this requirement deals with multiple operational systems such as CRM and human resources. The requirement might also need some data from sales and inventory if the decision makers want to bring sales and inventory factors into their decisions.
As a supermarket owner or decision maker, it would be very important to understand what products in which branches were in higher demand. This kind of information helps you to provide enough products to cover demand, and you may even think about creating another branch in some regions.
The requirement of integrating multiple operational systems together in order to create consolidated reports and dashboards that help decision makers to make a proper decision is the main directive for Business Intelligence. Some organizations and businesses use ERP systems that are integrated, so a question may appear in your mind that there won't be a requirement for integrating data because consolidated reports can be produced easily from these systems. So does that mean that these systems still require a BI solution?
The answer in most cases is yes. The companies or businesses might not require a separate BI system for internal and parts of the operations that implemented it through ERP. However, they might require getting some data from outside, for example, getting some data from another vendor's web service or many other protocols and channels to send and receive information. This indicates that there would be a requirement for consolidated analysis for such information, which brings the BI requirement back to the table.
After understanding what the BI system is, it's time to discover more about its components and understand how these components work with each other. There are also some BI tools that help to implement one or more components. The following diagram shows an illustration of the architecture and main components of the Business Intelligence system:.
The BI architecture and components differ based on the tools, environment, and so on. The architecture shown in the preceding diagram contains components that are common in most of the BI systems.
In the following sections, you will learn more about each component. The data warehouse is the core of the BI system. A data warehouse is a database built for the purpose of data analysis and reporting. This purpose changes the design of this database as well. As you know, operational databases are built on normalization standards, which are efficient for transactional systems, for example, to reduce redundancy.
As you probably know, a 3NF-designed database for a sales system contains many tables related to each other. So, for example, a report on sales information may consume more than 10 joined conditions, which slows down the response time of the query and report. A data warehouse comes with a new design that reduces the response time and increases the performance of queries for reports and analytics. You will learn more about the design of a data warehouse which is called dimensional modeling later in this chapter.
It is very likely that more than one system acts as the source of data required for the BI system. So there is a requirement for data consolidation that extracts data from different sources and transforms it into the shape that fits into the data warehouse, and finally, loads it into the data warehouse; this process is called Extract Transform Load ETL. There are many challenges in the ETL process, out of which some will be revealed conceptually later in this chapter.
According to the definition of states, ETL is not just a data integration phase. Let's discover more about it with an example; in an operational sales database, you may have dozen of tables that provide sale transactional data.
When you design that sales data into your data warehouse, you can denormalize it and build one or two tables for it. So, the ETL process should extract data from the sales database and transform it combine, match, and so on to fit it into the model of data warehouse tables. There are some ETL tools in the market that perform the extract, transform, and load operations. SSIS also has many built-in transformations to transform the data as required. A data warehouse is designed to be the source of analysis and reports, so it works much faster than operational systems for producing reports.
However, a DW is not that fast to cover all requirements because it is still a relational database, and databases have many constraints that reduce the response time of a query. The requirement for faster processing and a lower response time on one hand, and aggregated information on another hand causes the creation of another layer in BI systems.
This layer, which we call the data model, contains a file-based or memory-based model of the data for producing very quick responses to reports. Microsoft's solution for the data model is split into two technologies: the OLAP cube and the In-memory tabular model.
The OLAP cube is a file-based data storage that loads data from a data warehouse into a cube model. The cube contains descriptive information as dimensions for example, customer and product and cells for example, facts and measures, such as sales and discount. The following diagram shows a sample OLAP cube:. In the preceding diagram, the illustrated cube has three dimensions: Product , Customer , and Time.
Each cell in the cube shows a junction of these three dimensions. Aggregated data can be fetched easily as well within the cube structure. For example, the orange set of cells shows how much Mark paid on June 1 for all products.
As you can see, the cube structure makes it easier and faster to access the required information. Multidimensional modeling is based on the OLAP cube and is fitted with measures and dimensions, as you can see in the preceding diagram. The tabular model is based on a new In-memory engine for tables. The In-memory engine loads all data rows from tables into the memory and responds to queries directly from the memory. This is very fast in terms of the response time.
The frontend of a BI system is data visualization. In other words, data visualization is a part of the BI system that users can see. There are different methods for visualizing information, such as strategic and tactical dashboards, Key Performance Indicators KPIs , and detailed or consolidated reports.
As you probably know, there are many reporting and visualizing tools on the market. Microsoft has provided a set of visualization tools to cover dashboards, KPIs, scorecards, and reports required in a BI application. Excel is also a great slicing and dicing tool especially for power users. There are also components in Excel such as Power View, which are designed to build performance dashboards.
Sometimes, you will need to embed reports and dashboards in your custom written application. Chapter 12 , Integrating Reports in Application , of this book explains that in detail. Every organization has a part of its business that is common between different systems. That part of the data in the business can be managed and maintained as master data.
For example, an organization may receive customer information from an online web application form or from a retail store's spreadsheets, or based on a web service provided by other vendors. Master Data Management MDM is the process of maintaining the single version of truth for master data entities through multiple systems.
Even if one or more systems are able to change the master data, they can write back their changes into MDS through the staging architecture.
The quality of data is different in each operational system, especially when we deal with legacy systems or systems that have a high dependence on user inputs. As the BI system is based on data, the better the quality of data, the better the output of the BI solution. Because of this fact, working on data quality is one of the components of the BI systems.
As an example, Auckland might be written as "Auck land" in some Excel files or be typed as "Aukland" by the user in the input form. As a solution to improve the quality of data, Microsoft provided users with DQS. DQS works based on Knowledge Base domains, which means a Knowledge Base can be created for different domains, and the Knowledge Base will be maintained and improved by a data steward as time passes. There are also matching policies that can be used to apply standardization on the data.
A data warehouse is a database built for analysis and reporting. In other words, a data warehouse is a database in which the only data entry point is through ETL, and its primary purpose is to cover reporting and data analysis requirements. This definition clarifies that a data warehouse is not like other transactional databases that operational systems write data into. When there is no operational system that works directly with a data warehouse, and when the main purpose of this database is for reporting, then the design of the data warehouse will be different from that of transactional databases.
If you recall from the database normalization concepts, the main purpose of normalization is to reduce the redundancy and dependency.
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