Combining third-party market data faster helps a venture capital firm inject data throughout the investment process

Challenges

A leading venture capital firm realized they needed a faster way to link third-party data to keep pace with investment decisions.

Outcomes

The data product team increased the number of due diligence projects it completes in a week by 5x to further inject data into the investment process.

To make more informed investment decisions, the data product team at a leading venture capital firm was tasked with finding a faster, more efficient way to combine third-party sources and add a unique company identifier. The firm’s initial approach – manually linking data from sources like PitchBook and S&P Capital IQ and using a spreadsheet to track company information – proved too time consuming.

Lacking the headcount to manage infrastructure and tired of mapping data sources in a spreadsheet, utilizing a cloud-based data mastering solution that leveraged machine learning made the most sense. They found the best fit in Tamr’s market data linkage data product template.

Getting trusted data faster has allowed the data product team to provide data for more due diligence projects, enabling the firm to make more data-driven investment decisions.

Creating accurate data sets at the pace of business

Like many financial services firms, a venture capital firm focused on technology companies purchases third-party data sets to aid with due diligence when reviewing possible investments. The challenge that the data product team faced was efficiently linking this data together to keep pace with the firm’s increasing velocity of deals. This newly formed team first handled this task by manually combining the third-party data and tracking data sources in a spreadsheet. After the firm saw value in this market data, the team’s next objectives were to scale the process and make it faster.

“Getting answers to questions quickly was a challenge. We needed speed. There were a lot of steps to get data from three to five different sources, clean it up, and make it look like a unified data set," the data product manager said.

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“In a high velocity environment like venture capital,

having a high degree of confidence in the data that we’re providing the firm is critical. Investors want to know they’re getting insight that’s accurate since it informs big decisions, such as whether to make an investment.”

– Data product manager at a leading venture capital firm

Also important was getting better data without increasing headcount. The team was lean so hiring data scientists or engineers to set up and maintain infrastructure wasn’t an option. The head of data suggested Tamr, having used it at his previous employer, a major investment firm. Tamr combines machine learning with human feedback to automate the heavy lifting around cleaning, curating, and enriching data while letting people review the results for quality to develop trust in the data. And as a SaaS solution, Tamr doesn’t require extensive infrastructure.

Master company data from disparate sources to power insight-driven investment strategies

Map entities across disparate sources of market data efficiently and accurately. Create a common identifier (“TamrID”) that can be used to simplify joins across market data sources and keep your analytics up-to-date.

“We wanted a solution that would allow us to match different data sources with ML in a way that gave us a high level of confidence in the results and provide the ability to correct results that didn’t match correctly. We’re also a small team and cloud architectures have matured in recent years to make it easier to build these capabilities without hiring a lot of people. Plus, we had a proof point that Tamr was reliable and that other top-tier firms were using it. And we would benefit from the years of experience Tamr has with dealing with these types of data problems,” the data product manager said.

Main challenges:

Quickly combining third-party data sets like Bloomberg, Refinitiv, CB Insights, and FactSet to match the velocity of deals
Gaining complete, accurate, and continuously-updated views of portfolio companies without expanding engineering resources
Connecting a growing list of alternative data sources with a cloud-based, scalable master data management solution

Tamr’s data product template enables organizations to gain complete, accurate, and continuously-updated views of portfolio companies and potential investments with minimal effort. The built-in data quality services and entity resolution models, built using Tamr’s patented machine learning-based technology, easily scales as organizations acquire new, third-party data sources.

Injecting data into the investment process

The data product team now has a repeatable, standardized way to carry out due diligence projects and has set up a self-service data system. Anyone in the firm can search for and access data on 400,000 companies and get a better understanding of what’s going on with the business. The velocity at which data is being injected into the firm has increased substantially. The team now completes multiple due diligence projects each week, compared to around 10 in a quarter when they first began using Tamr.

“The team gets a lot of compliments on how much work we're able to produce and how responsive we are. I think that's an indicator that this initiative is working,” the data product manager said.

With accurate market data, the firm can more easily identify other third-party data sets that it may want to add for greater insight. And having a standardized process for integrating market data makes adding new data to an existing dataset much easier, enhancing the firm’s data on a company.

Tamr Results:

Increase the number of due diligence projects the data product team completes in a week by 5x
Provide investors and deal teams with more accurate data faster for more informed investment decisions
Land clean data directly in the Snowflake Data Cloud to reduce time spent moving data
Generate trusted market data in fast, scalable way to match the firm’s velocity of deals

“Our ability to meet the demands of the firm has significantly increased. Being better informed increases the likelihood that we’ll make the right decisions. Increasing our due diligence projects by, say, a factor of five, over the course of the first year with Tamr and maybe accelerate beyond that to touch every diligence project in a year is really exciting,” the data product manager said. “One new investment pays back the investment in Tamr multiple times over.”

The firm stores its data in the Snowflake Data Cloud. Being able to directly land clean market data into Snowflake from Tamr using a connector allows the data team to spend less time on moving data and more time on using that data to help the business.

“Landing data directly in Snowflake allows us to maintain and iterate on what we've built with Tamr in a much more seamless way. We’re more focused on the sophisticated modeling side of things and how to manage and deploy that and moving data from one place to another,” the data product manager said.

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