Recently I was helping one of my children research a topic for a school paper. She was doing well, but the results she was getting were overly broad. So, I taught her some “Google-Fu,” explaining how you can structure queries in ways that yield better results. She commented that the searches should be smarter than that, and I explained that sometimes the problem is that search engines look at your past searches and customize results as an attempt to appear smarter. Unfortunately, those results can be skewed and potentially lead someone in the wrong direction. It was a good reminder that getting the best results from search engines often requires a bit of skill and query planning.
Then the other day I saw this commercial from Motel 6 (“Gas Station Trouble”) where a man has problems getting good results from his smart phone. That reminded me of seeing someone speak to their phone, getting frustrated by the responses received. His questions went something like this: “Siri, I want to take my wife to dinner tonight, someplace that is not too far away, and not too late. And she likes to have a view while eating so please look for something with a nice view. Oh, and we don’t want Italian food because we just had that last night.” Just as amazing as the question being asked was watching him ask it over and over again in the exact same way, each time becoming even more frustrated. I asked myself, “Are smart phones making us dumber?” Instead of contemplating that question I began to think about what future smart interfaces would or could be like.
I grew up watching Sci-Fi computer interfaces like “Computer” on Star Trek (1966), “HAL” on 2001 : A Space Odyssey (1968), “KITT” from Knight Rider (1982), and “Samantha” from Her (2013). These interfaces had a few things in common: They responded to verbal commands; They were interactive – not just providing answers, but also asking qualifying questions and allowing for interrupts to drill-down or enhance the search (e.g., with pictures or questions that resembled verbal Venn diagrams); They often provided suggestions for alternate queries based on intuition. Despite having 50 years of science fiction examples we are still a long way off from realizing that goal. Like many new technologies, they were originally envisioned by science fiction writers long before they appeared in science.
There seems to be a spectrum of common beliefs about modern interfaces. On one end there are products that make visualization easy, facilitating understanding, refinement and drill-down of data sets. Tableau is a great example of this type of easy to use interface. At the other end of the spectrum the emphasis is on back-end systems – robust computer systems that digest huge volumes of data and return the results to complex queries within seconds. The Actian Analytics Platform is a great example of a powerful analytics platform. In reality, you really need both if you want to maximize the full potential of either.
But, there is so much more to be done. I predict that within the next 3 – 5 years we will see business and consumer examples that are closer to the verbal interfaces from those familiar Sci-Fi shows (albeit with limited capabilities and no flashing lights). Within the next 10 years I believe we will have computer interfaces that intuit our needs and facilitate generating the correct answers quickly and easily. While this is unlikely to be at the level of “The world’s first intelligent Operating System” envisioned in the movie “Her,” and probably won’t even be able to read lips like “HAL,” it should be much more like HAL and KITT than like Siri (from Apple) or Cortana (from Microsoft). Siri was groundbreaking consumer technology when it was introduced. Cortana seems to have taken a small leap ahead. While I have not mentioned Google Now, it is somewhat of a latecomer to this consumer smart interface party, and in my opinion is behind both Siri and Cortana.
So, what will this future smart interface do? It will need to be very powerful, harnessing a natural language interface on the front-end with an extremely flexible and robust analytics interface on the back-end. The language interface will need to take a standard question (in multiple languages and dialects) – just as if you were asking a person, deconstruct it using Natural Language Processing (NLP), and develop the proper query based on the available data. That is important but only gets you so far.
Data will come from many sources – things that we consider today with relational, object, and graph databases. There will be structured and unstructured data that must be joined and filtered quickly and accurately. In addition, context will be more important than ever. Pictures and videos could be scanned for facial recognition, location (via geotagging), and in the case of videos analyze speech. Relationships will be identified and inferred based on a variety of sources, using both data and metadata. Sensors will collect data from almost everything we do and (someday) wear, which will provide both content and context. The use of Stylometry will identify outside content likely related to the people involved in the query and provide further context about interests, activities, and even biases. This is how future interfaces will truly understand (not just interpret), intuit (so it can determine what you really want to know), and then present results that may be far more accurate than we are used to today. Because the interface is interactive in nature it will provide the ability to organize and analyze subsets of data quickly and easily.
So, where do I think that this technology will originate? I believe that it will be adapted from video game technology. Video games have consistently pushed the envelope over the years, helping drive the need for higher bandwidth I/O capabilities in devices and networks, better and faster graphics capabilities, and larger and faster storage (which ultimately led to flash memory and even Hadoop). Animation has become very lifelike and games are becoming more responsive to audio commands. It is not a stretch of the imagination to believe that this is where the next generation of smart interfaces will be found (instead of from the evolution of current smart interfaces).
Someday it may no longer be possible to “tweak” results through the use or omission of keywords, quotation marks, and flags. Additionally, it may no longer be necessary to understand special query languages (SQL, NoSQL, SPARQL, etc.) and syntax. We won’t have to worry as much about incorrect joins, spurious correlations and biased result sets. Instead, we will be given the answers we need – even if we don’t realize that this was what we needed in the first place. At that point computer systems may appear nearly omniscient.
When this happens parents will no longer need to teach their children “Google-Fu.” Those are going be interesting times indeed.
Back in early 2011 myself and 15 other members of the Executive team at Ingres were taking a bet on the future of our company. We knew that we needed to do something big and bold, and decided to build what we thought the standard data platform would be in 5-7 years. A small minority of the people on that team did not believe this was possible and left, while the rest of us focused on making that happen. There were three strategic acquisitions to fill-in the gaps on our Big Data platform. Today (as Actian) we have nearly achieved our goal. It was a leap of faith back then, but our vision turned out to be spot-on and our gamble is paying off today.
Every day my mailbox is filled with stories, seminars, white papers, etc. about Big Data. While it feels like this is becoming more mainstream, it is interesting to read and hear the various comments on the subject. They range from, “It’s not real” and “It’s irrelevant” to “It can be transformational for your business” to “Without big data there would be no <insert company name here>.”
What I continue to find amazing is hearing comments about big data being optional. It’s not – that genie has already been let out of the bottle. There are incredible opportunities for those companies that understand and embrace the potential. I like to tell people that big data can be their unfair advantage in business. Is that really the case? Let’s explore that assertion and find out.
We live in the age of the “Internet of Things.” Data about nearly everything is everywhere, and the tools to correlate that data to gain understanding of so many things (activities, relationships, likes and dislikes, etc.) With smart devices that enable mobile computing we have the extra dimension of location. And, with new technologies such as Graph Databases (based on SPARQL), graphic interfaces to analyze that data (such as Sigma), and identification technology such as Stylometry, it is getting easier than ever to identify and correlate that data.
We are generating increasingly larger and larger volumes of data about everything we do and everything going on around us, and tools are evolving to make sense of that data better and faster than ever. Those organizations that perform the best analysis, get the answers fastest, and act on that insight quickly are more likely to win than the organizations that look at a smaller slice of the world or adopt a “wait and see” posture. So, that seems like a significant advantage in my book. But, is it an unfair advantage?
First, let’s keep in mind that big data is really just another tool. Like most tools it has the potential for misuse and abuse. And, whether a particular application is viewed as “good” or “bad” is dependent on the goals and perspective of the entity using the tool (which may be the polar opposite view of the groups of people targeted by those people or organizations). So, I will not attempt to make judgments about the various use cases, but rather present a few use cases and let you decide.
Scenario 1 – Sales Organization: What if you could not only understand what you were being told a prospect company needs, but also had a way to validate and refine that understanding? That’s half the battle in sales (budget, integration, and support / politics are other key hurdles). Data that helped you understand not only the actions of that organization (customers and industries, sales and purchases, gains and losses, etc.), but also the goals, interests and biases of the stakeholders and decision makers. This could provide a holistic view of the environment and allow you to provide a highly targeted offering, with messaging tailored to each individual. That is possible, and I’ll explain soon.
Scenario 2 – Hiring Organization: As a hiring manager there are many questions that cannot be asked. While I’m not an attorney, I would bet that State and Federal laws have not kept pace with technology. And, while those laws vary state by state, there are likely loopholes that allow for use of public records. Moreover, implied data that is not officially taken into consideration could color the judgment of a hiring manager or organization. For instance, if you wanted to “get a feeling” if a candidate might fit-in with the team or the culture of the organization, or have interests and views that are aligned with or contrary to your own, you could look for personal internet activity that would provide a more accurate picture of that person’s interests.
Scenario 3 – Teacher / Professor: There are already sites in use to search for plagiarism in written documents, but what if you had a way to make an accurate determination about whether an original work was created by your student? There are people who, for a price, will do the work and write a paper for a student. So, what if you could not only determine that the paper was not written by your student, but also determine who the likely author was?
Do some of these things seem impossible, or at least implausible? Personally, I don’t believe so. Let’s start with the typical data that our credit card companies, banks, search engines, and social network sites already have related to us. Add to that the identified information that is available for purchase from marketing companies and various government agencies. That alone can provide a pretty comprehensive view of us. But, there is so much more that’s available.
Think about the potential of gathering information from intelligent devices that are accessible through the Internet, or your alarm and video monitoring system, etc. These are intended to be private data sources, but one thing history has taught us is that anything accessible is subject to unauthorized access and use (just think about the numerous recent credit card hacking incidents).
Even de-identified data (medical / health / prescription / insurance claim data is one major example), which receives much less protection and can often be purchased, could be correlated with a reasonably high degree of confidence to gain understanding on other “private” aspects of your life. The key is to look for connections (websites, IP addresses, locations, businesses, people), things that are logically related (such as illnesses / treatments / prescriptions), and then make as accurate of an identification as possible (stylometry looks at things like sentence complexity, function words, co-location of words, misspellings and misuse of words, etc. and will likely someday take into consideration things like idea density). It is nearly impossible to remain anonymous in the Age of Big Data.
There has been a paradigm shift when it comes to the practical application of data analysis, and the companies that understand this and embrace it will likely perform better than those who don’t. There are new ethical considerations that arise from this technology, and likely new laws and regulations as well. But for now, the race is on!
Science has been interesting to me for most of my lifetime, but it wasn’t until my first child was born that I shifted from “interested” to “involved.” You see, my daughter was diagnosed with Systemic Onset Juvenile Idiopathic Arthritis (SoJIA – originally called Juvenile Rheumatoid Arthritis, or JRA). As I wrote it a brochure, this was a big name for such a little girl. She was only 15 months old at the time, and there was very little research on the topic back then. This was also a time a few years before the breakthroughs of biological medicines like Enbrel were being used on children.
One of the things that I found is that this disease could be horribly debilitating. Children often had physical deformities as a result of this disease. As a first time parent difficult to imagine that life for your child. Luckily, I had just started a company and it was starting to be successful, so I decided to finds ways to personally make a tangible difference.
As someone new to “major gifts” and philanthropy I quickly learned that some forms of gifts were more beneficial than others. While most organizations wanted you to start a fund (which we did), the impact from that tended to be more long-term and less immediate. I met someone who showed me a different and better way (here’s a post that describes that in more detail).
I didn’t want to blindly give money. Rather, I wanted to treat these donations like investments. In order to be successful I needed to understand some of the medicine and some of the science in these areas. That started a new era of independent learning in my life.
There was a lot going on in the fields of Genetics and Genomics at the time (here’s a good explanation of the difference between the two). My interest and efforts in this area led to a position on the Medical and Scientific Advisory Committee with the Arthritis Foundation. With the exception of me, these were talented and successful physicians who were also involved with medical research. We met quarterly, and I did ask questions and make suggestions that made a difference. But, unlike everyone else on the committee, I needed to study and prepare for 40+ hours for each call to ensure that had enough of an understanding to add value and not be a distraction.
A few years later we did work for a Nanotechnology company (more info here for those interested). The Chief Scientist wasn’t that interested in explaining what they did until I described some of our research projects on gene expression. He then went into great detail about what they were doing and how he believed it would change what we do in the future. I saw that and agreed, but also started thinking of the potential for leveraging nanotechnology with medicine.
I was listening to the “TED Radio Hour” while driving today and heard a segment about entrepreneur Richard Resnick. It was exciting because it got me thinking about this again, and this is a topic that I haven’t thought about much for the past few years (the last time was contemplating how Actian’s analytic products could be useful in this space).
There are efforts going on today with custom medicines that target specific genes. The genetic modifications being performed on plants today will be performed on humans in the near future (I would guess 10-15 years). The body is an incredibly adaptive organism, so it will be very challenging to implement anything that is consistently safe and effective. But, that day will come, and many people will be upset when it finally does.
It’s not a huge leap to go from genetically modified “treatment cells” to nanotechnology. Just think, machines that can work independently within is, do what they are programmed to do, and more importantly identify adaptations as they occur and alter their approach accordingly. To me, this is extremely exciting. It’s not that I want to live to be 100 – because I don’t. But, being able to do things that have a positive impact on the quality of life for children and their families is a worthy goal from my perspective.
Keep learning, keep an open mind, and see what you can personally do to make a difference. You will never know unless you try.
I’ve mentioned in the past that I like to read, experiment, and learn as much as possible about as many thing as possible. My goal isn’t to be the Jack of all trades and Master of none. Rather, I view knowledge and experience as pieces that can be used to build a mosaic of something interesting and/or worthwhile.
Years ago when I first started programming my manager had me work with the top performers in the group. Being inquisitive and wanting to improve, I asked a lot of questions to understand why things were done the way they were, and learn as much as possible. One Analyst I worked with was extremely sensitive, and after fielding a few questions he told me, “Programming is like art, two people will interpret things in two different ways, but in the end you will have two pictures that are similar and both do the job. So, quit messing with my picture.”
At first I was somewhat offended, but then I realized that much of what he told me was right. That led me to incorporate better methods and approaches into what I did, making them my own, as a way to continually improve. It really was a lot like art from that perspective.
What makes teaching worthwhile to me is helping people improve in ways that are their own, rather than teaching them how to do things in one specific “right” way. One analogy is that you are teaching people to navigate, rather that providing them with the route. Also, in order to be a good teacher you need to have a solid grasp on the topic, be willing and able to relate to students, and want to help them learn. It’s rewarding on a couple of different levels.
Amazing teachers are out there, and I’ve met many of them. Those people are worth their weight in gold – especially when they are teaching your children. They have their own kind of “magic” that can inspire people and help them do more than they ever thought was possible.
But, those aren’t the people that the rest of this post is about. This is about corporate trainers, training companies, and consultants that engage in training, and people who just view training as a way to make an easy buck. Good content is expensive to create, and good trainers need to have subject matter expertise and a passion for what they do (similar to good teachers in any context).
The book that I am currently reading is, “Leadership and Training for the Fight” by Paul R. Howe, a retired U.S. Army Special Operations Master Sergeant. The focus of the book is on mindset and mental discipline – two things that I believe are very important (which is how I stumbled across this book).
While there is a lot of good content in this book, there was one section that struck a chord with me. So, here it is:
“Why do you want to teach?
Before you can begin teaching, you must ask yourself the most basic question: Why do you want to teach? Are you driven by ego, your self-esteem? Do you merely like to hear yourself talk, or do you have a genuine desire to pass information along?”
To paraphrase the rest, adding my own take, take the time to first figure out why you want to teach. Is it to bolster your ego to feel important? Or, does it look to be an easy road to a steady paycheck? These things don’t add value for your students, and most will easily see right through you. A good reputation is hard to get and easy to lose – words to live by in my book.
However, if you are looking to honestly help, are willing to continue to learn yourself to be the best that you can be, and able know when you are in over your head so you can look for help – then that is a great start. Every time you are handed a lesson plan try to make it better than when you received it. Finally, give it your all and really try to make a positive impact. It only requires a bit of work ethic and pride.
Your ability to teach well starts with your understanding of the topic, but that is just the foundation. Being able to apply a seemingly abstract concept to a concrete problem is a very helpful skill. Being open to other approaches that might seem strange at first but then you see the brilliance in the solution is also helpful.
Teaching is about helping others, and not trying to be the smartest person in the room. And remember, not everyone wants to learn and/or improve so don’t take that personally. Just do your best to help people grown and improve. To me, that’s the right reason to want to teach.
A while back I wrote a post titled, To Measure is to Know. That is only part of the story, so please read on.
The other side of the coin is that what you measure defines how people behave. This is an often forgotten aspect of Business Intelligence, Compensation Plans, Performance reviews, and other key areas in business. While many people view this topic as “common sense,” based on the numerous incentive plans that you run across as a consultant it seems that is not the case.
Is it a bad thing to have people respond by focusing on specific aspects of their job that they are being measured on? That is a tough question. This simple answer is, “sometimes.” This is ultimately the desired outcome of implementing specific key performance indicators (KPIs), but it doesn’t always work. So, let’s dig into this a bit deeper.
One prime example is something seemingly easy yet often anything but – Compensation Plans. When properly implemented these plans drive organic business growth through increased sales and revenue (both likely items being measured), as well as drive steady cash flow by constantly closing within certain periods (usually months or quarters). What could be better than that?
Salespeople focus on the areas where they have the greatest opportunity to make money. Presumably they are selling the products or services that you want them to based on their comp plans. Additionally, certain MBO (management by objective) goals are presumably focused on positive outcomes that are important to the business, such as bringing-on new reference accounts. Those are forward looking goals that increase future (as opposed to immediate) revenue. In a perfect world, with perfect comp plans, all of these business goals are codified and supported by motivational financial incentives.
Some of the most successful salespeople are the ones that primarily care only about themselves. They are in the game for one reason – to make money. Give them a plan that is well constructed and allows them to win and they will do so in a predictable manner. Paying large commission checks should be a goal for every business, because when they are doing that their own business is prospering.
But, give a salesperson a plan that is poorly constructed and they will likely find ways to personally win in ways that are inconsistent with company growth goals (e.g., paying commission based on deal size, but not factoring in the overall impact of excessive discounts). Even worse, give them a plan that doesn’t provide a chance to win and the results will be uncertain at best.
Just as most tasks tend to expand to use all time available, salespeople tend to book most of their deals at the end of whatever period is being used. With quarterly cycles most of the business tends to book in the final week or two of the quarter – something that is not ideal from a cash flow perspective. Using shorter monthly periods may increase business overhead, but the potential to significantly increase business from salespeople working harder for that immediate benefit will likely be a very worthwhile tradeoff.
What about motiving Services teams? What I did with my company was to provide quarterly bonuses based on overall company profitability and each individual’s contribution to our success that quarter. Most of our projects used task oriented billing where we billed 50% up-front and 50% at the time of the final deliverables. You needed to both start and complete a task within a quarter to maximize your personal financial contribution, so there was plenty of incentive to deliver and quickly move to the next task. As long as quality remains high this is a good thing.
We also factored-in salary costs (i.e., if you make more than you should be bringing-in more value to the company), the cost of re-work, and non-financial items that were beneficial to the company. For example, writing a white paper, giving a presentation, helping others, or even providing formal documentation on lessons learned added business value and would be rewarded. Everyone had the right motivation, performed work and delivered quality work products as needed, and made good money doing so.
This approach worked very well for me, and was continually validated over the course of several years. It also fostered innovation, because the team was always looking for ways to increase their value and earn more money. Many tools, processes and procedures came out of what would otherwise be routine engagements.
Mistakes with comp plans can be costly – due to excessive payouts and/or because they are not generating the expected results. Back testing is one form of validation as you build a plan. Short-term incentive programs are another. Remember, where there is not risk there is little reward, so accept the fact that some risk must be taken to find the point where the optimal behavior is fostered.
It can be challenging and time consuming to identify the right things to measure, the right number of things (measuring too many or too few will likely fall short of goals), and provide the incentives that will motivate people to do what you want or need. But it is definitely worthwhile work if you want your business to grow and be healthy.
This type of work isn’t rocket science, and therefore is well within everyone’s reach.
Let me preface this by saying both are important in business, and both are complementary roles. But, if you don’t recognize the difference it becomes much harder to execute and realize value / gain a competitive advantage. Having great ideas that are not understood or validated is pointless, just as being great at “filling in the gaps” to do amazing things does not accomplish much if what you are building achieves little. This post is about Dreaming Big, and then turning those dreams into actionable plans.
The visionary person has great ideas but doesn’t always create plans or follow-through on developing the idea. There are many reasons why this happens (distractions, new interests, frustration, lack of time), so it is good to be aware of that as this type of person can benefit by being paired with someone who is willing and able to understand a new idea or approach, and then take the next steps to flesh out a high-level plan to present that idea and potential benefits to key stakeholders.
The insightful person sees the potential in an idea, helps others to understand the benefits and gain their support, and often creates and executes a plan to prototype and validate the idea – killing it off early if the anticipated goals are unachievable. They document, learn from these experiences, and become more and more proficient with validation of the idea or approach and quantification of the potential benefits. Neither of these types of people are affected by loss aversion bias.
It’s amazing how frequently you hear someone referred to as being Visionary, only to see that the person in question was able to eliminate some of the noise and “see further down the road” than most other people. While this is a valuable skill to have, it is more akin to analytics and science than art. Insight usually comes from focus, understanding, intelligence, and being open minded. Those qualities are important in both business and personal settings.
On the other hand, someone who is visionary looks beyond what is already illuminated and can be detected or analyzed. It’s like a game of chess, and the visionary person is thinking six or seven moves ahead. They are connecting the dots while others are still thinking about their next move.
The interesting thing is that this can be very frustrating situation for everyone. The person with the good idea may become frustrated because they feel that they are misunderstood or ignored. The people around that visionary person become frustrated, wondering why that person isn’t able to focus on what is important or why they fail to see / understand the big picture. Those visionary ideas and suggestions are viewed as tangential or even irrelevant. It is only over time that the others understand what the visionary person was trying to show them – often after a competitor has started to execute on the idea. And, the insightful person that wants to make a difference can feel constrained in environments that are static and have little opportunity for change.
Both the insightful person and the visionary people feel that they are being strategic. Both are focused on doing the right thing. Both have similar goals. That’s what is truly ironic. They may view each other as the competition, rather than seeing the potential of collaborating. This is where a strong management team can have a positive impact by putting these people together and providing a small amount of time and resources to explore the idea. When I had my consulting company I sometimes joked, “What would Google do?”
The insightful person may see a payback on their ideas much sooner than the visionary person, and I believe that is due to their focus on what is already in front of them. It may be a year or more before what the visionary person has described shifts to the mainstream and into the realm of insight – hopefully before it reaches the realm of common sense (or worse yet, is completely passed by).
My recommendation is that people create a system to gather ideas, along with a description of what the purpose, goals, and advantages of those ideas are. Foster creative behavior by rewarding people for participation, whether or not the ideas are used. Then, review those ideas on a regular basis. With any luck you will find some good ideas – some insightful and possibly some even visionary. Look for commonalities and trends to identify the people who are able to cut through the noise or see beyond the periphery, and the areas with the greatest potential for innovation. This approach will help drive your business to the next level.
You never know where the next good idea will come from, and supporting efforts like these provide opportunities to grow – people, products, and profits.
I have been very fortunate throughout my career. There have been incredible opportunities, risks with big rewards, and lessons learned from mistakes and failure (e.g., one of the biggest lessons learned early is that most mistakes will not kill you, and therefore you can find a way to recover from them). But, what I believe was most helpful were the people who saw something in me and invested in my career.
None of these people had to help me. It’s possible that they did so for their own benefit (i.e., the better I do my job the easier it is for them), but I believe they were passing along a gift. I was lucky have started receiving these gifts early in my career, as they have been very valuable both personally and professionally.
As a mentee, you often do not recognize either the value of what you are receiving, or the effort going into providing that gift to you. Its only years later that you fully appreciate what others have done for you.
In my first programming job I had a manager who had me work with people, would follow-up every time to ask what I had learned, and one day gave me my first project. I had six months experience, this was a big project for an automotive customer (the first customer loyalty coupon system for a major auto manufacturer back in late 1980’s), and it had to be delivered in six months. He let me build it, checked to see if I had questions, would provide feedback and direction if I asked, but pretty much left me alone.
After two months I thought I was finished. He picked the system apart, showing me the flaws and discussing the logic and reasoning behind my decisions. He told me I still had time and asked me to do it again. After another two months or so he told me this would work, and would be acceptable from anyone else. He reminded me that I still had time. When I came back with the third iteration system he reviewed it, smiled, and said that he could not have done this better himself. I learned so much in those six months.
As a manager this person had so many reasons not to give me the project, to just tell me what to do, and to not let me redo it (twice). From a short-term management perspective what he did was wasteful. But, from a big picture perspective he was doing things that helped me create so much more value for the company for the 3-4 years I continued to work there. The benefit certainly outweighed the cost, and this person (Jim) was wise enough to see that.
Several years ago there was a young woman in Australia that contacted me via LinkedIn, asking for suggestions on improving her skills in order to advance her career. I gave her many assignments over the course of a year and she did amazing work. She advanced in her company, later relocated to another country, and then switched industries. She currently holds a high-level position and has been very successful.
From my perspective it all comes down to how you view people and relationships. Are they like commodities that are used and replaced as needed, or are they assets that can grow in value? I like to think that I have helped the “career portfolio” of several people – which helps ensure that business is not a zero sum game. Hopefully those people will do the same thing, creating leverage on those investments.
So, what do you think?