If one wants to get better at public speaking, there is no better way of doing that than to practice. Personally I think the best experience are where you are giving a talk to an audience, as that seems to improve my performance better than speaking to a mirror. I also read a lot of blogs regarding public speaking hoping to learn some tips. One of the things I know not to do, but I do anyway is add in those nothing works like “um” or “ah”. I know that I shouldn’t. I also know I shouldn’t procrastinate, but I find myself doing that sometimes as well. Sometimes you have to figure out ways to make yourself do things, like making a deadline so you can hit it or putting yourself in a situation where you are asking people to criticize your speaking skills. If the goal is to get better, I think you have to move yourself out of the comfort zone you may be occupying in order to make that happen.
Speaking as a Competitive Sport
For those of you who haven’t heard of PASS Speaker Idol, which is understandable since it has only been around for a year, it is a competition where 12 people compete by giving a five minute technical speech on a topic of their choice in front of an audience and a panel of judges. There are four rounds of competition, and 3 people will advance to the finals. The winner will get to speak at PASS Summit next year on a topic of their choice. Here are all the competitors, and the competition times. If you are at PASS Summit hopefully you can attend some of the sessions.
Wednesday (3:15pm – 4:30pm)
- Todd Kleinhans
- William Durkin
- Ginger Grant
- Ed Watson
Thursday (4:45pm – 6:00pm)
- Rob Volk
- Amy Herold
- Bill Wolf
- Wes Springob
Friday (2:00pm – 3:15pm)
- Luciano Caixeta Moreira
- Ronald Dameron
- Theresa Iserman
- David Maxwell
You will notice I am going on the first day. I’ve decided to give a talk on SSIS, and figured out how to talk for only five minutes. I’ve been practicing, run through my demo, and took pictures of the demo in case it doesn’t work that day. After reading everything I could find on what happened last year, I’m feeling pretty good about my chances. Many thanks to Rob Volk t | b on all of the great information he put out about last year’s competition. A big thanks also to Denny Cherry t | b for not only starting the Speaker Idol contest but doing it again this year.
Winning Good Information
Regardless how the competition turns out, I will win information from those people who watch me speak how I can be a better speaker. Hopefully they won’t catch me saying “um” but I plan on learning how to apply some of the other things I learn to improve my talks the next time. If you are interested in where I am speaking next time, please take a look at the Engagements page on my blog where I list everywhere I have or will be speaking. One of the places I will be speaking is at the PASS Business Analytics Conference in May. I am thrilled to be able to talk about Implementing Successful Data Analytics Management Practices for two hours. After this week, I’m sure that presentation, and others will be even better than they would be. If you want to know how Speaker Idol turns out, please subscribe to my blog where I will be letting you know how it all turned out.
Data aficionado et SQL Raconteur
Microsoft previewed Cortana Analytics in July 13, 2015, and since then, they have published a lot of information on their site about it. Based on what I’ve seen on the internet, there appears to be a lot of confusion as to what Cortana Analytics is. This is completely understandable when you consider the number of different products the name Cortana has represented for Microsoft. My favorite is the image with the picture of a blue girl, which is from the Xbox game Halo 3. A video game was character was the first place Microsoft used the name Cortana in 2007. At the Microsoft BUILD Developer Conference in April 2014, the name Cortana was used for the Microsoft version of the Apple’s Siri phone application. If you are interested in hearing about it, I’ve included a link to the Channel 9 video here where they talk about Cortana. Finally, a year later Microsoft comes out with a product called Cortana Analytics. No wonder people are confused.
Cortana Analytics is not a Product
Cortana Analytics: the bow tying different applications together
To help bring clarity to what Cortana Analytics is and is not, I wanted to start out with what I think is the most confusing point. Cortana Analytics is not a product, but a name given to a bunch of other applications which are designed to work together. In essence, Microsoft tied a bow around a bunch of applications and called the bow, Cortana Analytics. Here’s an example scenario. Start by sending water meter data from the physical meters to the cloud, where you aggregate, analyze, store and end up with a Power BI application on your phone showing you a visualization of some aspect of the data. To make this happen from a technical perspective using Microsoft’s tools, one would need to probably create an Event Hub, run a Streaming Analytics Process, use Data Factory to call a Machine Learning experiment, migrate the data to an Azure Storage account of some kind and then create a Power BI report to be sent to your phone. All of that, is Cortana Analytics. It is not one product, but a big bow tying all of the applications they have designed to work together under one name. Power BI is part of it. On that note, I recently saw Microsoft do a demo with Power BI where they integrated the Cortana-phone like functionality of talking to Power BI and it displayed the information it was asked. I have no idea when this will be released, but it sure was a neat demonstration. In this demo, they mentioned they were adding Cortana funcationality to Power BI, which really didn’t help the confusion level with the name.
Cortana Analytics Web Presentation
I recently recorded a video presentation of Cortana Analytics where I described in greater detail the components which make up Cortana Analytics and how they work together. That video is available here. As I am working more with the components which make up Cortana Analtics, such as Machine Learning and Power BI, I will definitely be devoting more blog post to the topic, so please subscribe to my blog if you are interested in learning more about it.
Data aficionado et SQL Raconteur
I can’t remember when I first heard this axiom, but it was sometimes when I was in elementary school. At the time I thought it was the silliest thing I had ever heard. After all, a tree falls regardless of my opinion or recognition. The tree is going to do what it is going to do and my interaction with it is meaningless. The sentence was one of those things, which is best described by Lewis Black as something which “…causes your brain to come to a screeching halt“. It made no sense to me, until later.
Why tell the world?
One of the reasons for this blog is pretty much the same as everyone else’s blog, namely, I wanted to write down what I know. So I got a high-speed internet connection (such as those available through optimum internet plans), created a blog, and started writing. There are a number of reasons for writing, to ensure I remember things, to find a link to something that works, to give back to those who have helped me by helping others or the desire to share something over the internet. The reason for informing the world varies. Displaying what your interest or expertise is can lead to opportunities where people ask you to do things where you have demonstrated your knowledge about a topic. As a member of a speaker selection committee, I know being able to find out about a speaker’s expertise made me recommend them or not.
Falling Trees Making a Sound
If someone gives a lot of talk and no one knows it, will they be asked to speak again? If you work with a real expert at a certain task and no one knows it, will this person be able to command a very high rate of pay at a future job? If your employer does something for you, such as sending you to a technical conference and you tell the world, are they more likely to send you to another in the future? While I am sure the answers to these questions can be universally answered by the consultant mantra “It depends“, for what it’s worth, perhaps letting the interwebs know will increase your chances of being asked or increase your value. If you are also one of those people who hate self-promotion, a blog may seem rather braggadocios. It’s also one of the few ways you have of telling people what you do.
The World is the Forest
It’s a big world out there. At some point, someone is going to ask you what you do. Sure you can practice an elevator speech. But when someone is sitting around surfing the internet and trying to find out about you, isn’t it best that you be the one who lets them know what you’ve done and what you know? Now, I don’t know if having a blog has helped me do anything I have done or not, but I continue to post. The world is a forest, and when I chop down a tree this is where I will make a sound.
Data aficionado et SQL Raconteur
An Example of Machine Learning: Google’s Self-Driving Car
Often times when I give a talk about machine learning, I get a question about what is data mining and what is machine learning, which got me to thinking about the differences. Data mining has been implemented as a tool in databases for a while. SSIS even has a data mining task to run prediction queries on an SSAS data source. Machine Learning is commonly represented by Google’s self-driving car. After reading the article I linked about Google’s car or study the two disciplines, one can come to the understanding that they are not all that different. Both require the analysis of massive amounts of data to come to a conclusion. Google uses that information in the car to tell it to stop or go. In data mining, the software is used to identify patterns in data, which are used to classify the data into groups.
Data Mining is a subset of Machine Learning
There are four general categorizations of Machine Learning: Anomaly Detection, Clustering, Classification, and Regression. To determine the results, algorithms are run against data to find the patterns that the data contains. For data mining the algorithms tend to be more limited than machine learning. In essence all data mining is machine learning, but all machine learning is not data mining.
Goals of Machine Learning
There are some people who will argue that there is no difference between the two disciplines as the algorithms, such as Naïve Bayes or Decision trees are common to both as is the process to finding the answers. While I understand the argument, I tend to disagree. Machine learning is designed to give computers the ability to learn without specifically being programmed to do so, by extrapolating the large amounts of data which have been fed to it to come up with results which fit that pattern. The goal of machine learning is what differentiates it from data mining as it is designed to find meaning from the data based upon patterns identified in the process.
Deriving Meaning from the Data
As more and more data is gathered, the goal of turning data into information is being widely pursued. The tools to do this have greatly improved as well. Like Lotus 123, the tools that were initially used to create machine learning experiments bear little resemblance to the tools available today. As the science behind the study of data continues to improve, more and more people are taking advantage of the ability of new tools such as Azure Machine Learning to us data to answer all sorts of questions, from which customer is likely to leave aka Customer Churn or is it time to shut down a machine for maintenance. Whatever you chose to call it, it’s a fascinating topic, and one I plan on spending more time pursuing.
Data aficionado et SQL Raconteur