analytics
Using Themes for Enhanced Problem Solving
Thematic Analysis is a powerful qualitative approach used by many consultants. It involves identifying patterns and themes to better understand how and why something happened, providing context for other quantitative analyses. It can also be used when developing strategies and tactics because of its “cause and effect” nature.
Typical analysis tends to be event-based. Something happened that was unexpected. Some type of triggering or compelling event is sought to either stop something from happening or to make something happen. With enough of the right data, you may be able to identify patterns that help predict what will happen next based on past events. This data-based understanding may be simplistic or incomplete, but often it is sufficient.
But people are creatures of habit. If you can identify and understand those habits and place them within the context of a specific environment that includes interactions with others, you may be able to identify patterns within the patterns. Those themes can be much better indicators of what may or may not happen than the data itself. They become better predictors of things to come and can help identify more effective strategies and tactics to achieve your goals.
This approach requires that a person view an event (desired or historical) from various perspectives to help understand:
- Things that are accidental but predictable because of human nature.
- Things that are predictable based on other events and interactions.
- Things that are the logical consequence of a series of events and outcomes.
Aside from the practical implications of this approach, I find it fascinating relative to AI and Predictive Analysis.
For example, you can proactively monitor data, activities, and patterns by understanding recurring themes and triggers. That provides actionable intelligence that can be automated and incorporated into a larger system. Machine Learning and Deep Learning can analyze tremendous volumes of data from various sources in real-time.
Combine that with Semantic Analysis (and products like AllegroGraph) to harness the power of language through taxonomies and ontologies. The monitoring system would better understand what is happening (not just be aware of it), possibly determine how and why it happened, and result in even more focused and accurate predictions. Finally, include spatial and temporal data such as IoT, metadata from photographs, etc., and you should be able to view something as though you were very high up – providing the ability to “see” what is on the path ahead. It is obviously not that simple, but it is exciting.
This approach provides a multi-dimensional view of events and their causality, giving you the means to identify and prevent complex problems before they negatively affect a business.
Keeping these thoughts in mind will help you see details others have missed. Better tools can make for better analysis, better strategies, and better outcomes. Who wouldn’t want that?
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.
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!
A missed opportunity for Geospatial
I have a Corvette that I like to work on for fun and relaxation. It gives me an excuse to learn something new and an opportunity to hone my troubleshooting skills. It can be a fun way to spend a few hours on a weekend.
A few weekends ago, I was looking for a few parts for a small project. This was spur-of-the-moment and didn’t need to be done right away (as the car will be stored soon for the winter). I found the parts I needed from a single company, but then something strange happened.
The website had my address and knew the two parts I wanted, but the process was not easy and almost cost them the sale. This company forced me to manually check five different store locations to see if they had both parts. In this case, two of the five did. One store was about 5 miles from my house and the other about 20 miles away.
It would have been helpful if this website used the available data (inventory and locations) to present me with two options, or, better yet, default to the closest store and note the other store as an option. Using spatial features, this would be extremely easy to implement. It’s the equivalent of the “Easy Button” that one office supply company uses in their commercials.
Now, let’s take this example one step further. The website makes things quick and easy, leaving me with a very pleasant shopping experience. It could then recommend related items (it did, but by that time I had wasted more time than necessary and was questioning whether or not I should start that project that day). The website could also create a simple package offer (e.g., auto wax and polishing supplies) to increase my cart value while leaving me impressed with the convenience. While this last portion isn’t spatial, it is complementary technology that enhances the value provided by spatial technology.
All simple things that would generate more money through increased sales and larger sales. It is easy to justify from both a business and technical perspective, assuming the company is aware of this issue.
I frequently tell my team that, “People buy easy.” Help them understand what they need to accomplish their goals, price it fairly, demonstrate the value, and they make the rest of the sales process easy. This makes happy customers and leads to referrals. It makes good business sense.
So, while geospatial technology might not be the solution to all problems, this is a specific use case where it would. The power of computing systems and applications today is that so much can be done so fast, often with reasonably low technology investment. The first step is to ask yourself, “How could we be making this process easier for our customers?”
A little extra effort and insight can pay off big for your business.
Spurious Correlations – What they are and Why they Matter
In an earlier post, I mentioned that one of the big benefits of geospatial technology is its ability to show connections between complex and often disparate data sets. As you work with Big Data, you tend to see the value of these multi-layered, often multi-dimensional perspectives on a trend or event. While that can lead to incredible results, it can also lead to spurious data correlations.
First, I am not a Data Scientist or Statistician, and there are definitely people far more expert on this topic than I am. But, if you are like the majority of companies out there experimenting with geospatial and big data, it is likely that your company doesn’t have these experts on staff. So, a little awareness, understanding, and caution can go a long way in this scenario.
Before we dig into that more, let’s think about what your goal is:
- Do you want to be able to identify and understand a particular trend – reinforcing actions and/or behavior? –OR–
- Do you want to understand what triggers a specific event – initiating a specific behavior?
Both are important, but they’re different. My focus has been identifying trends so that you can leverage or exploit them for commercial gain. While that may sound a bit ominous, it is really what business is all about.
A popular saying goes, “Correlation does not imply causation.” A common example is that you may see many fire trucks for a large fire. There is a correlation, but it does not imply that fire trucks cause fires. Now, extending this analogy, let’s assume that the probability of a fire starting in a multi-tenant building in a major city is relatively high. Since it is a big city, most of those apartments or condos likely have WiFi hotspots. A spurious correlation would be to imply that WiFi hotspots cause fires.
As you can see, there is definitely the potential to misunderstand the results of correlated data. A more logical analysis would lead you to see the relationships between the type of building (multi-tenant residential housing) and technology (WiFi) or income (middle-class or higher). Taking the next step to understand the findings, rather than accepting them at face value, is very important.
Once you have what looks to be an interesting correlation, there are many fun and interesting things you can do to validate, refine, or refute your hypothesis. Even without high-caliber data experts and specialists, you can likely identify correlations and trends that can give you and your company a competitive advantage. Don’t let the potential complexity become an excuse for not getting started. As you can see, gaining insight and creating value with a little effort and simple analysis is possible.
Where Ideas, Experiences, and Lessons Learned Intersect 