1.2 Introduce the Data Lifecycle - Video Tutorials & Practice Problems
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<v ->Every Excel workbook exists</v> within a larger data ecosystem. This includes other workbooks that may be from other users and database systems, such as ERPs, CRMs, and other operational systems. Each of these systems contains organizational data and is important to understand how all these relate in the larger data lifecycle. Let us talk about Excel in the context of the data lifecycle so we can better understand its role as a technology in the lifecycle. In this course, we have a theme of framing the data lifecycle using people, process, technology, and data. For example, let's use an invoice management process. In the middle, we have Excel as our technology. And within Excel, we have data. We then have data producers, which put data into systems. Data producers often acquire data, and they may perform additional data entry manipulation steps to support that process. Data consumers often use data for reporting on a process or to integrate data into other processes. To manage these processes, we often follow a consistent pattern. Once again, let's use the invoice management process as our example. Maybe we need to get timesheet data into Excel from a time-tracking system. For this, we would import data. Next, maybe we need to add an hourly rate so we can determine the amount to bill. This would amount to some data entry in some form or fashion. Next, we may do some data validation, answering such questions as, did we enter too many hours or not enough? And does this invoice seem reasonable after all of my calculation steps have occurred? With the above steps completed, we can generate a timely and accurate invoice that we're confident in, and this would be a reporting process. And last but not least, we would then look to distribute our work to our clients. In thinking about the whole picture now, we may use the invoice manager process spreadsheet to track when invoices are due and if they're paid or not. There's a whole lot more we can do here, but let's keep this example simple to start. So how do we go about creating reliable data as a producer? It all starts with people. We need to train our people on how to use data capture and management tools effectively from a tool perspective. This is training people on the technologies. Next, we need to build good processes using those technologies that are easily understood and followed. Our end goal of all this is high-quality data. And we can achieve that if we manage the above interactions well. With confidence in the producer processes, consumers can pick up on data that is timely and accurate to help paint a picture of business performance and do it with confidence. Keep in mind that often the root cause of bad data is some deficiency in the data producer fees of the data lifecycle. With that, the focus for the majority of my career has really been around data consumption, and I was really exposed to the typical data producer activities. But recently, I've been spending more time as a data producer within my own organization. I believe that bringing a ground-up focus to people, process, technology, and data, and embedding into the fabric of our company will help us become a more data-driven organization that can make timely and accurate decisions with confidence. <v ->Complimenting Chris's experience,</v> most of my career I have worked with and supported data producers within operations, finance, financial and production accounting, treasury, supply chain, and several other business functions. I have witnessed firsthand how trained data producers and optimized data flows, including manual data entry, can simplify downstream data preparation activities and significantly enhance the analytical capabilities for data consumers. I have also noticed that although Excel is a core component for data analysis, in most companies, it is not one of the primary focuses for their skills training and development programs. During this live lesson, we will leverage our experience as data producers, modelers, and consumers to highlight some fundamental Excel capabilities and best practices relevant to a typical data analyst role.