How many tennis balls could you fit in all of the skyscrapers in New York City? How many gummy bears could you fit in an airplane? How long would it take to fill the Mariana Trench with peanut butter?
Like most people, you’d probably need some time and maybe a whiteboard to guess these answers. Not only are the questions difficult to imagine and rationalize, but also there’s a lot of relevant background information needed to be able to make an accurate guess.
Without collecting all relevant data, any answer is a guess.
Imagine watching any sports game where you could only see one team and no scoreboard. You would be able to guess how each team is doing from positioning and reactions, but it would be difficult to say confidently which team is winning or who is more likely to win the entire game. It would be even more difficult to win bets against someone else watching normally on TV being spoon-fed a plethora of statistics and hearing the opinions of professional commentators.
Despite the unfairness of this competition, this is how many people invest in stocks. Dark Pools can consist of more than half of the overall trade volume for a stock, and yet despite this huge imbalance, many people don’t know they exist let alone monitor their activities. Gaining access and utilizing all the information available before making critical decisions can level the playing field and facilitate educated decisions.
Data can help us make sense of big numbers and simplify complex ideas. Pluto is roughly 3 billion miles away, so how long would it take to walk there? About a billion hours, which is longer than watching all the content on all major streaming sites back to back 20,000 times. While the number and the analogy mean the same thing, one is substantially easier to understand intuitively than the other.
Data explanation and visualization is a crucial component of understanding complex data points.
Financial data is infamously one of the largest data sets in the world and endlessly complicated, so seeing it in easy-to-process graphs instead of raw metrics helps elucidate. It’s difficult to visualize two numbers of orders of magnitude apart, but it’s easy to see how big one circle is against another.
Likewise, it’s almost impossible to rationalize numbers without context for what they mean. Financial data is rife with jargon and acronyms that require a dictionary to read, let alone understand. Being able to process this information is knowing not just what the words mean, but also how it affects a company and how it compares to other similar companies.
One of the best ways to use data is not as a standalone item in a complex sheet but as a living, breathing, dynamic guide for making better decisions. Data in isolation is hard to understand intuitively and even worse to try to act upon. Combined with visualizations, it can form a complete picture for faster understanding and superior intuitive answers.
Not all data is created equal.
This paradigm is far from original, with many of the most prominent data sources using citations and best practices to try to eliminate the uncertainty. One of the largest crypto data providers revealed that 65%-95% of all their data was inaccurate and untrustworthy. In the wake of the LIBOR scandal, cracks in the financial system were exposed and the underbelly of market manipulation was revealed. Recently, payment for order flow was popularized, selling people’s trades to big institutions and allowing them to take profit away from investors. Far from novel, these are just several examples of data being difficult to trust.
The commonality from all three of these is a fundamental agency problem; each of these groups stood to gain financially from manipulating their data or failing to correct incorrect data. The solution is to find data sources that are fundamentally incentivized to provide accurate data. Most brokers provide access to financial data, but often there is a conflict of interest, such as a broker selling their own stock, or fees on trading either explicit or invisible through slippage.
For this reason, it’s essential to carefully evaluate the trustworthiness of data sources and not take information at face value, especially when critical decisions are being made from it. Healthy skepticism and asking if there is a conflict of interest can expose early on whether a data set is purely analytical or might be inaccurate and skewed.
Data is one of the most valuable resources in the world.
Almost half of the top seven biggest companies in the world use data as their primary product for good reason. Making informed, objective decisions can eliminate uncertainty on correct choices and often guarantee the best possible outcomes. Learning to make these decisions off of comprehensive, well understood and trustworthy data can drastically increase the effectiveness of any decision and bring order to an otherwise chaotic world.
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