There are few things more beautiful than watching a horse gallop at full speed across a field! The rhythmic sound of hooves dancing the dirt. Debris spring to life when they machines leap forward! Nostrils flair while necks push to reach each step. The rider floats inches above the saddle in a gliding world wind.
Mankind began domesticating these animals around 10,000 years ago (1). Since then we have sought to harness and improve their power. In polo we seek to use this power in a competitive manner. That requires a lot, and I mean a lot, of training! You can see that hard work in their gait, dexterity, and responsiveness.
In my spare time I make polo mallets and sometimes get lucky enough to sell a few here or there. 50% of profits go to orphans. Another 10% may be donated to horse rescues (or other related). As a hobby business, and a sport I sometimes get to play, I can't think of any other places to spend some free time. Put on my headphones and get to work. I sell them around $70 as practice mallets until quality is improved. The grip quality and availability straight canes seems to be my primary concern. Got an idea on how to use a rubber inner core for the handle.
Monday, December 2, 2019
Wednesday, November 27, 2019
Digital GDP on Government's Mind. Can We Maximize Growth Through Cluster Development?
Data has exploded over the past few decades. The value associated with digital services, software, and its impact on industry development is elusive at best. Older models are limited by the assumptions gained during their historical development. GDP is one such measure of national output but often doesn't do well in calculating digital value. Research has shown that digital content can increase GDP by as much as 6% (Byrne & Corrado, 2019).
From an economic standpoint, digital content represents only a sliver of total economic value. According to one of the largest Chinese technology manufacturers Huawei Technologies Co, we are skipping over a significant portion of our productive value in the GDP debate (Wladawsky-Berger, 2017). There is real value that will drive future markets and we have only just started to calculate that value.
The Federal Research discussion on the value of consumer goods is broken down into the home goods that we buy to access networks and the subscription services for digital services (Byrne & Corrado, 2019). The value not reflected in standard GDP measurements amounts to approximately $1,775 per connected user per year from 2004 to 2017. The formula they use for the value of consumer digital services is below (hardware+subscription):
 |
| (Byrne & Corrado, 2019) |
One of the major advantages of technology is that it creates spill over that speeds the pace of cluster growth.. "The digital spillover happens when technology accelerates knowledge transfer, business innovation, and performance improvement within a company, across supply chains and amongst industries, to achieve a sustainable development economic impact"(Xu & Cooper, 2019, p. 24).
Principle: Data services and technology have real value on innovation and GDP.
Consider that emerging online networks and software empowers companies to collaborate in ways that was not possible 20 years ago. Information is increasingly being shared and having major impact on jobs, life, and the economy as we know it! Those networks lead to innovation that is responsible for things like new cell phones and improved government efficiencies.
As much as $1.7 Trillion can be added to the economy by 2025 through digitization (Xu & Cooper, 2019). The same report said that 24.3% of the global GDP will be from digitization. Matched with proper cluster management it is possible that number could be much larger putting the U.S. on a leading path of inventors and builders.
Principle: New inventions and the Butterfly Effect Increase Cluster Development.
In the cluster model the right factors lead to innovation and in turn impact the growth potential of the entire cluster. Think of a new manufacturing method in one company and how it is quickly adopted throughout the cluster to strengthen the entire system. The networks (physical and virtual) improve the speed of technology transference to other members of the system that creates benefits for the entire cluster.
Principle: Technology can Increase Innovation through Connecting Ideas
Innovation occurs by connecting new and unique ideas to solve problems. Companies that are in close proximity to each other, share resources, and have movement of employees (spill over) have an easier time innovating. In the cluster model it is possible to see how creative destruction (Schumpeter) leads to pressure to innovate through the need for change, the firm invests in new products/services, and the social networks create and disseminate that innovation throughout the entire cluster.
In the physical world, companies have an advantage by being next to each other and share physical assets. In contrast, in the virtual world companies have the mutual advantage of sharing intellectual resources. Physical proximity enhanced with digital interactivity that leads to product innovation through the sweat spot of economic growth.
According to an article in Wall street Journal the U.S. has the largest digital economy 35% of GDP when compared to 18.5% average for other advanced nations (Wladawsky-Berger, 2017). The number further represents about 1/3 digital assets and 2/3 digital spill over. Spill over often results in additional innovation as its make its way throughout its cluster.
The digital economy is going to continue growing. According to a 2017 Huawei Technologies Co., Ltd. and Oxford Economics study entitled Measuring the true impact of the digital economy, the digital economy will move from 15.5% of the entire global economy in 2016 to 24.3% in 2025. The U.S. should be leading that growth sector by making adjustments now to enhance future invention and manufacturing sectors.
More interesting the same study found that for every $1 invested in digital technologies over the past three decades a whopping $20 was added on average to GDP (Xu & Cooper, 2019, p. 24) That was much higher rate of return when compared to other investments that maintained on average a $3 return per $1 invested. As investment in technology increases so does wealth increase for a nation.
Principle: Investments in national technology leads to high impacts on GDP.
Interactive clusters strength is based in part on its infrastructure. Of which, the ability to share data and information relies heavily on high speed data delivery that can move ideas and resources quickly. Cities that invest in their technology infrastructure may find better opportunities for future growth as networks form to improve cluster development.
Principle: Technology infrastructure can enhance cluster development.
Our digital GDP provides a major strategic advantage that will lead to national innovation when harnessed properly. It is through this digital economy and the innovation it provides that new products that enhance manufacturing occur. When we put people to work building the products our companies invented we will find, not only our national wealth growing, but also our personal and local income.
There are two important things to consider when fitting this study into an economic cluster research....
1. Digital collaboration networks create value for businesses within a cluster.
2. Bunching these networks into interactive clusters may enhance GDP through market driven innovative outcomes.
The study I'm working on fits with modern cluster research but was developed semi-independently from other theories. It has a few advantages that include sustainable development and the connecting of a few theories into one that may lead to break out markers. It is not a finished product but you may read about how far I am HERE
David Byrne, Carol Corrado (2019) Accounting for Innovations in Consumer Digital Services: IT still matters Federal Reserve Board, Washington, D.C.https://www.federalreserve.gov/econres/feds/files/2019049pap.pdf
Wladawsky-Berger, I (September, 2017). GDP Doesn’t Work In A Digital Economy. The Wall Street Journal. Retrieved https://blogs.wsj.com/cio/2017/11/03/gdp-doesnt-work-in-a-digital-economy/
Xu, W. & Cooper, A. (2017). Measuring the true impact of the digital economy. Huawei Technologies Co., Ltd. and Oxford Economics.
https://www.huawei.com/minisite/gci/en/digital-spillover/files/gci_digital_spillover.pdf
Monday, November 25, 2019
Steps to Preparing Data- Keep it Clean!
Collecting pounds of data is useless unless we can do something with it that leads to new knowledge and information. You may start with a mound of useless numbers, samples, and information. It can feel a little overwhelming. You can reduce data anxiety by thinking about your study beforehand and following a few steps to preparing it for analysis and use.
Pre-planning is important. Developing your coding process, organization methods, and statistical measurements beforehand will lead to a better study. That doesn't mean the process are set in stone but that a better data plan improves the end results.
Scrub out useless data that isn't going to help your study. Be aware that what you may find useless does contain useful information. For example, if you have a lot of people who abandon your survey it may be the language, design, or even type of questions that push people to leave.
Take out that data which is truly not helpful to your study because of inaccuracy and human error. Review each removed data points and try and keep some records of what you did. I save multiple versions before and after the scrubbing.
Then I begin to categorize the information about the variables. Sometimes I need to code the data so it is more useful. That occurs when you need a specific numerical number or letter to designate where the data came from. Depending on how I want to categorize I will use any number of methods and coding methodologies.
There is a lot of information out there on classification of data. I suggest you read this blog article from the Digital Guardian Blog. https://digitalguardian.com/blog/what-data-classification-data-classification-definition It provides some great resources.
Data classification makes data useful. You will want to ensure that whatever encoding process you use that you can find the things you need. I have seem people put together great coding systems and then find they can't retrieve the information from their data bases properly. Keep it simple in science!
Raw data is relatively useless. You have a responsibility to make it more useful by preparing it for data analytics. The better job you do at the root level, the better off you will be when you want to analyze that data for connections and meaning. One of the best things you can do is have this all written down and figured out before you get started.
Pre-planning is important. Developing your coding process, organization methods, and statistical measurements beforehand will lead to a better study. That doesn't mean the process are set in stone but that a better data plan improves the end results.
Scrub out useless data that isn't going to help your study. Be aware that what you may find useless does contain useful information. For example, if you have a lot of people who abandon your survey it may be the language, design, or even type of questions that push people to leave.
Take out that data which is truly not helpful to your study because of inaccuracy and human error. Review each removed data points and try and keep some records of what you did. I save multiple versions before and after the scrubbing.
Then I begin to categorize the information about the variables. Sometimes I need to code the data so it is more useful. That occurs when you need a specific numerical number or letter to designate where the data came from. Depending on how I want to categorize I will use any number of methods and coding methodologies.
There is a lot of information out there on classification of data. I suggest you read this blog article from the Digital Guardian Blog. https://digitalguardian.com/blog/what-data-classification-data-classification-definition It provides some great resources.
Data classification makes data useful. You will want to ensure that whatever encoding process you use that you can find the things you need. I have seem people put together great coding systems and then find they can't retrieve the information from their data bases properly. Keep it simple in science!
Raw data is relatively useless. You have a responsibility to make it more useful by preparing it for data analytics. The better job you do at the root level, the better off you will be when you want to analyze that data for connections and meaning. One of the best things you can do is have this all written down and figured out before you get started.
Tuesday, November 19, 2019
Boxing and Muay Thai as Fitness: Watch the Knee to Abdomen!
Its almost all cardio!
That is right! Boxers are known to be in shape. They spend almost all of their time training and very little in the ring. Thus, they are constantly conditioning and that means little to no fat on their bodies!
Useful coordination!
When your body works together to complete these moves there is useful coordination. The movements can be used for other types of activities that rely on those basic skills.
Muscle and upper body strength!
Kickboxing is known to produce people with high muscle strength. Mixing speed and force together creates a nuclear powerhouse for growing muscle.
How did I do? Well...it was a great practice. Lots of body mechanic movement type stuff to make sure proper elbow placement and proper rotating of hip during kicks.
Sampling Your Population-One Nut at a Time!
Sampling is a little like going to the grocery store and plucking a nut or two to "sample" the product. While this might get you kicked out of your local grocery store in a research world it is encouraged to sample as much as you want!!!! No one will shun you for grabbing a handful of sample nuts as long as you share what you did! 🤯
It would be in most cases, except a few small populations, excruciatingly difficult to test everyone in the population. We then must consider a smaller and more reasonable size that "sort of" shared the same characteristics as the main population.
The risk in many of these cases is that sampling is done incorrectly and misrepresents the true population. To better ensure accuracy we may use a number different methods such as random and convenience samples. The video below gives a few ideas....
There is no such thing as an accurate sample! You can sample as much as you like, over and over, and it won't be 100% correct! Sampling in different ways by taking multiple measurements from different areas leads to better outcome.
Sometimes samples are not big enough to draw any real conclusions. Confidence levels and sample size calculations can help ensure a size of participants needed to make meaningful conclusions.
Likewise, how we design our study is going to have influence on the samples taken. Designs will impact how and what types of samples are needed and where we draw them from.
What we should learn here is that sampling is very important and if we desire to have studies that draw meaningful conclusions from the data some consideration over sampling is needed. Review your study design, access to samples, the selection and tools you will need to make a solid analysis of your study focus.
It would be in most cases, except a few small populations, excruciatingly difficult to test everyone in the population. We then must consider a smaller and more reasonable size that "sort of" shared the same characteristics as the main population.
The risk in many of these cases is that sampling is done incorrectly and misrepresents the true population. To better ensure accuracy we may use a number different methods such as random and convenience samples. The video below gives a few ideas....
There is no such thing as an accurate sample! You can sample as much as you like, over and over, and it won't be 100% correct! Sampling in different ways by taking multiple measurements from different areas leads to better outcome.
Sometimes samples are not big enough to draw any real conclusions. Confidence levels and sample size calculations can help ensure a size of participants needed to make meaningful conclusions.
Likewise, how we design our study is going to have influence on the samples taken. Designs will impact how and what types of samples are needed and where we draw them from.
What we should learn here is that sampling is very important and if we desire to have studies that draw meaningful conclusions from the data some consideration over sampling is needed. Review your study design, access to samples, the selection and tools you will need to make a solid analysis of your study focus.
Sunday, November 10, 2019
Data!!! Data!!! Checking on Secondary Data Sources!
Data can come from all types of sources and piles up in our dashboards and can be of great help or hindrance to the achievement of our goals. If the data isn't managed properly you can make fatal errors that can destroy your business or cost you a lot of money down the road. Because research is expensive most people rely on secondary sources. Secondary sources are cheap but do require a level of care to ensure the data is saying what you think it is saying.
Let us start by saying that secondary data can be expensive! There is a reason why secondary data makes more sense versus inventing and conducting your own research. It can be expensive and time consuming to create new studies. If you don't have the expertise in your business you will likely need outside consultants.
Those businesses that rely on secondary data may want to consider a few issues.....
1. Make sure the data fits your purposes. Secondary data may or may not meet your needs. Just because someone counted the amount of cars it may have no relation to the type of radio those cars use.
2. Check the sources of data. Yes...you need to read the fine print! How the data is collected, organized used and managed is key to deter validity for your study.
3. Look for similar type studies that may support or not support the data collection methods of the study.
You would be amazed but there are times when studies look to create a biased result. This can often happen in political or corporate arenas where a specific outcome has consequences. Academic and government data is more likely to be non-biased when compared to corporate research.
4. Look for non-profits, government, and others who collected data and have a stake in accurate information.
Sometimes the secondary sources are not enough and you must try and build your own knowledge on an ideas. Surveys are often used to help gauge opinions about services and needs. While surveys are generally easy to build if you ask straight forward questions there are all types of caveats and mistakes you can make when you don't have "in house" scientific knowledge. The best bet is to use experts when you plan on using the information for significant investments.
Here is a little advice when determining in house or secondary sources. First try and use secondary sources that provide contextual information about the problem. If there the available information doesn't necessarily answer your question consider an in-house study. Be very sure the answer is worth the time and expense. If you are gauging your customers opinions in a small business just ask them what you want and take notes. If you are poling 20K customers you may want to have someone look at your survey and delivery methods before proceeding.
Let us start by saying that secondary data can be expensive! There is a reason why secondary data makes more sense versus inventing and conducting your own research. It can be expensive and time consuming to create new studies. If you don't have the expertise in your business you will likely need outside consultants.
Those businesses that rely on secondary data may want to consider a few issues.....
1. Make sure the data fits your purposes. Secondary data may or may not meet your needs. Just because someone counted the amount of cars it may have no relation to the type of radio those cars use.
2. Check the sources of data. Yes...you need to read the fine print! How the data is collected, organized used and managed is key to deter validity for your study.
3. Look for similar type studies that may support or not support the data collection methods of the study.
You would be amazed but there are times when studies look to create a biased result. This can often happen in political or corporate arenas where a specific outcome has consequences. Academic and government data is more likely to be non-biased when compared to corporate research.
4. Look for non-profits, government, and others who collected data and have a stake in accurate information.
Sometimes the secondary sources are not enough and you must try and build your own knowledge on an ideas. Surveys are often used to help gauge opinions about services and needs. While surveys are generally easy to build if you ask straight forward questions there are all types of caveats and mistakes you can make when you don't have "in house" scientific knowledge. The best bet is to use experts when you plan on using the information for significant investments.
Here is a little advice when determining in house or secondary sources. First try and use secondary sources that provide contextual information about the problem. If there the available information doesn't necessarily answer your question consider an in-house study. Be very sure the answer is worth the time and expense. If you are gauging your customers opinions in a small business just ask them what you want and take notes. If you are poling 20K customers you may want to have someone look at your survey and delivery methods before proceeding.
Thursday, November 7, 2019
Industry-Curriculum Alignment Through Skills Assessment
It was great to work with my colleagues presenting a project started a while ago. We found a way to assess market readiness of graduates by using market research mixed with current assessment practices. The methodology worked great for something new! Will likely discuss a little more in the future. :)
Monday, November 4, 2019
Chewy Outdoor U.P. Adventures-Thoughts from a Dog :)
 |
| Not sure where he taking me but I hope there are treats! |
This is Chewy! He is a sock thief and a convicted potty violator. Despite his shortcomings he loves the outdoors.
We try hunt, hike, cross country run, snow shoe and other stuff as much as we can. This little guy can jump in the water and play around as much as the big dogs.
 |
| Must have been an elephant path. |
Supporting wild life, habitat restoration, sustainable hunting, pollution clean up, and a lifestyles that allows for outdoor activities is helpful to our lives. There is a part of us that must reach back to our natural state every once in a while to feel more at ease. This is one of the reasons why I love the Upper Peninsula of Michigan!
 |
| Refreshing water to drink. |
 |
| Every time I put one in my mouth I get yelled at! |
Sunday, November 3, 2019
Discovering Problems with Exploratory Research
When we think of research an image comes to our mind of the lab coat professor going over calculations, long formulas and test tubes to come up with the next greatest discovery. No need for the coke bottle glasses that move into the finite details of "fineness". Exploratory research may ask a question but doesn't necessary seek a specific answer. Its main goal is to get into the weeds to understand the inner workings of their subject without coming to an pre-conclusions.
Exploratory Research:
When you are not sure exactly what is going on but you got a solid "hunch" you may want to explore ideas and concepts to better understand a problem. It focuses in a broad area and continues to adjust and changes as the situation changes. It is possible to start with one understanding and end with a completely different one later on.
The essential purpose of exploration is to go into the unknown and explore what seems to be an appropriate path of discovery. You really don't know what to expect and instead allow elements to go forward unimpeded so that you can start to see some relations between elements in a way that leads to future research.
Description of Occurrence.
As you explore ideas and concepts you want to include as much descriptive detail as possible. Because you don't know what the elements are you want to record as much pertinent information as you can so as to further your analysis. Sometimes things that seem important today are not so important tomorrow. At some other time you very well may find the opposite to be true.
Describing what is occurring and taking proper notes, recordings, and other documentation helps in the overall evaluation down the road. You will want to document any study you have conducted from start to finish. There should be some logic and method you are following throughout your work and the type of documentation will relate to your design.
There is no Causality in Exploratory Research
There is no causality in exploratory research. We don't know what the variables are but have an idea of where to look for them. While you may end up making some inferential judgments about causality the strength of those connections and whether they are relevant relies on a more quantitative approach.
Don't go into the research with a lot of pre-beliefs and bias or the directions you take will end up leading to false conclusions. The goal is to find new associations among ideas and concepts and not necessarily to prove that existing one's exist. Time again and again the bias of the research makes it into what they are discovering.
Why its not like a Lab?
In a lab you can design studies that manipulate variables. In the field the variables are at the mercy of their environment making cause and effect much more difficult to determine. Exploratory research allows one to determine potential connections while quantitative determines the strength, or insignificance, of those connections.
Exploratory Research:
When you are not sure exactly what is going on but you got a solid "hunch" you may want to explore ideas and concepts to better understand a problem. It focuses in a broad area and continues to adjust and changes as the situation changes. It is possible to start with one understanding and end with a completely different one later on.
The essential purpose of exploration is to go into the unknown and explore what seems to be an appropriate path of discovery. You really don't know what to expect and instead allow elements to go forward unimpeded so that you can start to see some relations between elements in a way that leads to future research.
Description of Occurrence.
As you explore ideas and concepts you want to include as much descriptive detail as possible. Because you don't know what the elements are you want to record as much pertinent information as you can so as to further your analysis. Sometimes things that seem important today are not so important tomorrow. At some other time you very well may find the opposite to be true.
Describing what is occurring and taking proper notes, recordings, and other documentation helps in the overall evaluation down the road. You will want to document any study you have conducted from start to finish. There should be some logic and method you are following throughout your work and the type of documentation will relate to your design.
There is no Causality in Exploratory Research
There is no causality in exploratory research. We don't know what the variables are but have an idea of where to look for them. While you may end up making some inferential judgments about causality the strength of those connections and whether they are relevant relies on a more quantitative approach.
Don't go into the research with a lot of pre-beliefs and bias or the directions you take will end up leading to false conclusions. The goal is to find new associations among ideas and concepts and not necessarily to prove that existing one's exist. Time again and again the bias of the research makes it into what they are discovering.
Why its not like a Lab?
In a lab you can design studies that manipulate variables. In the field the variables are at the mercy of their environment making cause and effect much more difficult to determine. Exploratory research allows one to determine potential connections while quantitative determines the strength, or insignificance, of those connections.
Tuesday, October 29, 2019
First Snow in the Upper Peninsula
The first snow is one of the most beautiful. White, fluffy, soft with a dusting on the tree tops. With pristine beauty comes a price. You must make the effort to get out and see it! There are miles upon miles of hiking and hunting trails in the Upper Peninsula of Michigan just waiting for you to explore. When you need to get out of the city and into nature to gain peace of mind head north! These pics were taken on Days River. I'm pretty sure "Chewy" my dog is wondering whether he has some husky blood in him. 😮
Subscribe to:
Posts (Atom)