Requirements, Data Modeling, and Planning
You begin your journey by assessing the requirements and data for your project. The use case will be a fictional scenario, but everything will be built using real data from the Federal Aviation Administration’s (FAA) Wildlife Strike Database. The data is real, the topic can be understood by anyone, and the findings within the data are interesting and fun. According to the FAA’s website, about 47 animal strikes are reported daily by aircraft. These incidents can damage airplanes, potentially endanger passengers, and negatively impact wild animal (especially bird) populations.
For the use case, you have been assigned to provide your leadership with tools to do an interactive analysis of the FAA Wildlife Strike data, find insights about factors that influence the incidents, and also make predictions about future wildlife strike incidents and the associated costs. The primary goal of your project, predicting the future impact of FAA Wildlife Strikes, will require building some Power BI machine learning models.
Before uploading data to Power BI’s machine learning (ML) tools, you’ll need to create tables of data that will train the ML models. There is an old saying about data and analytics: “Garbage in, garbage out.” Software as a Service (SaaS) machine learning tools are easy to use, but you still need to feed them good-quality curated data. Identifying the right training data and getting it into the right format are crucial steps in an ML project.
This project will encompass data exploration, data transformation, data analysis, and additional downstream data transformations before you begin working with Power BI ML tools. You are already an experienced business intelligence (BI) professional and Power BI user, and now you are ready to take your skills to the next level with ML in Power BI!
Power BI supports connections to source data in many different formats, ranging from relational databases to unstructured sources to big flat tables of raw data. Countless books have been written about the best ways to structure and model data for different use cases. Rather than dive into the specifics of data modeling, for this book, we will begin with two simple assumptions:
- Most of the time, a star schema design will provide the most efficient storage and query performance for business intelligence data models
- Basic ML models, such as the ones you will build in this book, are usually created with a flattened table
Just to be clear, not every solution will follow these assumptions. Rather, these assumptions are generalizations that can provide you with a starting point as you approach the design of a new data model. Quite often, there will not be a perfect answer, and the optimal design will be dictated by the types of queries and business logic that are generated by the end consumers of the data model.
If you’ve never heard the terms star schema and flattened data before, don’t worry! The book will progress at a pace that is intended to help you learn and will also stay at a level that makes sense when you review the FAA data. Let’s browse the FAA Wildlife Strike data and decide upon the best data modeling strategy for your new project!
In this chapter, we will take the following steps so that you can understand the data, think through how it will be used, and then formulate a preliminary plan for the data model:
- Reviewing the source data
- Reviewing the requirements for the solution
- Designing a preliminary data model
- Considerations for ML
