For investment firms, knowing which companies to invest in is key to their success. But identifying good investment opportunities, and keeping investment data up-to-date, is a challenge. It’s common practice for firms to employ teams of people to manually integrate data from multiple sources and ensure that the data is accurate and up-to-date. But because these processes are human-driven, they are often inefficient and fraught with error.
How Data Products Improve Investment Data
The good news is that there is a better way. Leading investment firms are investing (no pun intended!) in data products as a way to ensure their data is complete, trustworthy, and continuously-updated. Here’s how they work.
Data products are a consumption-ready set of high-quality, trustworthy, and accessible data that people across a firm can use to solve business challenges. Specifically, data products help solve the challenge of identifying the right companies for investment quickly and efficiently.
In this case, those challenges include the ability to identify which companies to invest in. Organized by business entities and governed by domain, data products are the best version of data. They are comprehensive, clean, curated, continuously-updated data sets, aligned to key business entities, that both humans and machines can consume broadly and securely across the firm.
Data products include a number of key capabilities, and they all embody four key principles:
Discoverable and interoperable using intuitive user interfaces curated with feedback from end users and and machine interfaces that support real-time operations
Comprehensive and consistent by aligning to a domain-specific, universal schema and enriching data with reference data sources
Clean, accurate, and reliable with validation and cleaning based on global standards and version control for monitoring and provenance
Curated and continuously-updated using pre-built, machine learning-based matching models
Using data products, investment firms can gain access to higher-quality data while reducing human effort.
Data Products in Practice: Market Data Linkage
In order to have the best, most up-to-date information available, investment firms are constantly combining data from multiple, external sources such as Pitchbook, Capital IQ, Bloomberg, and others. But it’s difficult to monitor changes across all the companies you’re watching. For example, companies change ownership all the time, and tracking those ownership changes manually is not sustainable.
Enter data products. Using data products, investment firms can consolidate and integrate data from multiple, internal and external sources using a data product platform like Tamr Mastering. By assigning a unique ID (called Tamr ID), firms can have a single record for each entity that they continuously update using machine learning mastering models and enrichment from third party sources. As a result, they have clean, standardized data that users across the firm can consume and use visualizations, dashboards, and analytics.
Investment firms that use data products for market data linkage realize benefits such as:
Improved visibility into deal sourcing activities and portfolio performance
Modeling across data sources for correlations and benchmarking
Sector-level mapping of deal flows to improve target identification
Simplify joins across market data so analytics remain up-to-date
Developing the right relationships with the right intermediaries at the right time
Provide context to the business data most relevant to investment criteria
Data Products In-Depth: Market Data Linkage at Global Investment Firm
A leading investment firm that builds and invests in internet, software, and technology-enabled companies realized they needed better insights into market and company trends in order to outperform their competitors and, ultimately, attract more capital from outside investors. Their first step was to assemble a data products team to develop the infrastructure and processes to deliver on their objectives. The newly-formed team onboarded multiple next-generation applications, such as Snowflake, Fivetran, and dbt, but quickly realized they needed a scalable solution for data mastering to help them connect a growing list of alternative data sources.
The firm selected Tamr’s Market Data Linkage data product template because it enabled the firm to gain complete, accurate, and continuously-updated views of portfolio companies and potential investments with minimal effort from their scarce data engineering resources. The built-in data quality services and entity resolution models, built using Tamr’s patented machine learning-based technology, gave the team a high degree of confidence that the data product can scale as they acquire new, alternative data sources. And, it also gave the firm’s partners confidence in the insights they use to invest.
Highly-scalable and SaaS-based, Tamr Market Date Linkage automated 99%+ of continuous entity linkage activity and provided reusability across new data sources and data products, enabling the firm to find better deals and attract more external capital.
Tamr’s Market Data Linkage data product template helped many other investment firms accurately clean, curate and match diverse datasets quickly with minimal effort. As a result, these firms are able to better identify investment opportunities, improve and enrich benchmark portfolios, and power downstream dashboards for investment teams.