 Hello, and welcome to this session in which we will discuss the concept of database system. In this session, we will also cover terms such as database management system, data warehouse and data mart. Starting with the database system, it's basically a software system that does what? That manages data that is stored on a database. Now what is data or a database? Well, database is an organized collection of data. What is data? Data could be text, numbers, images, videos, so on and so forth. That's stored and managed on a computer system, server, someplace on the cloud. Well, that's what the data is. The purpose of a database system is to do what? What's a database system? Provide an organized and efficient way to store this information, manage it and specially retrieve it and manipulate the data and eventually analyze it. That's the purpose of the data is to take the data, analyze it and make business decisions for it. So a database system, the system itself is made of the database management system, the DBMS and the data itself, which are the figures and an application that uses the database. Then we have an application that uses the database. All of those together, they're called a database system. In an organizational database system, data from related application is often pulled together in logically related files that are stored in a database. So database usually think of various tables, various data, but that data is they are, I'm not, you know, I'm not, I'm not showing you this properly, but the point is you have different tables, different data, but they are connected logically. They are connected logically. They can be retrieved in a systematic way. So the database management system provide a way for multiple application to access the data in the database and also to ensure that the data is stored in a managed and efficient and secured manner. Now what are some popular DBMSs or database management system? MySQL, Oracle, Microsoft SQL server and post GRE SQL. Those are some popular ones. Before we proceed any further, I have a public announcement about my company farhatlectures.com. Farhat accounting lectures is a supplemental educational tool that's going to help you with your CPA exam preparation, as well as your accounting courses. My CPA material is aligned with your CPA review course, such as Becker, Roger, Wiley, Gleam, Miles. My accounting courses are aligned with your accounting courses, broken down by chapter and topics. My resources consist of lectures, multiple choice questions, true, false questions, as well as exercises. Go ahead, start your free trial today. No obligation, no credit card required. We need to be familiar with high level and low level of database system integration or integration in a database system. It all depends on the need of the organization. So the level of integration in a database system could be a high one or a level integration, depending on the two. So we need to know what is a high level, what's a low level? A high level integration typically means there are fewer, but larger databases that are used by many different applications. So this is what it is. It's, we have smaller one, fewer, not smaller, fewer, less, but they are large and they are used by many different applications. This approach can help reduce data redundancy because the information is all connected, fewer ones, and make it easier to achieve cross-functional reporting. So you can ask for the data from several places to be put in one report, which means that the data can be accessed and analyzed across different department or function within an organization. So what is an example of a high level integration database system? Is an ERP, and we talked about ERP. We have a one-hole session about ERP. ERP is a software basically that integrates all the business functions, such as accounting, finance, marketing, supply chain, so on and so forth. They're all integrated into one system. And you can bring data from different places all at the same time. Or a CRM. Also, we have a whole session about CRM, where you keep track of your customers, customer relationship management, about your customer habits, about your customer purchases, so on and so forth. Those are high-level integration. We also have what's called low-level integration. This involves smaller databases that are used by specific application or departments, like basically sitting in a silo. This approach can be more suitable for organization with less complex data needs, and with more decentralized decision-making structure. Because again, as I told you, it's silos. So the data basically are in different silos. They are not connected to each other. As I showed you earlier, where all the tables, all the information from different departments are integrated. This may also result in a more data redundancy, because you could have the data in this silo, and you could have a data in this silo. And it's more difficult to achieve cross-functional reporting. So if you want to get a report, you cannot include data from two different places. For example, you could have what's called a departmental database, which is a database only for a specific department. For example, the salespeople, they have their database, and it tracks information about their sales. They want to track information about their sales. Or what's called also standalone database. It means database is not integrated with other databases. For example, in a research lab that use experimental data, might be a standalone database. That could be the case. Also, personal databases. For example, the way you organize your own stuff, your own notes, your own files, it's a form of personal database. But it's not integrated with some other data. This is what we mean by low level of integration. It's not integrated with other ones. Now, you have to keep in mind, there's a range of integration level between high and low, and the appropriate level depending on the company needs. So there's no one answer. This is the right one, the high or the low. And it doesn't mean to be high 100% or low 100%. Could be a mix of the two. More definitions that deal with data and data systems or database systems is data warehouse. What is a data warehouse? It's a large centralized repository of data that's used for reporting analysis and decision making. Just basically a data warehouse where you have so much data. And that's what we mean by data warehouse. The purpose is to integrate the purpose of the data warehouse is to integrate and consolidate data from various sources. So you have sales, you have purchases, you have payroll, you have all sorts of data that are processing transaction and you want to send all this data to this data warehouse. So you can see a unified view of the organizational data. So it typically includes historical data from transaction, transactional system, and other data sources because the transaction did happen. The data organized and structured in a way that support efficient querying. So you can easily search it, analyze it, typically using a dimensional data model, such as star schema. So you could use different ways to analyze this data, search for specific things, filter it, so on and so forth. So data warehouses often use what we call ETL, extract, transform and load processes to extract data from various sources, transform the data into consistent format because that's very important because the data could have a different format. But once it goes into the warehouse, it has a consistent format because you're going to extract, transform and load. If you don't know what ETL is, we talked about it in a different session, and load the data into the warehouse. Once the data is loaded, it can be queried using what's called business intelligence tool, which we'll talk about this in the next session. We have few tools we can use to generate report and perform analysis. So the benefit of a data warehouse include improved decision making, increased operational efficiency, and enhanced business intelligence capabilities, which we'll talk about in the next session. So by consolidating and organizing the data from different sources, we have a comprehensive view of an organization's data, which is, it's a great, it's a gold mine. It's going to give us a lot of information which could help us identify pattern, entrance and enable us to make more accurate informed business decision. So data warehouses are commonly used in many industries such as finance, healthcare, retail, manufacturing. Practically in any industry, where large amount of data need to be managed and analyzed, and basically that's any industry. So we're going to go from data warehouse to something called datamart. I'm going to abbreviate it as DM. Datamart is similar to data warehouse, but it's a subset of a larger data. So we have, this is for example, if this is the warehouse, we could have a datamart. It's a small subset. It's designed to serve a specific business unit or a department. So this is what you want to think about when you're thinking of a datamart. Like a data warehouse, it's a database that used for reporting and analysis, but it contains smaller and more targeted sets of data. So a datamart contain data from multiple sources, including transactional system and other datamarts or the larger data warehouse. So it could bring the data from many different places. The data and a datamart is organized in a way to optimize the need for that business unit or department. Remember, datamart is to serve the business specific business unit or a specific department. And it may use different data model or structure, different model or structure than the larger data warehouse. So it could have a different model and structure than the bigger one because it has a specific need. One of the key advantage of a datamart is the ability to provide faster access because you have smaller data, you can have access to it faster for the business unit by providing a targeted sets of data. It's optimized for their need and datamart can enable more efficient querying and analysis. Also, datamart can be easier and less expensive to develop and maintain than a larger, you're dealing with a smaller size data rather than a larger one. However, it has a potential disadvantage and I'm sure you already guessed it and that's could lead to data silos where we have the data someplace and maybe we need it somewhere else and those two systems are not communicating because a datamart is specific for that department because the data is designed to serve a specific business unit. It may contain data that's not shared or integrated with other datamart or the larger data warehouse. This is a potential disadvantage but this can this can lead to inconsistency inconsistencies in data and difficulty in achieving a comprehensive view of the organization. The best way to do it, data integration and sharing may be implemented to ensure consistency and accuracy across datamarts and the larger data warehouse. So you want to somehow include both. Now, a good example of a datamart, suppose a retail company has a large data warehouse that store data from various sources, sales, transaction, inventory and customer data. The company also have several datamart that are designed to serve specific units such as sales, marketing and finance. For example, the sales datamart, this is only for sales, it could contain a subset of the data from a larger data warehouse such as sales, transaction, customer data and product. Now, as long as the information is coming from the data warehouse, the data warehouse have it but the risk is, let's assume we have a DM here and we have a warehouse, this is the data warehouse. So okay, we are feeding information into this DM but the risk is maybe this DM is also bringing information from another source and this information is not being shared with the overall, that's the risk of data silo. So the sales data might be designed to help the sales department answer the following questions. So this datamart is specifically designed to help them answer what are the top selling product in a particular region or time period? What is the average order value for different customer segment? How has sales performance changed over time? Also, we could have a datamart for the marketing department, could contain a sub data of from a larger data warehouse that's optimized for the need of the marketing department. It may be designed to help marketing department answer questions like what are the most cost effective marketing channel for different customer services at online, if it's online, is it social media, is it Google search, if it's social media, is it Facebook, is it Instagram, is it LinkedIn? What channel? So just help us answer those questions. What is return on investment for different marketing campaign? How much are we investing? How many people are clicking through those campaigns? So on and so forth. How are customer preference and behavior changing over time? So it will be a datamart specifically helping marketing people, just like we have one for sales, we have one for marketing. 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