How Microsoft Could Put Real Power Into Power BI
I have a dream that could be brought to fruition by Microsoft. If you want to know what it is, read the article.
I have a dream that could be brought to fruition by Microsoft. If you want to know what it is, read the article.
In this article, I compare two working methods for people doing data analytics. I explain the differences in the approaches and why I have switched my working methods from one to the other.
Four forms of transportation. A trip from hell. A total cost of over $60 bucks when it normally costs about $20 and it took 4 hours longer than usual. Be prepared to learn a few things about the next big things in public transporation.
I love the music of the late/great Harry Chapin. I promised myself way back in the late 1970’s that I would not become the father in the song “Cats in the Cradle”. I appreciate every day I have, and every once in a while I have to remind myself of the beauty, love and inspiration that surrounds me. This is one of those stories.
Working on charitable endeavors has its advantages. Sometimes you get to work with data that is so unexpectedly beautiful that it takes your breath away. This happened to me the other day, so I thought I’d share a new Tableau 10 feature called custom regions to show how powerful this feature can be.
Some articles form quickly, some take a long time to gel. Twenty, thirty, forty drafts of this one have not been enough.
I don’t know how I can possibly explain to people who might be interested in learning how to become better workers with data, but I have tried to do it with this writing. I have tried to explain what has taken me 30 years to realize.
This work is borderline philosophical but it is based in experience and backed by my observations on what works and what does not work when it comes to achieving better data comprehension.
Do you want to drive a high-powered computer? If so, take a 15 minute tutorial on how to use Google Photos to to make great photo collections for your friends and family.
Google is an amazing company that is delivering value to me every single minute of every day. One of the tools I use from them is Google Photos. This application is fantastic and has progressed dramatically over the past few years. The facial recognition accuracy, via deep learning neural networks, is outstanding. I’ve been waiting for the right time to do this project, so now I’m pumped to be able to complete this work.
I thought that is would be interesting to compare Google Analytics to Wordpress Analytics. I expected these platforms would produce nearly identical data. What I found, however, was much different than my expectation. The research for this work will need to continue.
Life is a funny thing. One day you are up, the next day you are down. The only question remaining is this: Will Power BI be able to recover from today’s disaster enough to change my opinion of its value?
Power BI. It even sounds bad to the bone. Over the next couple of days, I’m going to write about Power BI as I learn to use it. I have an open mind, 30 years of Excel experience, and a diverse analytics background. Let the games begin.
If you have ever had an insult thrown in your face, you must read this story. It happened to me and a couple of awesome co-workers about 12 years ago when an audacious and outlandish person we just met suddenly threw direct insults at us like he was throwing darts at a dartboard. Prior to this experience, I never knew people could behave this way.
This is part 3 of 3danim8’s blogging experimental analysis. This article investigates how important speed-burner articles are compared to slow-burners. I can create speed burner articles by writing about Rich Roll, shown above. In contrast to his ultra-endurance performance, these articles fade quickly.
When you present your data to Tableau, it is possible to fool the data interpreter. When this happens to you, you can become frustrated. To learn how to understand and then resolve the problem, just read this article.
Tableau 10 highlighting can be very useful for big data sets. In this example, I show how highlighting quickly reveals differences in consumer behavior by state. Some of the behavioral difference are caused by the geographical settings the consumers live within.