Different Designs For Reporting Data

Check out the solution in the video Different Designs For Reporting Data. Remember that new development videos are posted on Mondays with other videos possibly being posted other days of the week. We look at four different designs for reporting dating in this video and where they may apply. Keep in mind that there are other derivatives of this, but these are the standard four that we'll see in many different situations even when they differ.

Some questions to think about:

  • What reporting style would be best for people who need as many data points as possible?
  • What reporting style would be best for people who need the latest information?
  • What reporting style would be best for people who need deeper information after an initial look?
  • How will our choice affect performance in all of the above?
We must pay close attention to what the client wants, even if their need is not overtly stated. Sometimes we may hear that every data point matters, yet the actual use differs.

Automating ETL
Check out the highest-rated Automating ETL course on Udemy, if you're interested in data.

My students will find this video helpful as a basis for some ideas for their reporting. As I mention, I did not put this video in the course so that I could save room for more meaningful videos. That being said to students, these should provide you with some ideas of useful video styles that you can use depending on the data. In the business world, we generally will build one or two of these four reporting styles I mention in the video. The decision will depend on the client.

Since this post discusses data, one important note I always make to my readers is to avoid the Soviet Paradox. In 1991, the Soviet Union dissolved, even though the Soviet Union was one of the most educated societies in human history. Unlike other countries, the Soviet Union had more data than any other country along with some of the smartest people in the world. Yet the Soviet Union dissolved and ended without existing more than 100 years. Unfortunately, data have little to do with success. In addition, data often divide people even when the people are looking at the same data sets. Be extremely careful with assuming more data are better because data analysis is seldom associated with success.

YouTube | Automating ETL | T-SQL In 2 Hours | Consumer Guide To Digital Security

H2
H3
H4
3 columns
2 columns
1 column
Join the conversation now
Ecency