I get this question a lot, “How do we know things before they happen?”, for example, it was known that Donald Trump would be president way back in the year 2000. Think about it, that’s 16 years before it happened. How could we possibly know so far ahead?
To make you aware of how we knew, I could just blurt out a simple short sentence with as few as 5 or so words, you would hear it, and that would effectively end the conversation because those 5 words, although correct, would be lacking one very important factor…… “Context”.
the circumstances that form the setting for an event, statement, or idea, and in terms of which it can be fully understood and assessed.
When it comes to unstructured information, “Context” is king, but the majority of people look at information from a very different perspective, “Bias”. Most people in the world filter information through pathways of bias, making unstructured data impossible for them to even be aware of, let alone understood once seen or heard.
- Examples of restrictive bias:
- Who is the source?
- Democrat or Republican
- Fox or MSNBC
- Priest or Monk
- Local or Foreign
- Elite or Street
- Who is the source?
As you can see above people have built-in, pre-defined biases that they rely on to judge whether or not to even listen or review new information. Some people will walk away based on who provided the information and others may turn away if the information doesn’t support a religious or political viewpoint. So, a large portion of information is not only ignored by most people but to make matters worse since most people only look to certain sources for information, those sources themselves have biases that restrict what information they share and propagate to the masses.
Unstructured data doesn’t follow any of the above mentioned common practices for engaging new information. Unstructured data follows a completely different process known as “Pattern Recognition”. This method eliminates bias as a filter in its entirety and relies solely on confirmation of past successful ties within associated data. In other words, the source is only a source when it has previously supplied information that later turns out to be true and amplifies its viability as a source for any additional information that also proves to be true.
- How many correct hits?
- Did the source provide correct information?
- How many times was the source correct?
- How accurate was the information in relation to the event?
- How many correlating pings?
- Did any other source have similar information?
- Are there clusters of events popping up from multiple validated sources?
- Have we seen this pattern before?
- How many correct hits?
Keep in mind that unstructured data is not restricted to traditional sources, unstructured data is hidden in plain sight, embedded openly within a source not necessarily focused on the topic at hand. It is often presented as a form of fiction, art, poetry or even a reoccurring pattern between multiple media outlets.
This is a difficult concept to accept so we are going to provide you with several examples to review and process. I recommend that you take your time with each one and try to fully understand the implications of each occurrence.
Let’s start with something easy and straightforward, a fictional story called “The Titan”. This is a fictional story written by a man named Morgan Robertson in 1898 called “The Titan”. It’s important to note that this book was written more than a decade before the Titanic disaster. Let’s review the details.
Four key points here; The “Titan”, “Hit an Iceberg”, and “Sank in April”, “Near Midnight”. All four of these hits are obvious direct connections to the Titanic but keep in mind that in 1898 there was no Titanic. What you are seeing on that side by side comparison is a newspaper worthy front page story. A report of what happened to the Titanic 14 years before it happened, years before it was created and years before anyone even had a plan to make the ship. There are actual police reports of crimes with less information than this used to find perpetrators.
This is a fantastic amount of detail to have so far in advance. Since this is not a traditional source of information and is listed as fictional it gets very little, if any, attention. It does not fall within the guidelines of trusted information sources. Think about it, if it had been a trusted source and you had access to it would you have gotten on the Titanic?
Let’s try another example: In 1838 Edgar Allan Poe wrote a fictional Novel called “The Narrative of Arthur Gordon Pym of Nantucket”. He tells a story of 4 men who are stranded out to sea and find themselves without adequate food or water, starving and dehydrated. To survive they begin to drink urine and the blood of a captured sea turtle. To avoid death, “Richard Parker” suggests that they draw straws to decide which one of them should be killed for the others to consume and survive. “Richard Parker” picks the short straw and is cannibalized by his fellow survivors.
46 Years later, four real men traveling from England to Australia found themselves fighting to survive aboard a lifeboat after their yacht was capsized by a huge wave. To survive they begin to drink urine and the blood of a captured sea turtle. To avoid death by starvation they chose to draw straws to decide who should be sacrificed and consumed for the sake of the crew. The crew member who had the shortest straw was a man named “Richard Parker”. He was cannibalized by his fellow survivors.
- Both: Have 4 crew members
- Both: Drink urine and the blood of a captured sea turtle
- Both: Drew straws and then cannibalized a man named Richard Parker
The key point here is “Richard Parker”, Edgar Allan Poe, 46 years earlier, correctly provides details of a horrific situation and the full name of the individual cannibalized within it. This later becomes a world known court case for the survivors and sets nautical morality regulations around the world.
The last example before we get to Donald Trump: A music band is created in the 90’s named “I Am the World Trade Center”. They released a song named “September” on track 11 in the album only a few months before 9/11:
In July 2001, we can literally see the information “September 11th World Trade Center”. Keep in mind this is a music band releasing songs, not predictions.
These are but a few examples I selected out of MANY others that are just as worthy to be in this post. Let’s take a much simpler, honest and straightforward approach by treating the information the same way a machine or computer would treat it, as just data. This will allow us to see ahead of the event and position ourselves in a way that benefits us.
We do this by finding a pattern of success and then expanding the pattern out to see what else it can provide. For instance, we found that a fictional book can provide credible information about future events, so we started looking at other fictional books to see if more connections could be made, and they were. We also started looking at more writings from authors who have previously provided accurate information to see if they had additional direct hits, and they did. We then started looking for information that had not yet come into realization and waited to see if it would one day occur, and it has.
In this final example, we are going to look at Donald Trump winning the election and see if we can articulate how we knew in advance.
To do this we are going to use a popular TV show called “The Simpsons”. Let’s take a close look at the unstructured data and see if there is a pattern that can be recognized.
Simpson Episode In 1994, a fake product showing a logo of an apple with a worm having trouble understanding the voice directions of its owner.
Not only were The Simpsons right about this happening in the future when Siri was first released but they even indicated that Apple would be the company to provide the product.
In the 1990 episode “Two Cars in Every Garage and Three Eyes on Every Fish”, the local nuclear power plant runoff water causes fish to mutate with 3 eyes in the local lake.
In 2011, a three-eyed fish was pulled from a reservoir in Argentina. The reservoir in question was fed by water from a nuclear plant in the province of Córdoba.
Simpson Episode In 1993, Gunter and Ernst were attacked by one of their tigers on stage during an entertainment act.
In 2003 Siegfried and Roy attacked by one of their tigers on stage during an entertainment act.
In an episode in 1997, Marge is trying to cheer up Bart as he lies on his bed. “Would you like to read a book?” she asks as she holds up a book called Curious George and the Ebola virus.
“I already did,” Bart says as he points to an apocalyptic drawing on the wall showing a pile of dead bodies.
In December 2013, an outbreak of the Ebola virus started in Guinea and quickly spread to Liberia and Sierra Leone.
Over the next few years, there were 28,657 reported cases and more than 11,300 deaths.
The episode in 1998, S10 E2 ‘The Wizard of Evergreen Terrace’, Homer Simpson predicted the mass of the Higgs Boson particle
14 Years later, On 4 July 2012, CERN’s Large Hadron Collider announced they had each observed a new particle in the mass region around 126 GeV. The Higgs boson.
The episode in 2012, S23 E10 ‘Politically Inept’, A ticker on a rolling news station that Homer appears on reads “Europe puts Greece on eBay”.
Three years later the government debt meltdown in Greece saw it become the first country to default on an IMF loan repayment and go bankrupt.
Episode: ‘The Simpsons Movie’ A chance remark from Marge chastising Lisa for worrying about spies while on the run sparks an alarm in an NSA building. The building contains thousands of workers listening to private conversations across the country and Marge’s loose lips lead to the family’s arrest.
Six years before Edward Snowden blew the whistle on the true extent of the NSA’s spying on American citizens, with an extensive surveillance network of the National Security Agency. Up until that moment, this was at best a conspiracy to US citizens.
Episode: S25 E16 ‘You Don’t Have to Live Like a Referee’
Homer is visited by the executive vice president of the “world football federation” who wants him to be a referee in the upcoming World Cup. He’s promptly arrested by American authorities for corruption and carted off.
(A Double Hit in One) A year before the football world was turned upside when a series of high-ranking FIFA officials were arrested by the FBI on charges of corruption. The episode even predicted Germany as the winners of that year’s tournament.
In 2005 episode: “The Simpsons” apparently predicted the Super Bowl XLVIII matchup between the Denver Broncos and the Seattle Seahawks.
In 2013, Years later was an American football game between the Conference (AFC) champion Denver Broncos and (NFC) champion Seattle Seahawks to decide the (NFL) champion for the 2013 season.
From a 2009 episode that doesn’t just predict a new Star Wars movie, but it’s competition at the same time. An image in the episode shows two movie posters, both of which were later hanging in movie theaters around the country, showed a new Star Wars movie competing against a new Chipmunks film.
We use the term competing loosely of course, as “The Force Awakens” has set nearly every opening weekend record. Of all the things, it’s an interesting pair of movies to put next to each other. By 2009, the prequel trilogy had been over for years, and there was little discussion beyond fanboy dreams that there would be any future Star Wars films.
The pattern we found in this unstructured data is “Precise One Off’s”. Each bit of data focuses on just a single event in the future. Each event seems somewhat implausible, random or unlikely to transpire but does anyway.
Now that we see the pattern and know how it works let’s look at this hit below.
|Donald Trump||The episode in 2000 S11 E17 ‘Bart To The Future’|
Lisa has become President and, in a scene where she addresses her inner circle, she says: “We’ve inherited quite the budget crunch from President Trump.” They then go on to talk about Donald Trump as president multiple more times and the country now being completely broke.
|Donald Trump becomes president in 2016|
Using the recognized pattern, we have locked onto the moment in the episode that meets the requirements for the correct context to track unstructured information from this source.
Now any good news reporter or analyst would ask very reasonable questions like,
- “Where are they getting the information”
- “How did they know”
At some point, you are also going to have people saying things like:
- “It’s just a coincidence”
- “Given enough time anyone can get something right”
This is most likely going to be the most important concept that I can share with you on how unstructured data benefits you and will also be the most difficult for most people to follow.
- “Nothing matters but the data.”
- “The who and why is irrelevant.”
To help you understand, answer the following question: What is the most relevant bit of information in the following 3 lines in respect to yourself?
- The lottery numbers for next week will come from a source in Ohio.
- The lottery numbers can be determined at random given enough tries.
- The lottery numbers are 199-36-82-12-114
As you can see in the example having the numbers to the next lottery can be extremely advantageous whereas knowing where they come from or the name of the source has no effect on your direct financial future. This is the method we use to pursue unstructured data. We simply identify a valid confirmed source though pattern recognition and then we use that information to get ahead of the event.
I will be posting more information on this site for anyone who wants to take a deeper dive into the world of unstructured data. Let me know if you have any questions and thanks for reading.
Side Note: That episode with the Simpsons about Trump had more information on the impact of his presidency….. I recommend you watch the episode.
- This is one source with multiple hits that prove to be valid for individually separate subjects or events.
- Other patterns can be multiple sources, not validated, but all focused on a single subject or event.
- Or even a mix of the two patterns.