5 Exercise: Project Progress - Video Tutorials & Practice Problems

<v ->All right.</v> Now, let's get back to your work on your project. So let's focus on what the next steps are to bring your project to reality. So we talked a lot about data. What's the data like for the project you have to do? So are there IT problems that are gonna get in the way of you being able to deliver on your AI project? So do you have the data you need? Is it trapped in silos? Do you need to cross those silos or do you have a data warehouse or a data lake or data marts or some combination of all of them? Where is your data right now? How do you get access to it? Is that the place you have to start to solve your problem to even get the data prepared or are you luckier? And the data you need is actually in shape, and you can actually start using it right away. So as you're focusing on your project, you have to make sure that you know where the data is that you need. Now, is there something you need to do to make the data ready for your AI project? So even if it is accessible, does it have to be transformed in some way? Is there something you need to do to be able to get access to it? And if there is kind of a waiting game that you have to play while you are waiting for the data to be ready for your AI project, what kind of progress can you make before it's ready? Are there things you can do to upgrade processes? Could you be taking some test data, and modeling with it even if you don't have access to all of the data yet? What kinds of things can you do in parallel while you're waiting for your data to show up in the form you need? Now I wanna ask, can you improve your data? So what do you already do to make sure that the data is high quality? So how do you collect the data today? Is that good enough? Are you getting really accurate data? Do you know the data's accurate? How can you prove or tests that it's accurate, and how do you make sure that you're storing what you collected? So how are you doing all of those things now? So that's the first thing is to kind of take inventory of what you're doing now. Next, what I want you to do is ask yourself, "What could you do?" I want you to brainstorm all of the ways you could collect data, what are different ways you could get better data? What are ways that you could get more data? Are there things you could do to make your data more accurate? How could you improve the quality, and the quantity of your data, and kind of as a motivator, what would that mean to you? What would the impact of more data be? What would the impact of more accurate data be? What is the business impact of making these improvement to your data that you're contemplating? Next, I want you to think about how marketing data ought to be used? So. Are you proud of the way you're using data about your customers? How are you using that data? Is there something that if your customers knew you were collecting this data, and they knew what you were doing with it, would they be okay with that? Because that's something you really need to think about. And is it the best thing for your customers? Not just whether it's the best thing for you. And is it the most effective for your marketing? If customers found out about it, would that actually be bad for your brand image? And so what if anything, should you change? Should you make any changes in the way you're collecting data, especially data about your customers. So let's summarize what you're going to do in this part of your exercise for your project. So you're gonna continue working on your plan first by identifying any kind of data infrastructure problems that you might need help from the IT team to resolve. So what is it that you need help with to get your data in the accessible shape that you needed it to be? And I want you to determine if you really have all of the data you need, could you be collecting more? Do you need data that's more accurate than what you have now. So how can you assess the quantity, and the quality of the data that you have on hand? And last, I want you to think about what your policies are. Are you doing the right thing by your customers? Are your policies protecting your customers? Would your customers be okay if they knew what you were doing with their data? So that's it for this project exercises, and I'll see you back here.