Adages about lies, damned or otherwise, and their relationship to statistics are often used to imply a disingenuousness on the part of the accused. But as anyone in a data-heavy industry (and these days, that is most people) will tell you, it is never as simple as counting a complete and consistent dataset.
As the rise of ‘big data’ shows, datasets are rarely easily found, marshalled or analyzed. It can take complex software, some lateral thinking and a lot of manpower to compile datasets that are comprehensive, consistent and comparable.1
Moreover, it is becoming harder rather than easier to do so. Privacy laws are making it more burdensome to collect personal data, even as people’s online lives become more intricate than ever before. In the financial world, the same is true: an increasing number of corporate entities are shielded from regulatory or reporting obligations, even as their corporate structures are more complicated than ever before. The relative reporting burdens on different countries, industries and entities can make any financial dataset into a patchwork quilt, making it hard to draw concrete conclusions.
A prime example is the alternative assets industry; a key financial sector, it now holds almost $9tn in assets under management.2 Yet very few industry participants have mandatory reporting requirements. This is despite the industry having a tangible impact on the wider financial ecosystem: alternative assets funds are of a size to compete with corporate and strategic investors for acquisitions; venture capital funds have helped foster a booming global tech ecosystem; real estate and infrastructure funds build, run and maintain iconic and vital buildings, bridges and tunnels.
Pension funds, insurance companies and government agencies all invest in alternative assets, and so their success or failure can impact even the personal finances of individuals. The high stakes involved create a need for reliable, accurate and ethical data. Investors in alternative assets need to be able to make decisions with access to the best available information. Fund managers must be able to place their activities in the fullest possible context. Regulators, commentators and analysts must be given an accurate assessment of the industry in order to effectively manage or analyze the industry.
At the same time, though, the nature of the industry makes the challenge of collecting and organizing such data significantly more difficult than in almost any other financial sector. It can be hard to know how to trust any data provider when they tell you they know the “truth.” How can they claim to have all the information? Where have they gathered it from? And how are they distilling these disparate data points into a semblance of reality? What assumptions have gone into their models?
In this series of blogs, we discuss some of the issues that face companies seeking to gather financial data, and how their processes and assumptions can affect the intelligence on which financial decisions are made.
1There is a large body of academic study into the challenges of processing and studying large and complex datasets. See particularly Chen, Zhang 2014 A Survey on Big Data, and Boyd, Crawford 2011 Critical Questions for Big Data.
2According to the 2018 Preqin Alternative Assets Performance Monitor, the industry holds assets under management of $8.81tn as at the end of March 2018.
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