Do Your Own Research… Better? (Part 1 of 2)
An introduction to data triangulation
A quick note from me: This piece ended up being a lot longer than planned. I’ve decided to split it into two parts. This first part sets the context for and introduces the concept of “data triangulation.”
For anyone who has taken any kind of financial risk in recent years, whether from conventional trading and investing in securities to deeper forays into betting and speculation, you’ve probably bumped into the “DYOR!!” phenomenon that has become a movement online, even going as far as reaching “meme” status in certain cases.
So what does everyone really mean when they respond with “Do Your Own Research” every time anything with a financial implication implodes with sufficient fallout that troves of “Long time reader, this is a throwaway account; this happened, what do I do?” posts come piling in on Reddit?
While you do have some who go to great lengths to share how they research and even try to disseminate information to help others, for most responses, I sometimes feel like it's more of a slap in the face bundled with a “I told you so, lol” to the ones affected.
One walks off feeling embarrassed and ashamed, with many having to deal with the real implications of their losses, never actually learning from the experience.
What I find scary is how many people forget the scale and frequency at which this happens. And despite most having had at least one similar experience themselves that they can relate to, you often find the constructive criticism getting drowned out by the “meme-ing.”
Now, I don’t think that Internet culture is going to change; the lack of accountability driven by anonymity and being many steps removed from a particular situation is one of the darker realities that affects not just personal finance, but every aspect of society today.
But the Internet and those who offer a helping hand in research can sometimes be intimidating as well: “Where do I even start?" "Who can I trust?”
Changing habits is hard
In the absence of background and context, the responsible and short answers to the questions above would be: “It depends” and “no one on the Internet, ever.”
If you’re someone who is always looking to improve their game and is able to readily absorb and apply new information and approaches with outcomes that measurably improve over time, that’s really great to hear. I wish I was more like you.
For myself and perhaps others, changing something can be hard.
At the end of the day, habits, routines, and beliefs form over time and are usually difficult to break. There are an infinite number of articles and videos out there on how to change and “be better,” but I do find that focusing on smaller, gradual adjustments and really working on them has helped me achieve more meaningful change over time.
The Japanese business philosophy of “Kaizen,” or continuous improvement, is an interesting concept to draw a parallel to here.
While it can seem like an arduous and abstract process to apply in daily life, what many don’t realize is the extent to which this approach is engrained in the culture and thinking of many people outside of work in Japan.
I’m not going to belabor the reasons behind this, but I do feel that there is an emphasis on continuous improvement being especially important for the smaller things, and when everyone focuses on them, it can result in more impactful change for the wider group over time.
Let’s be clear: I’m not saying you need to find a group to be able to work on small, gradual changes and improve your research, but rather to keep this in the back of your mind.
“Okay, so what kind of small or subtle changes can I make to improve my research then?”
What is “Data Triangulation?”
Before we dive into that, perhaps some background on triangulation and why I’ve decided to talk about it first. I learned and applied a form of triangulation back in my days as a consultant (sadly, some time ago now).
It was usually the approach of choice for any project requiring comprehensive research and analysis within a short period of time. This was typically the case whenever you were at the earlier stages of a possible financial transaction and the buy or sell side wanted some commercial due diligence done.
There’s actually a lot of literature on triangulation online. I’d encourage you to take a look and form your own opinion. The basic gist of it is to use two or more different methods, or data sources, to form a more balanced and detailed view of a situation.
This way, if an observation, conclusion, or decision needs to be made, it is based on at least some level of scrutiny.
Sounds pretty reasonable, right?
I’m not going to delve into the pros and cons of triangulation or the debate of whether this technique is more applicable to qualitative versus quantitative research or whether we’re talking about primary or secondary data sources. It’d simply take forever to research and write about, and at the end of the day, it’d just be my subjective opinion as a layperson.
The fact is, this approach was one of the more popular ones where I used to work. It wasn’t perfect (nothing is), but it was effective enough in getting the job done.
Let me state upfront that I’m not a proponent of triangulation, nor am I sound at applying it. It is just that over time, the concept has stuck with me and is something I keep in mind whenever I conduct research to form an opinion or make a decision on something.
Now let’s actually talk about it.
The form of triangulation that we used back in those days required at least three methods or data sources instead of two. I think this is an important difference.
In academia, where research tends to be more transparent, scrutinized, and time-consuming, you may be able to get away with at least two.
But when you scurry over to the private sector, where time is always of the essence and there’s never enough of it yet you still want certainty, bumping up the minimum requirement is usually one arbitrary way to make everyone feel more comfortable (except for the poor people who need to meet the deadline).
Funnily enough, I always thought that three data points seemed more of a triangulation than two, figuratively speaking.
For the three different methods, they ideally involved a “top-down” approach, a “bottom-up” approach, and an independent view from a trusted third party.
This meant that no matter the results of whatever you were trying to research or estimate, you’d arrive at them from markedly different directions. And whether the results were close together or far apart, the separate approaches would help create a more informed and defendable narrative to support decision-making.
To very briefly cover each method, the “top-down” approach was often the easiest but also least accurate, as it typically comprised anecdotal views from senior executives or industry experts via interviews.
"Bottom-up,” meanwhile, involved gathering as much primary, or raw, data as possible from internal and external sources, analyzing it, developing assumptions where gaps exist, and modeling estimates to form a view.
And finally, getting an independent perspective from a reputable third party was basically a “CYA” (Cover Your A**) move.
Together, these data points would give a range and an average of whatever was being estimated, which was usually some kind of high-level market valuation, followed by observations and recommendations.
Overall, the approach was deployed across both the qualitative and quantitative work that we did.
If the project was a scan and review of existing literature and therefore qualitative in nature, then going beyond three data points was often necessary (e.g., evaluating two to three examples that approached it one way, then another two to three examples that looked at it another way, etc.).
But if the output was more quantitative, that is, closer to what I described above, then three different data points were usually sufficient.
I would say that triangulation really helped level up my research and analytical skills to the point where I still benefit from it today.
Often under pressure and not being able to bridge the gaps in forming disparate but meaningful views, it required a lot of learning on the go and differentiated thinking to figure things out.
In Part 2, I share how just taking the concept of triangulation and applying it can still go a long way towards improving research outcomes—and how it actually helped me get started with investing.
See you in the next one!