In the past I had occasion to teach technical courses, often to groups of 20 or more people. It was always interesting. There were the one or two people trying to prove how much smarter / better than you they were. There were the one or two people who were there just so they didn’t have to work. But most of the people were there to learn. You figured out who was who pretty quickly. Falling into the trap of labeling them and then only focusing on a subset can be problematic.
My teaching approach was to ask people about real issues (current or past), and use them as case studies for the class. This made the lessons more tangible for everyone. People were forced to develop an understanding of he problem with incomplete knowledge, ask clarifying questions, and then offer suggestions that may or may not work.
The funny thing is that sometimes someone would suggest a solution that just seemed completely off the wall. You would want to understand their line of thinking so that you could show them a better way. Occasionally you would find that their unorthodox approach was really something brilliantly simple and/or highly effective – and very different from what you were expecting.
Every time I taught a course I would learn something. Different perspectives lead to a different understanding of the problems at hand, and that can lead to creative and innovative solutions. The best ideas sometimes come from the places where you least expect them.
Even with the most seasoned teams there are opportunities for teaching and learning. You may hear questions or statements that initially lead you to believe that someone doesn’t understand the problem or goal. It becomes easy to dismiss someone when you don’t feel they are adding value.
But, if you take the extra effort to drill-into their line of thinking you could be very surprised. If nothing else your team should feel more motivated and empowered with the process, and that leads to taking ownership of the problem and finding a solution. Results improve when everyone is focused on a common goal and they feel their contributions matter.
Everyone wins, as long as you give them the chance…
Whether you are a business owner, a manager, or a parent, finding the right way to motivate your team is important to maximize performance and results. Each person is a little bit different, is looking for something a little different, and once you figure out what is important you can get the most out of them. Not everyone wants to be a star – and that is usually OK (as long as they have the right attitude, skills and work ethic).
But, there are those exceptional people that want to be the best, are willing to take risks, work hard, and “think different” in order to succeed. These are the people who are self-motivated, and continue to raise the bar for the entire team as part of a high-performance culture. These are the people who move the dial.
I’ve had the pleasure of being taught by people like this, working with people like this, managing people like this, and helping a few people become people like this. Every once in a while you have a few of these special people working together, and that is when really amazing things happen. These people are generally curious and wonder “why not?” They are confident (not arrogant), intelligent, and passionate about being successful.
Back when I was funding research projects I had a trip scheduled to Philadelphia. I asked a friend at a local hospital to make an introduction to meet someone from “CHOP” (the Children’s Hospital of Philadelphia). They were always ranked as one of the top hospitals, and I wanted to see why. The introduction was made and a lunch meeting was scheduled. I was looking forward to the meeting, but had no real expectations for the meeting.
Much to my surprise, a half-dozen people in a large conference room with a catered lunch greeted me. They gave me presentations on their various projects (which was unusual as they did not know me, but did know I was involved with research projects at other facilities). Everyone in the room was amazing, and the department head (Dr. Terri Finkel) was one of the most impressive people that I had ever met.
After lunch was over I told Dr. Finkel that I appreciated the lunch, but wondered why they went to so much trouble when I never promised to do anything in return for them. She just smiled and replied, “We love what we do, and love having the opportunity to talk about our projects and passions to people who have similar interests.” That made a huge impression on me, and within about six months we were funding projects using a unique approach that Terri suggested in response to a question I had about getting the most “bang for my research dollars.”
Over the course of a few years this team did incredible things that had a tangible impact on Pediatric Rheumatology. There were many great researchers, but two of them really stood out (Drs. Sandy Burnham and Randy Cron – both continue to do amazing things to this day). The results of this team were so much better than everyone else that we supported. They provided a huge return on my investment, and I can take pride in knowing that in some small way I made a difference through these efforts.
To me it came back to the basics. Intelligent people who were passionate about making a difference, who were confident enough to be challenged, and who were led by a visionary person who saw an opportunity to motivate her team and help me achieve my goals. It’s the best type of win-win scenario possible.
These people moved the dial back then, and continue to move it today. It is a thing of beauty to watch stars like this perform. These are the people who shine twice as bright and guide others down the path to success. And, you can find them in every industry and every walk of life.
For several years my company and my family funded a dozen or so medical research projects. I had the pleasure of meeting and working with many brilliant MD/Ph.D. researchers. My goal was to fund $1 million of medical research and find a cure for Arthritis. We didn’t reach that goal, but many good things came out of that research.
Something that amazed me was how research worked. Competition for funding is intense, so there was much less collaboration between institutions than I would have expected. At one point we were funding similar projects at two institutions. The projects went in two very different directions, and it was clear to me that one was going to be much more successful than the other. It seemed almost wasteful, and I thought that there must be a better, more efficient and cost-effective way of managing research efforts.
So, in 2006 I had an idea. What if I could create a cloud based (a very new term at the time) research platform that would support global collaboration? It would need to support true analytical processing, statistical analysis, document management (something else that was fairly new at the time), and desktop publishing at a minimum. Publishing research findings is very important in this space, so my idea was to provide a workspace that supported end-to-end research efforts (inception to publication) and fostered collaboration.
This platform would only really work if there were a new way to allow interested parties to fund this research that was easy to use and could reach a large audience. People could make contributions based on area of interest, specific projects, specific individuals working on projects, or projects in a specific regional area. The idea was a lot like what Crowdtilt (www.crowdtilt.com) is today. This funding mechanism would support non-traditional collaboration, and would hopefully have a huge impact on the research community and their findings.
Additionally, this platform would support the collection of suggestions and ideas. Good ideas can come from anywhere – especially when you don’t know that something is not supposed work.
During one funding review meeting I made a naïve statement about using cortisone injections to treat TMJ arthritis. I was told why this would not work. But, a month or so later I received a call explaining how this might actually work – Conceptual Expansion at its best! That led to new a research project and positive results (see http://onlinelibrary.wiley.com/doi/10.1002/art.21384/pdf).
You never know where the next good idea might come from, so why not make it easy for people to share those ideas.
By the end of 2007 I had designed an architecture using SOA (service oriented architecture) using open source products that would do most of what I needed. Then, in 2008 Google announced the “Project 10^100” competition. I entered, confident that I would at least get honorable mention (alas, nothing came from this).
Then, in early 2010 I spent an hour discussing my idea with the CTO of a popular Cloud company. This CTO had a medical background, liked my idea, offered a few suggestions, and even offered to help. It was the perfect opportunity. But, I had just started a new position at work and this fell to the wayside. That was a shame, and I only have myself to blame. It is something that has bothered me for years.
It’s 2013, there are far more tools available today to make this platform a reality, and it still does not exist. The reason that I’m writing this is because the idea has merit, and think that there might be others who feel he same way and would like to work on making this dream a reality. It’s a change to leverage technology to potentially make a huge impact on society. And, it can create opportunities for people in regions that might otherwise be ignored to contribute to this greater good.
Idealistic? Maybe. Possible? Absolutely!
Two years ago I was assigned some of the product management and product marketing work for a new version of a database product we were releasing. To me this was the trifecta of bad fortune. I didn’t mind product marketing but knew it took a lot of work to do it well. I didn’t feel that product management was a real challenge (I was so wrong here), and I really didn’t want to have anything to do with maps.
I was so wrong in so many ways. I didn’t realize that real product management was just as much work as product marketing. And, I learned that geospatial was far more than just maps. It was quite an eye-opening experience for me – one that turned out to be very valuable as well.
First, let me start by saying that I now have a huge appreciation for Cartography. I never realized how complex mapmaking really is, and how there just as much art as there is science (a lot like programming). Maps can be so much more than just simple drawings.
I had a great teacher when it came to geospatial – Tyler Mitchell (@spatialguru). He showed me the power of overlaying tabular business data with common spatial data (addresses, zip / postal codes, coordinates) and presenting the “conglomeration of data” in layers that made things easier to understand. People buy easy, so that is good in my book.
The more I thought about this technology – simple points, lines, and area combined with powerful functions, the more I began to think about other uses. I realized that you could use it to correlate very different data sets and graphically show relationships that would otherwise extremely difficult to make.
Think about having access to population data, demographic data, business and housing data, crime data, health / disease data, etc. Now, think about a simple and easy to use graphical dashboard that lets you overlay as many of those data sets as you wanted. Within seconds you see very specific clusters of data that is correlated geographically.
Some data may only be granular to a zip code or city, but other data will allow you to identify patterns down to specific streets and neighborhoods. Just think of how something so simple can help you make decisions that are so much better. The interesting thing is how few businesses are really taking advantage of this cost-effective technology.
If that wasn’t enough, just think about location aware applications, the proliferation of smart devices that completely lend themselves to so many helpful and lucrative mobile applications. Even more than that, they make those devices more helpful and user friendly. Just think about how easy it is to find the nearest Indian restaurant when the thought of curry for lunch hits you. And these things are just the tip of the iceberg.
What a lucky day it was for me when I was assigned this work that I did not want. Little did I know that it would change the way that I think about so many things. That’s just the way things work out sometimes.
Ever since I worked on redesigning a risk management system at an insurance company (1994-1995) I was impressed at how better decisions could be made with more data – assuming it was the right data. The concept of, “What is the right data?” has intrigued me for years, as what may seem common sense today could have been unknown 5-10 years ago and could be completely passé 5-10 years from now. Context becomes very important because of the variability and relevance of data over time.
This is what makes Big Data interesting. There really is no right or wrong answer or definition. Having a framework to define, categorize, and use that data is important. And at some point being able to refer to the data in-context will be very important as well. Just think about how challenging it could be to compare scenarios or events from 5 years ago with those of today. It’s likely not an apples-to-apples comparison but could certainly be done. The concept of maximizing the value of data is pretty cool stuff.
The way I think of Big Data is similar to a water tributary system. Water enters the system many ways – rain from the clouds, sprinkles from private and public supplies, runoff, overflow, etc. It also has many interesting dimensions, such as quality/purity (not necessarily the same due to different aspects of need), velocity, depth, capacity, and so forth. Not all water gets into the tributary system (e.g., some is absorbed into the groundwater tables, and some evaporate) – just as some data loss should be anticipated.
If you think in terms of streams, ponds, rivers, lakes, reservoirs, deltas, etc. there are many relevant analogies that can be made. And just like the course of a river may change over time, data in our “big data” water tributary system could also change over time.
Another part of my thinking is based on an experience I had about a decade ago (2002 – 2003 timeframe) working on a project for a Nanotech company. In their labs, they were testing various things. There were particles that changed reflectivity based on the temperature that was embedded in shingles and paint. There were very small batteries that could be recharged tens of thousands of times, were light and had more capacity than a common 12-volt car battery.
And, there was a section where they were doing “biometric testing” for the military. I have since read articles about things like smart fabrics that could monitor the health of a soldier and do things like apply basic first aid and notify others once a problem was detected. This company felt that by 2020 advanced nanotechnology would be widely used by the military, and by 2025 it would be in wide commercial use. Is that still a possibility? Who knows…
Much of what you read today is about the exponential growth of data. I agree with that, but as stated earlier, and this is important, I believe that the nature of and sources of that data will change significantly. For example, nano-particles in engine oil will provide information about temperature, engine speed and load, and even things like rapid changes in movement (fast take-off or stops, quick turns). The nanoparticles in the paint will provide weather conditions. The nanoparticles on the seat upholstery will provide information about occupants (number, size, weight). Sort of like the “sensor web,” from the original Kevin Delin perspective. A lot of “Information of Things” data will be generated, but then what?
I believe that time will become an important aspect of every piece of data, and that location (X, Y, and Z coordinates) will be just as important. But, not every sensor will collect location (spatial data). I do believe there will be multiple data aggregators in common use at common points (your car, your house, your watch). Those aggregators will package the available data in something akin to an XML object, which allows flexibility. From my perspective, this is where things become very interesting relative to commercial use and data privacy.
Currently, companies like Google make a lot of money from aggregating data from multiple sources, correlating it to a variety of attributes, and then selling knowledge derived from that plethora of data. I believe that there will be opportunities for individuals to use “data exchanges” to manage, sell, and directly benefit from their own data. The more interesting their data, the more value it has and the more benefit it provides to the person selling it. This could have a huge economic impact, and that would foster both the use and expansion of various commercial ecosystems required to manage the commercial and privacy aspects of this technology.
The next logical step in this vision is “smart everything.” For example, you could buy a shirt that is just a shirt. But, for an extra cost, you could turn-on medical monitoring or refractive heating/cooling. And, if you felt there was a market for extra dimensions of data that could benefit you financially, then you could enable those sensors as well. Just think of the potential impact that technology would make to commerce in this scenario.
This is what I personally believe will happen within the next decade or so. This won’t be the only type of or use of big data. Rather, there will be many valid types and uses of data – some complementary and some completely discrete. It has the potential to become a confusing mess. But, people will find ways to ingest, categorize, and correlate data to create value with it – today or in the future.
Utilizing data will become an increasingly competitive advantage for people and companies knowing how to do something interesting and useful with it. Who knows what will be viewed as valuable data 5-10 years from now, but it will likely be different than what we view as valuable data today.
So, what are your thoughts? Can we predict the future based on the past? Or, is it simply enough to create platforms that are powerful enough, flexible enough, and extensible enough to change our understanding as our perspective of what is important changes? Either way it will be fun!