Analytics and its place in the NFL... - 04/06/19 06:06 PM
I have to preface this thread with a couple of things...
***THIS IS MEANT TO BE EDUCATIONAL ONLY***
***THIS IS NOT MEANT TO BE AN ARGUMENT PERTAINING TO THE BROWNS USE OF ANALYTICS*** btw this will be put on every post I make in this thread...lol
If you are here to argue...start your own thread. The purpose of this thread is to explain what Analytics is, in a more in depth forum than it could be in an obscure post within an obscure thread. The sense I get from posts is that Analytics is misunderstood as an entity and therefore the "use" of Analytics is misunderstood. I hope to shed some light on what Analytics actually is and then offer some hypothetical scenarios as to how it can be used in today's NFL. I need to reiterate that they are hypothetical but in my estimation it is reasonable to assume that someone in the NFL is using Analytics in the same manner. But most likely they are more advanced than I can hypothesize.
This is going to be a multi part thread/project. Massive books have been written on the subject of Analytics and I am not going to be able to fit it all into a single post...LOL So if you have questions, definitely ask...but also know that There will be further posts. In fact your questions may help steer the direction of those next posts...
Just a little about me. I have been working with computer data for 22 years. I'd like to say I have worked with 2 of the largest data sources you can find. Land/Title and Healthcare. I have created a Database Application that allowed my company to be the first in the Industry to serve the entire State of Ohio, and now serves the entire country. I have created Analytical Reports in the Clinical arena that led to process changes. In one case, the changes saved my Hospital over $500,000 a year. In most cases the reports I have written led to process changes that saved money. I currently administer reporting systems as well as monitor and support data ETL's (Extracts, Transfers, and Loads) for the top Hospital in my State.
With that said, I am not the end all, be all concerning the topic of Analytics. I am a Data, Systems, and Reporting Analyst, not a Data Scientist. But I work Daily with Data Scientists and Data Engineers in making sure our Hospital is provided the data Insights it needs to both operate today as well as prepare for the future.
I will NOT have all the answers. BUT, if I don't know, I will find out. And as most of my experiences with Analytics have been within the Healthcare field, most of my examples will lean toward in that direction. I ENTHUSIASTICALLY encourage those on this board that work in Analytics, or have worked with the results of Analytics, to post their experiences and take on roles of teachers in this thread as well.
I have to say it one more time...If you are here to argue about the Browns specific use of Analytics, please take your argument to another thread. I will ask Moderators to delete posts that do not contribute to the purpose of this thread.
With that said...Let us Begin!!!
********
The purpose of Data Analytics is to provide INSIGHT to those who make decisions, with, indirectly, having the ultimate goal of improving business performance. That is a mouthful.
LOL
Insight, you will hear that word often. So lets start with the straight definition...
noun
1. an instance of apprehending the true nature of a thing, especially through intuitive understanding
2. penetrating mental vision or discernment; faculty of seeing into inner character or underlying truth.
Psychology .
an understanding of relationships that sheds light on or helps solve a problem.
(in psychotherapy) the recognition of sources of emotional difficulty.
an understanding of the motivational forces behind one's actions, thoughts, or behavior; self-knowledge.
So the aim of Data Analytics is to use data to shed light and understanding of a situation.
Analytics rely heavily on 3 things. Good Data, Good Questions, and Good People to work the data.
Good Data...
We have a term we use all the time. Garbage in, Garbage out. Quite simply if you do not have Good data as a foundation, it is impossible to expect beneficial results from processing that data.
Human error is inevitable. Nurses and Doctors may fat finger a number or name. Receptionists see someone with the same name and mistakenly use that record instead of a new one or correct one if it exists. It happens. But if a diagnosis is under the wrong patient, how can we expect to have accurate results when looking for patients with a specific diagnosis history? We take many measures to reduce this possibility. We have a team of people to correct these instances. In any case, we take great efforts to ensure that we have Good Data as a foundation of our Analytics.
Good Questions
You can have the best Data Possible and the Greatest Analytics Team on the face of the Earth. But if you don't ask the right questions, The data results are useless towards the ultimate goal of making great gains in business performance. For example, in the Hospital, this is the difference between having a Business expert and a Doctor asking the questions about saving money on the treatment of a specific infection. Both have extensive experience within their own field. But the questions they ask are going to be (for lack of a better term) biased towards that field. The business man asks questions towards the bottom line money. While the doctor asks questions towards the treatment process. Both have a piece of the question. But as they are just a part of the story, neither are asking a Good question. In order to get good INSIGHT into the situation you need to take into account both the businessman and the doctor. That question becomes very complex, very quickly. And the answers to that question are even more complex.
Good People
It takes a large number of people to produce the results needed. We have people who insure data integrity. We have people who model the data, we have people who move the data around. We have Data Scientist to create the algorithms needed to produce the answers. And we have people that support the hardware and software of all of these systems. Lastly we have Report writers who take these results and present them in a meaningful way. Anyone of these people can drop the ball, and if they do, the results are useless. So it is important to hire the right people. with the amount of data we are working with...it is not a job for just a couple people. This is not a small business Database. It is not a large business database.
To give an idea of the amount of data we work with, take this into consideration. The Library of Congress is the largest library in the world. It is one of the singular greatest collection of human knowledge on the face of the earth. The entire Library of Congress is reported to fit on approximately 10 to 20 terabytes. To go a little more granular. One bit is either a 0 or a 1. To make one character in this post requires 8 bits. 8 bits is 1 byte. 1024 bytes is 1 kilobyte. 1024 kilobytes is 1 Megabyte. 1024 Megabytes is 1 Gigabyte. And finally 1024 Gigabytes is 1 Terabyte. So if you take 1024 and raise it to the 4th power you get a number with 13 digits.(That is Trillions)Add another digit as you are multiplying again for 10-20 Terabytes in the library of Congress. That is a lot of data...So when I tell you that my hospital is performing analytics on data measured in Petabytes...You can get a better idea of the task that is being performed. What is a petabyte? A petabyte is 1024 Terabytes...so now we are at 1024 to the 5th power which is a 16 digit number (Quadrillions)
Much like a fire that relies on fuel, spark, and oxygen. If any one of the items spoken of is taken away...Analytics fails. Good Data, Good Questions, and Good People...Analytics is a three legged table and if any leg is taken away...the table falls.
I hope that is a good introduction and I hope it both gives you a little understanding of the purpose of analytics, the sheer scale of analytics, and what it relies upon to be successful. I also hope that it raise a myriad of questions within you to add to the discussion. In another post, I mentioned the different types of Analytics and how Analytics differs from Statistics. I think that will be a good stepping point into my next post.
***THIS IS MEANT TO BE EDUCATIONAL ONLY***
***THIS IS NOT MEANT TO BE AN ARGUMENT PERTAINING TO THE BROWNS USE OF ANALYTICS*** btw this will be put on every post I make in this thread...lol
If you are here to argue...start your own thread. The purpose of this thread is to explain what Analytics is, in a more in depth forum than it could be in an obscure post within an obscure thread. The sense I get from posts is that Analytics is misunderstood as an entity and therefore the "use" of Analytics is misunderstood. I hope to shed some light on what Analytics actually is and then offer some hypothetical scenarios as to how it can be used in today's NFL. I need to reiterate that they are hypothetical but in my estimation it is reasonable to assume that someone in the NFL is using Analytics in the same manner. But most likely they are more advanced than I can hypothesize.
This is going to be a multi part thread/project. Massive books have been written on the subject of Analytics and I am not going to be able to fit it all into a single post...LOL So if you have questions, definitely ask...but also know that There will be further posts. In fact your questions may help steer the direction of those next posts...
Just a little about me. I have been working with computer data for 22 years. I'd like to say I have worked with 2 of the largest data sources you can find. Land/Title and Healthcare. I have created a Database Application that allowed my company to be the first in the Industry to serve the entire State of Ohio, and now serves the entire country. I have created Analytical Reports in the Clinical arena that led to process changes. In one case, the changes saved my Hospital over $500,000 a year. In most cases the reports I have written led to process changes that saved money. I currently administer reporting systems as well as monitor and support data ETL's (Extracts, Transfers, and Loads) for the top Hospital in my State.
With that said, I am not the end all, be all concerning the topic of Analytics. I am a Data, Systems, and Reporting Analyst, not a Data Scientist. But I work Daily with Data Scientists and Data Engineers in making sure our Hospital is provided the data Insights it needs to both operate today as well as prepare for the future.
I will NOT have all the answers. BUT, if I don't know, I will find out. And as most of my experiences with Analytics have been within the Healthcare field, most of my examples will lean toward in that direction. I ENTHUSIASTICALLY encourage those on this board that work in Analytics, or have worked with the results of Analytics, to post their experiences and take on roles of teachers in this thread as well.
I have to say it one more time...If you are here to argue about the Browns specific use of Analytics, please take your argument to another thread. I will ask Moderators to delete posts that do not contribute to the purpose of this thread.
With that said...Let us Begin!!!
********
The purpose of Data Analytics is to provide INSIGHT to those who make decisions, with, indirectly, having the ultimate goal of improving business performance. That is a mouthful.
LOL
Insight, you will hear that word often. So lets start with the straight definition...
noun
1. an instance of apprehending the true nature of a thing, especially through intuitive understanding
2. penetrating mental vision or discernment; faculty of seeing into inner character or underlying truth.
Psychology .
an understanding of relationships that sheds light on or helps solve a problem.
(in psychotherapy) the recognition of sources of emotional difficulty.
an understanding of the motivational forces behind one's actions, thoughts, or behavior; self-knowledge.
So the aim of Data Analytics is to use data to shed light and understanding of a situation.
Analytics rely heavily on 3 things. Good Data, Good Questions, and Good People to work the data.
Good Data...
We have a term we use all the time. Garbage in, Garbage out. Quite simply if you do not have Good data as a foundation, it is impossible to expect beneficial results from processing that data.
Human error is inevitable. Nurses and Doctors may fat finger a number or name. Receptionists see someone with the same name and mistakenly use that record instead of a new one or correct one if it exists. It happens. But if a diagnosis is under the wrong patient, how can we expect to have accurate results when looking for patients with a specific diagnosis history? We take many measures to reduce this possibility. We have a team of people to correct these instances. In any case, we take great efforts to ensure that we have Good Data as a foundation of our Analytics.
Good Questions
You can have the best Data Possible and the Greatest Analytics Team on the face of the Earth. But if you don't ask the right questions, The data results are useless towards the ultimate goal of making great gains in business performance. For example, in the Hospital, this is the difference between having a Business expert and a Doctor asking the questions about saving money on the treatment of a specific infection. Both have extensive experience within their own field. But the questions they ask are going to be (for lack of a better term) biased towards that field. The business man asks questions towards the bottom line money. While the doctor asks questions towards the treatment process. Both have a piece of the question. But as they are just a part of the story, neither are asking a Good question. In order to get good INSIGHT into the situation you need to take into account both the businessman and the doctor. That question becomes very complex, very quickly. And the answers to that question are even more complex.
Good People
It takes a large number of people to produce the results needed. We have people who insure data integrity. We have people who model the data, we have people who move the data around. We have Data Scientist to create the algorithms needed to produce the answers. And we have people that support the hardware and software of all of these systems. Lastly we have Report writers who take these results and present them in a meaningful way. Anyone of these people can drop the ball, and if they do, the results are useless. So it is important to hire the right people. with the amount of data we are working with...it is not a job for just a couple people. This is not a small business Database. It is not a large business database.
To give an idea of the amount of data we work with, take this into consideration. The Library of Congress is the largest library in the world. It is one of the singular greatest collection of human knowledge on the face of the earth. The entire Library of Congress is reported to fit on approximately 10 to 20 terabytes. To go a little more granular. One bit is either a 0 or a 1. To make one character in this post requires 8 bits. 8 bits is 1 byte. 1024 bytes is 1 kilobyte. 1024 kilobytes is 1 Megabyte. 1024 Megabytes is 1 Gigabyte. And finally 1024 Gigabytes is 1 Terabyte. So if you take 1024 and raise it to the 4th power you get a number with 13 digits.(That is Trillions)Add another digit as you are multiplying again for 10-20 Terabytes in the library of Congress. That is a lot of data...So when I tell you that my hospital is performing analytics on data measured in Petabytes...You can get a better idea of the task that is being performed. What is a petabyte? A petabyte is 1024 Terabytes...so now we are at 1024 to the 5th power which is a 16 digit number (Quadrillions)
Much like a fire that relies on fuel, spark, and oxygen. If any one of the items spoken of is taken away...Analytics fails. Good Data, Good Questions, and Good People...Analytics is a three legged table and if any leg is taken away...the table falls.
I hope that is a good introduction and I hope it both gives you a little understanding of the purpose of analytics, the sheer scale of analytics, and what it relies upon to be successful. I also hope that it raise a myriad of questions within you to add to the discussion. In another post, I mentioned the different types of Analytics and how Analytics differs from Statistics. I think that will be a good stepping point into my next post.