In this video learn the importance of identifying and developing data dictionaries with your work.
- [Instructor] Let's talk about a data dictionary. They're great when they exist, but they don't always. When I went to work in corporate America, I went in and asked all the right questions, where are the tables, what are the fields? How are they all related, where's the data dictionary? And the response I got was, "Go see Bob." Who's Bob? Bob's already figured out where all the data is and how it's related. So every situation you work with will be different. A data dictionary maps the data, and it defines the data for us, and oftentimes it will show us the relationships.
If we don't have a data dictionary to show us the relationships, then we have to figure the relationships out by either using other reports, or talking to the Bobs of the world. If you design your own databases, then I'll encourage you to create your own data dictionary, let me show you a simple way to do that. For this example, I'm using our database. I'll go to the database tools, and in the analyze section, I'll choose database documenter. I'll go to our tables, I want to learn more about our login, our users, our videos, and our video sessions.
Of course if this was a brand new database for me, I would choose select all. I'll also go to the current database, and ask for a document of the relationships. I'll go ahead and click okay, and it'll generate a report for me that gives me every detail about every table, and every field. It'll give me all the properties of that field, and any information I might need to know about the data. I'll go to my last page so I can see the relationships. Again, having a relationship schematic that shows you how tables are tied together can save you a lot of time learning how to actually relate your tables.
I never fail to ask for the data dictionary. It definitely helps to have a guide through the data, and if you create your own datasets, be sure that you create the data dictionary.
- Define data analysis and data analyst.
- List roles in data analysis.
- Explain data fields and types.
- Define syntax.
- Explain how to interpret existing data.
- Describe data best practices.
- Repurpose data.
- Create a data dictionary.
- Compare and contrast linking versus embedding charts and data.
- Build pivot charts with slicers.