Sol is an analyst at a London venture capital firm. After asset managers skim proposals, they send the interesting ones to him. His role is to collect data.

Sol doesn’t mind doing this. After all he is learning and one day will be an asset manager himself. But the work is time-consuming, repetitive and dull.

Data capture is a challenge for unlisted assets. Public databases are patchy, with gaps in the numbers and it’s easy to miss updates.

For a better picture of a company, Sol scrapes websites for employee comments. This powers a proprietary sentiment analysis that runs for both existing and prospective investments.

This data presentation is a gold mine but extracting it is messy.

Sol has an idea. He talks to Svetlana.

“I did some data analysis during my biotech Masters. We could use automation software for our data capture and I’d spend my time analyzing it.”

“Financial data isn’t biotech, Sol. It’ll take too long to figure out how to match one source to another. Then you’ll have to tag and align web content.

By the time you’ve figured it out we’ll have missed several opportunities. And we don’t make that many investments.”

“True, although automated analysis would allow us to launch another fund. Perhaps a biotech fund?”

“I know you’re anxious to progress Sol, but we’ve done things this way for years. Why disrupt the process?”

Sol needs help!      

Data preparation is a straightforward ask.

• Automate, clean and standardize data and infill missing items
• Visualize analysis using proprietary charts and insights
• Ensure the latest data is available in real time.

The execution requires experience.

Sol engages a managed software provider in the financial services space. There are surprisingly few with the skills and experience to handle private data.

The first ask is to hook up TechCrunch, Tracxn and Crunchbase. It is a pain figuring out if you have the correct items and if they’re up to date.

Next, run the data through the Affinity CRM. There’s no need to spend money on new software, when what’s already there allows customized reporting.

Finally, the data must be the latest available. This requires pinging databases for updates, downloading data and updating analysis.

Sol presents the results to Svetlana.

“Well, it looks like you’ve done yourself out of a job, Sol…”

“But look what we got as a bonus. The software provider chases suppliers over errors and missing data. We’ve got a complete set for European biotech companies for the first time.”

“Hmm, but we need forecasts for the industry and companies. How long is that going to take?”

“I’ve built models for both. We’ve trained AI tools to make predictions using our inhouse methods and metrics. We could do this for any industry.”

“And what is that going to cost?”

“The AI is open source and we already pay for data. The models and metrics are the way we’ve always done things.” Svetlana smiles.

“Ok, you’ve got me. And maybe you’ve got your biotech fund.

We’ll reinvest what we save on research into management, but there’s still all the running costs”

“Thank you, Svetlana! Did I mention the software provider offers back office services?”

If data headaches cause you to miss opportunities and make you fearful of launching new funds, please contact Tanisha Lakhani or Harsh Patel.