EIS Component 5: Data in Enterprise Information Systems PDF

Summary

This document discusses the fifth and final component of an Enterprise Information System, which is data. It explains the concept of data, its flow within an organization, different categories of data, and the challenges of managing data. The document also outlines the scope of data within an EIS, highlighting different perspectives.

Full Transcript

EIS Component 5: Data in Enterprise Information Systems ======================================================= \>\> Introduction ----------------- The fifth and final component of any Enterprise Information System is what many consider the most valuable component of them all: "Data". As the sayi...

EIS Component 5: Data in Enterprise Information Systems ======================================================= \>\> Introduction ----------------- The fifth and final component of any Enterprise Information System is what many consider the most valuable component of them all: "Data". As the saying goes, \"Data is the new oil.\" So, let\'s explore how this precious resource flows through an Enterprise Information System. \>\> Defining "Data" -------------------- At its core, data is a collection of facts, figures, and information. Every moment, our species and its inventions are creating trillions of pieces of data, and interconnected devices are constantly collecting, analyzing and sharing this data. Data becomes valuable to an organization when it drives business decisions and operations successfully. And this is why organizations need to CAREFULLY manage their data to extract the most value from it. In a sense, this goal is the entire POINT of building an Information System in the first place. \>\>The Flow of Data in an Organization --------------------------------------- To understand how data is used within an EIS ecosystem, you need to look at the data lifecycle. Here's how data typically flows through an organization: 1. **Step 1: Data Entry:** First, data enters the organization from outside sources. This could be through "active collection" (like surveys or website analytics, for example) or through "passive reception" (like customer orders or supplier invoices). 2. **Step 2: Data Processing:** The incoming data is then filtered and processed to make it useful for the organization\'s purposes. This might involve cleaning the data, formatting it, or combining it with existing data. 3. **Step 3: Data Storage:** Processed data is then stored in the organization\'s databases or other storage systems. 4. **Step 4: Data Usage:** The stored data is used for various purposes across the organization, such as strategy formulation, production planning, sales forecasting, or marketing campaigns. 5. **Step 5: Data Modification:** As the data is used, it\'s often modified or updated. For example, customer information might be updated after a new purchase. 6. **Step 6: Modified Data Storage:** The modified data is then stored back in the system, creating a continuous cycle of data update and storage. 7. **Step 7: Data Distribution:** Finally, some of this data is distributed to external stakeholders, such as customers, suppliers, or government agencies. This lifecycle can repeat itself multiple times, with data constantly being collected, stored, modified, and distributed. Often, businesses will use specialized software or systems to help them manage this process. \>\> Categories of Data in an EIS --------------------------------- Just as there were categories of other EIS components, there are also data categories. Organizations collect, use, modify, and store countless types of data across the categories you see on the screen. Later on in the course, we'll look at examples of typical data collected in each of these categories. \>\> The Challenges of Data Management -------------------------------------- As with any valuable resource, data presents challenges to the organization that uses it. Among these are 1. Data Quality -- Data Quality: This involves making sure that the data an organization uses is ACCURATE, compLETE, and up-to-DATE. 2. Data Security -- Data Security involves protecting sensitive data from cybercriminals and any other unauthorized access. 3. Data Privacy -- Data Privacy involves complying with regulations like the EU GDPR and assuming Corporate Digital Responsibility in regards to the data of customers, employees, suppliers, and other stakeholders. 4. Data Volume -- A rapidly growing challenge in a world of Big Data is dealing with Data VOLUME. The ever-growing volumes of data are overwhelming and need to be managed properly in order to derive useful insights from them. \>\> Scope of Data in an EIS ---------------------------- Large organizations deal with millions of data units every single day. The scope of which data an organization considers to be part of its EIS can vary from narrow to broad -- just like with the other components we've discussed. - In the narrowest sense, only the data an organization explicitly stores in its databases is considered to be part of its EIS. - In contrast, the BROADEST interpretation of EIS Data includes absolutely ANY data collected, modified, used, stored, or distributed by the organization. - Most organizations fall somewhere between these two extremes. When defining the data in your OWN EIS, our opinion is that your filter should be your business processes. All the key data that is required to properly carry out your business processes should constitute the data of your EIS, no more, no less. \>\> Conclusion --------------- As we wrap up our series on the components of Enterprise Information Systems, it\'s clear that data ties everything together. It\'s the resource that flows through the hardware and software, guided by rules and protocols, and ultimately used by people to drive the organization forward. The ability to effectively collect, process, store, and leverage data is what separates successful organizations from the rest. And as future business and informational technology leaders, your understanding of data\'s role in your organization's EIS will be crucial in navigating the increasingly data-driven business landscape. Data isn\'t just the new oil -- it\'s the fuel that powers innovation, efficiency, and success in the modern enterprise. \>\> Types of Data ------------------ Before we dive into how data flows through an organization, let\'s briefly discuss two important distinctions in data types: 1. **Analog vs. Digital Data:** Analog data is continuous and usually represents physical measurements, like temperature or sound level. Digital data, on the other hand, is discrete and represented by a series of ones and zeros in a computer system. 2. **Discrete vs. Continuous Data:** Discrete data can be counted and separated into distinct units or categories. For example, the number of products sold is discrete data. Continuous data, however, can be measured but not counted and can take any value within a specific range. For instance, the weight of a product could be 21.62 grams, 34.74 grams, or any value in between. In modern Enterprise information systems, most data is in digital form and can represent both discrete and continuous information. This is because analog data can be easily converted into digital form for easier storage and analysis.

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