Technology
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!
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?
The Future of Smart Interfaces
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 to structure queries to get better results. She replied that search engines should be smarter than that. I explained that sometimes the problem is that search engines look at your past searches and customize results as an attempt to appear smarter or to motivate someone to do or believe something.
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, as well as occasional third-party validation.
Then the other day I saw this commercial from Motel 6 (“GasStation Trouble”) where a man has problems getting good results from his smartphone. That reminded me of seeing someone speak to their phone and get frustrated by the responses. 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 smartphones 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 suggested alternative queries based on intuition. That would have been helpful for the gentleman trying to find a restaurant.
Despite having 50 years of science fiction examples, we are still a long way off from realizing the goal of a truly intelligent interface. Like many new technologies, they were originally envisioned by science fiction writers long before they appeared in science.
A spectrum of common beliefs about modern interfaces seems to exist. On one end, some products make visualization easy, facilitating understanding, refinement, and drill-down of data sets. Tableau is an excellent 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. Several other vendors offer powerful analytics platforms. In reality, you need a strong front end and back end to achieve the full potential of either.
But there is so much more potential…
I predict that within the next 3 – 5 years, we will see business and consumer interface examples (powered by AI and Natural Language Processing, or NLP) 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 understand the request (not just the string of words), intuit our needs, and quickly and easily generate correct answers. 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, it 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, and develop the proper query based on the available data. That is important, but it only gets you so far.
Data will come from many sources – things that we consider today with relational, object, graph, and NoSQL databases. Structured and unstructured data with inconsistent formats 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 image searches), and, in the case of videos, analyze speech and background noises. 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, providing both content and context.
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, it will allow you 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, driving 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 will someday be created with a few simple commands. Games are becoming more responsive to input from multiple sources, and may someday use things like eye movement for guidance. It is not a stretch to believe that the next generation of smart interfaces will come from this direction or something similar, like leading-edge defense technology (rather than 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 – which will likely be driven by AI. 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 to be interesting times indeed.
Spurious Correlations Follow-up
In an earlier post, I wrote about spurious correlations. Over the weekend, I ran across a site that focuses on finding and posting amusing, spurious correlations. While the posts are intended to be funny, they make some very valid points. So, check it out, let me know what you think, and have some fun!
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!
Where Ideas, Experiences, and Lessons Learned Intersect 