 Hello and welcome to the session in which we'll discuss the concept of data life cycle or DLC. What is the data life cycle? Well, the data life cycle outlines the steps for managing and preserving data for utilization and repeated use. Why? Why do we need to preserve the data? Why do we need to maintain the data? Well, by implementing a cycle, the organization can enhance the possibility that the data will be functional. It means useful for us, useful for our purpose for our objective and have a longer lifespan. Simply put, we can use it for many years to go. So that's the purpose of the life cycle is to manage the data, preserve it for useful use. For this process, we're going to be using six steps. And usually those six steps, more or less like the COVID six steps of the data life cycle. The first one is plan and design, build and acquire, store, use, share, then archives slash destroy. And this is a picture of it. And if you know anything about Farhad, anything I anytime I have a series of steps, I'll explain each step separately. So I'm going to go over the step of plan and design, build and acquire in the data life cycle. Before we proceed any further, I have a public announcement about my company, Farhad Lectures.com. Farhad 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. Starting with plan slash design, which is step one of six, well, as those terms are very helpful, plan design, it means that that's the first step. I'm planning and designing the whole process. And that's an important step because it's going to lay the foundation for the entire data management process in order to achieve whatever my desired goal is. And the first thing I have to determine is what is my objectives? Why do I have this data? I have to clearly define the goals and objective of the data collection. You don't collect data to hoard the data. You want to use it for something. You want to use it, for example, for improving business operation, making data-driven decision or conducting research and reporting. So the first thing is, what is the objective? Why am I doing this? The second thing is what type of data am I dealing with? Identify the types of data that is required to reach my objective in step one. This includes determining the attributes, such as demographic information, transactional data, time series, et cetera, whatever I need. Well, the third thing I have to decide on in this plan slash design is how am I going to collect this data? Deciding on the method of data collection. I can use surveys, experiment, purchase the data from a third party. This could be acceptable as well. The method choosing depending on the type of data I am using and what is my objective. Then I have to have a management plan. Well, how am I going to manage the data? Develop a data management plan that outlines the procedures for storing, protecting, and preserving the data for future use. This plan also should address security, privacy, ethical consideration. Here, I'm just putting down the map. I'm going to talk about these steps a little bit further down the road. And last is in this step, I want to examine which method, which analysis method am I going to be using to extract the meaningful insight, to extract meaning from the data. Because the data, if I cannot obtain the knowledge through analysis, it's useless. So this could include statistical analysis, machine learning. I could use data visualization technique, as well as other steps. In step two, which is build slash acquire, in this step, data is either built or acquired through various sources. The steps involve the following activities. One is data collection. Now I am either building the data from my own transaction or remember I can acquire it. So data collection. Depending on the methods decided in the plan design step, remember data can be collected through survey, experiment or purchase. It's important to ensure that the data collected either way, either internally or externally is accurate. That's important, relevant to what we want to do, and of high quality. Well, after we obtain the data, we don't accept it as is, we're going to do some data cleaning. Once the data is collected, we clean it, we remove any errors, inconsistencies or duplicates. And if you ever work in data, whether for academia or in the real world, you will know that data always need cleaning. And that's, that's a tough, that's a tough task sometime. Also formatting data formatting. After cleaning, you will need to format the data to be inconsistent structure that can be easily processed and analyzed, especially if you are using a special software or some sort of a proprietary software to analyze the data. This could involve converting data into specific format, such as spreadsheet or a database or some other proprietary format. Then you have to verify the data. The final step in this is to verify the data, the accuracy of the data. This includes checking for any discrepancies, any missing values, any outliers, whether you want to keep them or not, they may affect the results of the analysis. In step three, we're going to determine how to store the data. So the store step involved storing the data collected in step two after we collected in a secured location. Now the storing could be physical, could be, could be computerized. This step is important for protecting and preserving the data for future analysis and decision making. The first thing is we have to determine the data storage. Well, it can be stored in a physical location, like a storage room or digital location, such as a database, which is computerized on a server somewhere. The storage method, either method chosen, whether it's physical or digital, should ensure that it's secure and accessible. So it's always secure and accessible. Then we have to make sure the data is being backed up. Regular backups of the data should be taken to ensure that the data is not lost in case of disaster or technical failure. Well, also we want to make sure it's secured. That's for sure. We talked about data storage at the beginning. How can we secure the data? Well, if it's a physical data, well, unauthorized access, locked doors, we want to protect it from theft, loss, fire, so on and so forth. Now, also if it's digital data, data encryption, access controlled, disaster recovery procedure. Also, we might have to deal with data privacy here because sometime organization must ensure that the data is stored in accordance with data privacy regulation inserting guidelines. Step four is use of the data. In this step, the data stored in step three is used to analyze and analyze and used for decision making. Here is where we're going to extract meaningful insight and knowledge from the data to support whatever we are doing, our objective, the business decision, improving operation, so on and so forth. First is data analysis. The data collected now goes through some sort of an analysis. It could be statistical analysis, machine learning, data visualization technique, or to extract the whole purpose extract, extract meaningful insight, just get more information. After we get the information, we need to interpret this information. And this could be, this could be, we could misinterpret this information. So this includes understanding the relationship between variables, identifying trends and making prediction. And this should be, this is I would say the most important. If you have the data, the software run it, everything is good. The data is good. The software is properly working. Now, do you interpret the data properly? And after you interpret data, let's assume you interpret it properly. Are you making the right decision after you interpret the data? So the insight and knowledge gained from the data analysis are used to make decision in various areas of the organization, such as product development, marketing, operation, whatever that decision is. Also, we can use visual representation or data visualization to do what? To gain more insight. It could be graphs, charts, and I have several lessons about data visualization, then they can help communicate the information, the insight, the knowledge gained from the data analysis to the stakeholder, to whoever is interested in this data. Sharing the data in this step, the results of the data and decision are shared with stockholder and archives for future use. So data sharing, the result of the data, like who are the stakeholders? Who are they? We're looking at management, employees, people who are making decision could be customers. If it's some good news about our product, partners to provide them with valuable insight, this can be done through presentation, email, other form of communication. Also, we want to make sure the data is accessible and the results are accessible in case somebody would like to double check them. This could involve implementing a data management system and tool to provide secure access to the data. Also, we want to archive this data. Archive means what? It's somewhere for future use, should be archive for future reference and use. This may involve storing the data in an archive database or physical location. Also, we want to have some sort of a data retention policy. How long do we keep it? We should establish how long we should keep the data. Sometimes it's by law. We have to keep it a certain amount of time, like financial information. Sometimes we want to also do our own data retention policy, right? Our own data retention policy. So we can keep it to make sure that the data is not deleted, destroyed, and needed when necessary. Last thing is archivings or slashing, destroying. Sometimes we might have to destroy the data. The final step of the cycle is destroying the data that's no longer needed. This step is crucial in protecting the privacy and security of individual and the organization. And sometimes we do so in compliance with data privacy regulations and guidelines. We cannot keep the data we have to destroy it. So data deletion is important. Which data do we delete? The one that we don't need? Or we want to prevent any unauthorized access to it, misuse, or sometimes it's required by law or industry. We have to know something about data sanitization. Data that contains sensitive information should be sanitized to prevent the recovery of the data. Basically destroy it and don't let anyone be able to do what? Recover this data. This may involve wiping the data from storage system or physically destroying the storage devices. Also we want to make sure in the destroying process we are in compliance. In compliance with what? Organization must ensure that the destruction of data is done with data privacy regulation and guidelines such as the general data protection regulation, the GDPR, we're going to talk about this much more later on, and HIPAA, the Health Insurance Portability and Accountability Act. If you're studying for the CPA exam, you're going to have to know a thing or two about those two guidelines and regulations. Also we want to document the destruction process to make sure we have evidence that we did what we did. Documentation of the destruction should be kept to ensure that the data was destroyed in a secure and compliant manner. As accountant, documentation is important for us. We want to keep track of everything that's going on. What should you do now? You should go to Farhat Lectures and look at questions such as this one that's going to help you understand the concept. What's the first step in the data life cycle? Hopefully you know that the first step is to plan and design because that's important. You don't start the process until you have a proper planning, proper design of the system. Again, on my website, farhatlectures.com, you will have access to additional questions plus solutions that's going to help you not only understand but be able to learn the information, whether you are studying for the CPA exam, the CMA exam, CISA, data life cycle or taking an accounting information system course or you are studying for your certified internal auditor. It's all going to help you just basically ingrain this material in your brain that you'll be able to answer those multiple choices. The answer to see the first step is the data life cycle, which involves defining the goals and objectives of the data collection and analysis and determining the type of data I'm going to be working with, the method of collecting and storing, basically drawing the map for the whole data life cycle. Good luck, everyone. Stay safe, invest in yourself, invest in your career.