As an asset manager, you thrive to differentiate yourself in today’s hyper-competitive market. Unfortunately, many asset managers are currently unable to grasp the opportunity from the abundance of data available today. Often, the obstacle is qualitative (aka. ‘unstructured’) data – specifically ingestible, granular and usable qualitative data.
Unstructured data – while being relatively easy to get hold of – is really hard to work with. Especially when it comes to scoring it to make more deeply-informed decisions. Let’s look at why that has been the case – and why it no longer needs to be.
The 3 big problems with using unstructured data
In the past, to get insights from unstructured data asset managers have generally had to pay someone (or more likely, a whole team of someones) to manually gather and analyse it. And like any manual process, it consumes heaped platefuls of time and resources and is liable to be undermined by human error.
But things are even worse than that – because even if you can afford to invest in the manual process of unstructured data analysis, that process doesn’t necessarily produce results that can be used as you need them to be.
That’s because of the nature of unstructured data, which creates three big problems.
Problem 1: Sheer volume
The first problematic thing about unstructured data is that there’s a lot of it. Actually, there’s a lot more than a lot of it. Most data (some say upwards of 90% of it) is unstructured. Research firm ITC predicts that there’ll be 175 billion terabytes of unstructured data by 2025. This means that even the most psychotically caffeinated team of experts is liable to miss some vital pieces of evidence when sifting through the towering data-pile. And there’s no way of telling what they might have missed.
Problem 2: Questionable quality
Making matters worse, qualitative data itself varies wildly in quality. Everything from an article in a reputable news outlet to a Facebook status update counts as unstructured data. All of it – from the most reputable article to the sketchiest tweet – is riddled with subjective biases, and none of it can be entirely ignored.
There’s no getting around it: you need to weigh the importance of all of these different sources and signals. Which is, given the aforementioned volume issue, fiendishly difficult. As is precisely identifying what events do and don’t critically affect market trends and investor exposure.
Problem 3: Fuzzy insights
Because of the sheer volume and inconsistency of unstructured data, manually collecting and analysing it inevitably produces incomplete, fuzzy and not entirely trustworthy datasets. These datasets are hard to derive valuable insights from. And even if you can derive insights from them, it could take days – if not weeks – to surface them. That’s a problem when effective investing demands a far more nuanced, low latency and consistently usable flow of unstructured data.
And because they’re so fuzzy, these unstructured datasets can’t be integrated and correlated with either your fundamental and quantitative datasets. This holds you back from seeing trends straight and making more incisive predictions based on that knowledge.
So far, so bad, you might think. But now here’s the good news (we told you it was coming.)
Unstructured data can now be made as accurate, timely and actionable as structured and fundamental data – and can be integrated with it. And the base metal of raw, unstructured data can at long last be transformed into asset-management-friendly insights.
Here (in brief) is how that works…
Natural Language Processing and Deep Learning collect, distill and analyse vast quantities of unstructured data at machine speed. (There goes the volume problem…)
Asset managers can easily interrogate unstructured data, uncover hidden signals and identify critical events that even the most expert humans can’t spot.
Scoring engines make it simple to sort the unstructured wheat from the unstructured chaff – and in turn to rank portfolio exposure and new opportunities. (So much for the quality problem…)
Trends, signals, and events automatically map onto entities – e.g. companies – to derive insights that can help guide decisions.
These insights are all pulled together and surfaced via a knowledge graph. With such an insights hub you can act on deep insights (not found on typical terminal products) in the timely fashion investment demands.
Finally, and by no means leastly, integrating these insights with fundamental and structured datasets powers even more accurate decisions.
(Goodbye fuzzy insights – it wasn’t nice knowing you!)
So there you have it. Unstructured data problem(s) solved.
Our bespoke software solutions leveraging modern data stacks and Artificial Intelligence could be exactly what you need to create the unique, granular and customised investment products investors are clamouring for – and establish a lead for yourself.