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The qualitative research job from hell

Qualitative Data Research is Hard (But it Doesn’t Have to Be)

You look up from your desk. It’s 11:30pm. Almost Saturday. Coffee o’clock.

Not an ideal time to caffeinate, really. But you’ve got no choice.

Because it’s looking like another literal all nighter for you and the rest of the firm’s analysts. You’ve been through this trauma so many times before you even have a name for it…

“The Qualitative Crunch”

Every so often (with an unfortunate emphasis on “so often”), your team will be called upon to do a whole heap of qualitative research – with far too little time to do it in.

And it’s that time again: your firm’s putting together a portfolio for a big investor that it wants to land – and this investor isn’t interested in being offered a standard-issue basket of options.

They want something much more tailored and targeted: a portfolio that fits with their highly complex and specific criteria for ESG, plus a variety of other themes and topics.

Your firm needs to find the best possible group of investable companies that meet this exacting criteria – which means crunching a veritable Himalayan mountain range of quantitative and qualitative data.

Oh, and the basket’s due Monday morning. (There goes your Friday night.)

It’s a tough job (and you’ve got to do it)

Your firm’s already crunched and filed the numbers. But numbers alone won’t suffice to get a read on how risk-free and opportunity-rich the candidate companies are.

So now you’re working through a giant spreadsheet of candidate companies and panning the Internet for qualitative gold about them.

There’s certainly no shortage of sources out there. Quite the opposite. There’s:

the companies’ websites, social media accounts, annual and quarterly reports
the LinkedIn and Twitter accounts of each company’s executive team members
the websites, social media accounts and reports of the companies’ competitors
news reports, market reports, blogs, vlogs…

So, yes: this is going to take all night. Definitely time for that coffee.

You rise from your desk, tear your eyes away from the monitor and totter, zombie-like, in the direction of the coffee machine.

As you lurch down the corridor, you mutter bitterly to yourself: ‘Be an investment analyst, they said. It’ll be fun, they said.’

And here’s the thing: it usually is fun.

The highs and lows of qualitative research

Qualitative research is your passion. Not only is it fascinating, it’s also super valuable for investors.

After all, as impressively solid-seeming as numbers are, you never really get the full picture from them. Unstructured data gives investors a bigger and more granular picture – particularly when it comes to those globe and market spanning themes they’re so interested in nowadays.

But as much as you’re excited by the insights it can give you, working with unstructured data can also be acutely challenging – particularly when you’re against the clock like this.

For starters, there’s a lot of it out there. (Have you seen the internet lately?). This means that the really valuable stuff isn’t as readily available as quantitative data. You have to dig for it.

Take the Metaverse. Everybody’s talking about it. But not every company is loudly pronouncing their involvement in it. The signal-to-noise ratio in this space makes it tricky to identify who the real leaders are.

Plus, there’s also a value chain of suppliers and enablers to think about.

Metaverse applications are reliant on enabling technologies which are themselves dependent on advances in underlying infrastructure. Working out the true exposure of company X to the Metaverse depends on finding out the exposure of their enablers and suppliers (and their enablers’ enablers and suppliers’ suppliers, ideally).

With enough time and resources, you can go really deep. And ideally, you would – because clients are looking for deep insights. (Not the obvious stuff that everyone read about in the FT this morning.)

But time is not on your side. You glance at your wristwatch as the coffee percolates in your stomach and see that it’s already ten to midnight.

Your desk is calling. (You wish it would shut up.)

Qualitative data problems, continued

You’re doing your best here, but you’re only human.

You know that whatever data you find, you’re bound to have missed something out – something potentially important. Plus, many of the sources you find are historical (last year’s Annual Report) and outdated – or (in the case of news and market reports) could be outdated by Monday morning.

You’ve got to weigh whatever it is you can find in this window for importance – which, of course, you’re an expert at doing. But even so, a lot of what you’re evaluating is opinion – and your own opinion about those opinions is potentially muddying the waters.

Then there’s the integration issue. Ideally, you’d be able to cross-reference and connect the qualitative data you’re putting together with your firm’s quantitative and fundamental data analysis. But that’s another problem – the models won’t mesh.

With the constrictions you’re working under, there’s no way you’ll be able to put together qualitative data that’s comprehensive and trustworthy enough to feed into your quant and fundamental data models.

Which further compromises your qualitative analysis – and deprives it of some much-needed enrichment.

All of this means you’re not even sure if this weekend-ruining research will be useful.

Does it have to be this way?

If you had more time you know you could get this research done more thoroughly. And with more resources, you know you could do it more quickly (without sacrificing sleep).

And you might even have time to use your expertise on the already-collected and weighed unstructured data to get more insights out of it and do better things with it.

Snap out of it, you sternly tell yourself. You must be dreaming.

This is an unfortunate but also inevitable part of your job. Just one of those things you have to live with… Right?


There is a better way – starting with Affinity

Our Affinity platform makes it easy for you to collect, connect and score vast volumes of qualitative data and integrate it without concern into your qualitative and fundamental data models.

This takes the pressure off you to find those insights and lets you concentrate on using the qualitative insights Affinity brings to the surface. (Plus, you’ll get your weekends back.)

Not only will you save the time and resources needed to conduct manual qualitative analysis, you’ll also be able to scale up your qualitative analysis – and offer investors even more customised, targeted, thematic, efficient and performant products.

Head to our platform page to discover more about your ticket out of qualitative research hell (and into thematic investing heaven).

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