The solution is to find data sources that are fundamentally incentivized to provide accurate data. 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. Far from novel, these are just several examples of data being difficult to trust. Recently, payment for order flow was popularized, selling people’s trades to big institutions and allowing them to take profit away from investors. In the wake of the LIBOR scandal, cracks in the financial system were exposed and the underbelly of market manipulation was revealed. One of the largest crypto data providers revealed that 65%-95% of all their data was inaccurate and untrustworthy. 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. Combined with visualizations, it can form a complete picture for faster understanding and superior intuitive answers. Data in isolation is hard to understand intuitively and even worse to try to act upon. 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. 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. Financial data is rife with jargon and acronyms that require a dictionary to read, let alone understand. Likewise, it’s almost impossible to rationalize numbers without context for what they mean. 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.
Data explanation and visualization is a crucial component of understanding complex data points.įinancial 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.
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