innovation
The Unsung Hero of Big Data
Earlier this week, I read a blog post regarding the recent Gartner Hype Cycle for Advanced Analytics and Data Science, 2015. The Gartner chart reminded me of the epigram, “Plus ça change, plus c’est la même chose” (asserting that history repeats itself by stating the more things change, the more they stay the same).
To some extent, that is true, as you could consider today’s Big Data as a derivative of yesterday’s VLDBs (very large databases) and Data Warehouses. One of the biggest changes, IMO, is the shift away from Star Schemas and practices implemented for performance reasons, such as aggregation of data sets, using derived and encoded values, using surrogate and foreign keys to establish linkage, etc. Going forward, it may not be possible to have that much rigidity and still be as responsive as needed from a competitive perspective.
There are many dimensions to big data: A huge sample of data (volume), which becomes your universal set and supports deep analysis as well as temporal and spatial analysis; A variety of data (structured and unstructured) that often does not lend itself to SQL based analytics; and often data streaming in (velocity) from multiple sources – an area that will become even more important in the era of the Internet of Things. These are the “Three V’s” people have talked about for the past five years.
Like many people, my interest in Object Database technology initially waned in the late 1990s. That is, until about four years ago, when a project at work led me back in this direction. As I dug into the various products, I learned they were alive and doing well in several niche areas. That finding led to a better understanding of the real value of object databases.
Some products try to be “All Vs to all people,” but generally, what works best is a complementary and integrated set of tools working together as a service within a single platform. It makes a lot of sense. So, back to object databases.
One of the things I like most about my job is the business development aspect. One of the product families I’m responsible for is Versant. With the Versant Object Database (VOD – high performance, high throughput, high concurrency) and Fast Objects (great for embedded applications like kiosks). I’ve met and worked with brilliant people who have created amazing products based on this technology. Creative people like these are fun to work with, and helping them grow their business is mutually beneficial. Everyone wins.
An area where VOD excels is with the near real-time processing of streaming data. The reason it is so adept at this task is the way that objects are mapped out in the database. They do so in a way that essentially mirrors reality. So, optionality is not a problem – no disjoint queries or missed data, no complex query gyrations to get the correct data set, etc. Things like sparse indexing are not a problem with VOD. This means that pattern matching is quick and easy, as well as more traditional rule and look-up validation. Polymorphism allows objects, functions, and even data to have multiple forms – something else that mirrors real life (just think about the variations of a peripheral device called a “printer”).
VOD and products like it do more by allowing data to be more, which is ideal for environments where change is the norm, such as: Cyber Security; Fraud Detection; Threat Detection; Logistics; and Heuristic Load Optimization. In each case, performance, accuracy, and adaptability are the key to ongoing success.
The ubiquity of devices generating data today, combined with the desire for people and companies to leverage that data for commercial and non-commercial benefit, is very different than what we saw 10+ years ago. Products like VOD are working their way up that “Slope of Enlightenment” because there is a need to connect the dots better and faster – especially as the volume and variety of those dots increases.
It is not a “one size fits all” solution, but it is often the perfect tool for complex data. More importantly, it is another tool to use in an ever-expanding data ecosystem.
These are indeed exciting times!
Non-Linear Thought Process and a Message for my Children
I have recently been investigating and visiting universities with my eldest daughter, a Senior in High School. Last week we visited Stanford University (an amazing experience) and spent a week in Northern California on vacation. After being home for a day and a half, I am in Texas for a week of team meetings and training.
On the first night of a trip, I seldom sleep, so I listened to the song “Don’t Let It Bring You Down” by Annie Lennox, a cover of a Neil Young song. That led to a YouTube search for the original Neil Young version, which led to me listening to “Old Man” – a favorite song of mine for over 30 years. That led to some reflection, which ultimately led to this post.
I mention this because it is an example of the nonlinear or divergent thought process (generally viewed as a negative trait) that occurs naturally for me. It helps me “connect the dots” faster and more naturally. It is a manner of thinking associated with ADHD (again, something generally viewed as negative). The interesting thing is that to fit in and succeed with ADHD, you tend to develop logical systems for focus and consistency. That has many positive benefits for me – such as systemic thinking, creating repeatable processes, and automation.
The combination of linear and non-linear thinking can really fuel creativity. The downside is that it can take quite a while for others to see the potential of your ideas, which can be extremely frustrating. But you learn to communicate better out of necessity. The upside is that you tend to create relationships with other innovators because they think like you, making you relatable and interesting to them. The world is a strange place.
It is funny how there are several points in your life when you have an epiphany, and things suddenly make complete sense. That makes you realize how much time and effort you could have saved if you’d figured something out sooner. As a parent, I always try to identify and create learning shortcuts for my children so they reach those points much sooner than I did.
I started this post thinking that I would document as many of those lessons as possible to serve as a future reminder and possibly help others. Instead, I decided to post a few things I view as foundational truisms in life that could help foster that personal growth process. So, here goes…
- Always work hard to be the best, but never let yourself believe you are the best. Even if you truly are, it will be short-lived, as there are always people doing everything they can to be the best. Ultimately, that is a good thing. You need to have enough of an ego to test the limits and capabilities of things, but not one that is so big that it alienates or marginalizes those around you.
- Learn from everything you do – good and bad. Continuous improvement is so important. By focusing on this, you constantly challenge yourself to try new things and find better (i.e., more effective, more efficient, and more consistent) ways to do things.
- Realize that the difference between a brilliant and a stupid idea is often perspective. Years ago, I taught technical courses, and occasionally someone would describe something they did that seemed strange or wrong. But if you asked questions and tried to understand why they did what they did, you would often identify the brilliance in that approach. It is both exciting and humbling.
- Incorporating new approaches or the best practices of others into your own proven methods and processes is part of continuous improvement, but it only works if you can set aside your ego and keep an open mind.
- Believe in yourself, even when others don’t share that belief. Remain open to feedback and constructive criticism as a way to learn and improve, but never give up on yourself. There is a huge but sometimes subtle difference between confidence and arrogance, and that line is often drawn at the point where you can accept that you might be wrong or that there might be a better way to do something. Become the person people like working with and not the person they avoid or want to see fail.
- Surround yourself with the best people that you can find. Look for people with diverse backgrounds and complementary skills. The best teams I have ever been involved with consisted of high achievers who constantly raised the bar for each other while simultaneously creating a safety net for their teammates. Those teams grew and did amazing things because everyone was very competitive and supportive of each other.
- Keep notes or a journal because good ideas are often fleeting and hard to recall. Remember, good ideas can come from anywhere, so keep track of others’ suggestions and make sure you attribute them to the proper source.
- Try to make a difference in the world. Try to leave everything you “touch” (job, relationship, project, whatever) in a better state than before you were there. Helping others improve and leading by example are two simple ways of making a difference.
- Accept that failure is a natural obstacle on your path to success. You are not trying hard enough if you never fail. But you are also not trying hard enough if you fail too often. That is very subjective, and honest introspection is your best gauge. Be accountable, accept responsibility, document the lessons learned, and move on.
- Dream big, and use that as motivation to learn new things. While I funded medical research, I learned about genetics, genomics, and biology. That expanded into interests in nanotechnology, artificial intelligence, machine learning, neural networks, and interfaces such as natural language and non-verbal/emotional communication. Someday I hope to tie these together to help cure a disease (Arthritis) and improve the quality of life for millions of people. Will that ever happen? I don’t know, but I do know that if I don’t try, it will never happen because of anything I did.
- Focus on the positive, not the negative. Creativity is stifled in environments where fear and blame rule.
- Never hesitate to apologize when you are wrong. This is a sign of strength, not weakness.
- And above all else, honesty and integrity should be the foundation for everything you do and are.
Hopefully, this will help my children become the best people possible, ideally early on in their lives. I was 30 years old before I had a clue about many of these things. Until that point, I was somewhat self-centered and focused on winning. Winning and success are good things, but are better when accomplished the right way.
Ideas are sometimes Slippery and Hard to Grasp
I started this blog to be an “idea exchange,” as well as a way to pass along lessons learned to help others. Typical blog advice is to focus on one thing and do it well to build a following. That is especially important if you want to monetize the blog, but that is not and has not been my goal.
One thing that has surprised me is how different the comments and likes are for each post. Feedback from the last post was even more diverse and surprising than usual. It ranged from comments about “Siri vs Google” to feedback about Sci-Fi books and movies to Artificial Intelligence.
I asked a few friends for feedback and received something very insightful (thanks, Jim). He stated that he found the blog interesting but wasn’t sure of the objective. He went on to identify several possible goals for the last post. Strangely enough (or maybe not), his comments mirrored the type of feedback that I received. That pointed out an area for improvement, and I appreciated that as well as the wisdom of focusing on one thing. Who knows, maybe in the future…
This also reminded me of a white paper written 12-13 years ago by someone I used to work with. It was about how Bluetooth would be the “next big thing.” He had read an IEEE paper or something and saw potential for this new technology. He used the example of your toaster and coffee maker communicating so that your breakfast would be ready when you walk into the kitchen in the morning.
At that time, I had a couple of thoughts. Who cared about something that only had a 20-30-foot range when WiFi had become popular and had a much greater range? In addition, a couple of years earlier, I toured the Microsoft “House of the Future,” where everything was automated and key components communicated. But everything in the house was all hardwired or used WiFi – not Bluetooth. It was easy to dismiss his assertion because it seemed impractical. The value of the idea was difficult to quantify, given the use case provided.
Looking back now, I see that white paper as insightful. If it was visionary, he would have come out with the first Bluetooth speakers, car interface, or even phone earpiece and gotten rich, but it failed to present practical use cases that were easy enough to understand yet different enough from what was available at the time to demonstrate the real value of the idea. His expression of the idea was not tangible enough and, therefore, too slippery to grasp and value.
I believe that good ideas sometimes originate where you least expect them. Those ideas are often incremental – seemingly simple and sometimes borderline obvious, often building on another idea or concept. An idea does not need to be unique to be important or valuable, but it needs to be presented in a way that makes it easy to understand the benefits, differentiation, and value. That is just good communication.
One of the things I miss most from when my consulting company was active was the interaction between a couple of key people (Jason and Peter) and myself. Those guys were very good at taking an idea and helping build it out. This worked well because we had overlapping expertise and experiences, as well as complementary skills and perspectives. That diversity increased the depth and breadth of our efforts to develop and extend those ideas by asking the tough questions early and ensuring we could convince each other of the value.
Our discussions were creative, highly collaborative, and a lot of fun. We improved from them, and the outcome was usually commercially viable. As a growing and profitable small business, you must constantly innovate to differentiate yourself. Our discussions were driven as much by necessity as intellectual curiosity, and I believe this was part of the magic.
So, back to the last post. I view various technologies as building blocks. Some are foundational, and others are complementary. To me, the key is not viewing those various technologies as competing with each other. Instead, I look for potential value created by integrating them. That may not always be possible and does not always lead to something better, but occasionally it does, so to me, it is a worthwhile exercise. With regard to voice technology, I believe we will see more, better, and smarter applications of it – especially as real-time and AI systems become more complex due to the use of an increasing number of specialized chips, component systems, geospatial technology, and sensors.
While today’s smartphone interfaces would not pass the Turing Test or proposed alternatives, they are an improvement over more simplistic voice translation tools available just a few years ago. Advancement requires tools that understand context to make inferences. This brings you closer to machine learning, and big data (when done right) significantly increases that potential.
Ultimately, this all leads back to Artificial Intelligence (at least in my mind). It’s a big leap from a simple voice translation tool to AI, but it is not such a stretch when viewed as building blocks.
Now think about creating an interface (API) that allows one smart device to communicate with another, like the collaborative efforts described above with my old team. It’s not simply having a front-end device exchanging keywords or queries with a back-end device. Instead, it is two or more devices and/or systems having a “discussion” about what is being requested, looking at what each component “knows,” making inferences based on location and speed, asking clarifying questions and making suggestions, and then finally taking that multi-dimensional understanding of the problem to determine what is really needed.
So, possibly not true AI (yet), but a giant leap forward from what we have today. That would help turn the science fiction of the past into science fact in the near future. The better the smart system’s understanding and inferences, the better the results.
I also believe that an unintended consequence of these new smart systems is that they will likely make errors or have biases like humans as they become more human-like in their approach. Hopefully, those smart systems will be able to automatically back-test recommendations to validate and minimize errors. If they are intelligent enough to monitor results and suggest corrective actions when they determine the recommendation does not produce the desired results, they would become even “smarter.” There won’t be an ego creating a distortion filter about the approach or the results. Or maybe there will…
Many of the building blocks required to create these new systems are available today. But it takes vision and insight to see that potential, translate ideas from slippery and abstract to tangible and purposeful, and then start building something cool and useful. As that happens, we will see a paradigm shift in how we interact with computers and how they interact with us. It will become more interactive and intuitive. That will lead us to the systematic integration I wrote about in a big data/nanotechnology post.
So, what is the real objective of my blog? To get people thinking differently, foster collaboration and partnerships between businesses and educational institutions to push the limits of technology, and spark discussion about what others believe the future of computing and smart devices will look like. I’m confident that I will see these types of systems in my lifetime, and I believe this could happen within the next decade.
What are your thoughts?
Big Data – The Genie is out of the Bottle!
Back in early 2011, other members of the Executive team at Ingres and I were betting on the future of our company. We knew we needed to do something big and bold, so we decided to build what we thought the standard data platform would be in 5-7 years. A small minority of team members didn’t believe this was possible and left, while the rest focused on making it happen. We made three strategic acquisitions to fill gaps in 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.
My mailbox is filled daily with stories, seminars, white papers, etc., about Big Data. While it feels like this is becoming more mainstream, reading and hearing the various comments on the subject is interesting. 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. Incredible opportunities await companies that understand and embrace its 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 we have tools to correlate it and understand so many things (activities, relationships, likes and dislikes, etc.). With smart devices that enable mobile computing, we have an extra dimension: location. And, with new technologies such as Graph Databases (based on SPARQL), graphical interfaces to analyze that data (such as Sigma), and identification technology such as Stylometry, it is getting easier to identify and correlate that data. Someday, this will feed into artificial intelligence, becoming a superpower for those who know how to leverage it effectively.
We are generating increasingly large 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. Organizations that perform the best analysis, get answers fastest, and act on that insight quickly are more likely to win than 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 remember that big data is just another tool. Like most tools, it can be misused and abused. Whether a particular application is viewed as “good” or “bad” depends on the goals and perspective of the entity using the tool (which may be the polar opposite of the groups targeted by those people or organizations). So, I won’t try to judge the various use cases; instead, I’ll present a few and let you decide.
Scenario 1 – Sales Organization: What if you could understand what you were being told a prospect company needs and 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 stakeholders’ and decision-makers’ goals, interests, and biases. 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: Many questions cannot be asked by a hiring manager. 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 allowing public records to be used. Moreover, implied data that is not officially considered could color a hiring manager’s or organization’s judgment. For instance, if you wanted to “get a feeling” that 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? Some people will do the work and write a paper for a student for a fee. 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 our credit card companies, banks, search engines, and social network sites already have about us. Add to that the identified information 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.
Consider the potential of gathering information from intelligent devices accessible through the Internet, your alarm and video monitoring system, etc. These are intended to be private data sources, but history has taught us 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 understand 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 accurately identify (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 in the practical application of data analysis, and companies that understand and embrace it will likely perform better than those that don’t. This technology also raises new ethical considerations and will likely bring new laws and regulations. But for now, the race is on!
Genetics, Genomics, Nanotechnology, and more
Science has interested me for most of my life, but it wasn’t until my first child was born that I shifted from “interested” to “involved.” My eldest daughter was diagnosed with Systemic Onset Juvenile Idiopathic Arthritis (SoJIA – originally called Juvenile Rheumatoid Arthritis, or JRA) when she was 15 months old, which also happened to be about six months into the start of my Consulting company, all while we were in the middle of a very critical Y2K ERP system upgrade and rehosting project. It was definitely a challenging time in my life.
At the time, there was very little research on JRA because it was estimated that only 30,000 children were affected by the disease, and the implication was that funding research would not have a positive ROI. The economics of medical research were eye-opening to me. This was also a few years before major breakthroughs like Enbrel for children.
One of the things that I learned was that this disease could be horribly debilitating. Children often had physical deformities as a result of this disease. Even worse, the systemic type that my daughter has could result in premature death. As a first-time parent, imagining that type of life for your child was extremely difficult.
Luckily, the company I had just started was taking off, so I decided to find ways to make a tangible difference for all children with this disease. We decided to donate 50% of our net profits to fund medical research. Our goal was to fund $1 million in research and find a cure for Juvenile Arthritis within the next 5-7 years.
As someone new to “major gifts” and philanthropy, I quickly learned that some gifting vehicles were more beneficial than others. While most organizations wanted you to start a fund (which we did), the impact tended to be more long-term, supportive, and much less immediate. Later, I met someone passionate, knowledgeable, and successful in her field who showed me a different, better approach (here’s a post that describes it in more detail).
I no longer wanted to blindly give money and hope it was used quickly and properly. Rather, I wanted to treat these donations like investments in a near-term cure. To be successful, I needed to understand research from both medical and scientific perspectives in these areas. That began a new phase in medical research and an independent learning journey in areas where I had limited understanding and expertise.
A lot was happening in 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. Except for me, the other members were talented, successful physicians who were also involved in medical research. We met quarterly, and I asked questions and made suggestions that made a difference. But unlike everyone else on the committee, I needed to study 40+ hours for each call to ensure I understood enough to add value and not be a distraction. Every quarter, I earned my seat at that table, and soon, most of the other members respected me for that (i.e., “not a typical donor”).
A few years later, we did work for a Nanotechnology company (more info here). The Chief Scientist wasn’t 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. That started my thinking about the potential of leveraging advanced nanotechnology in medicine.
While driving today, I was listening to the “TED Radio Hour” and heard a segment about entrepreneur Richard Resnick. It was exciting because it got me thinking about this again – a topic I haven’t thought about for the past few years (the last time, I was contemplating how new analytics products could be useful in this space).
There are efforts today with custom, personalized medicines that target only specific genes for a specific outcome. The genetic modifications being performed on plants today will likely be performed on humans in the near future (I would guess within 10-15 years as gene editing becomes more mainstream), possibly even performed by some type of robots. Because the body is an incredibly adaptive organism, it may be very challenging to implement anything consequential that is consistently safe and effective long-term. But that day will come.
It’s not a huge leap from genetically modified “treatment cells” to true nanotechnology (not just extremely small particles). Just think, machines that can be designed to work independently within us to do what they are programmed to do and, more importantly, identify and understand adaptations (i.e., artificial intelligence) as they occur and alter their approach and treatment plan accordingly based on findings and changes. This is extremely exciting. From my perspective, being able to do things that positively impact the quality of life for children and their families is a worthy goal.
My advice is to keep learning, stay open-minded, and do what you can to make a difference. You will never know what is possible unless you try. It will also be interesting to see how technologies evolve and work together to create an even greater impact.
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Where Ideas, Experiences, and Lessons Learned Intersect 