Daloopa Case Study
Preqin is a global provider of data, solutions and insights to more than 110k alternative assets professionals. LPs, GPs, investment consultants and other market participants, including law firms and academic institutions, rely on Preqin’s exclusive private market and hedge fund data to help them make well-informed decisions.
In order to collect and standardize data on private market transactions, Preqin needed to review up to 800k documents to extract a decade of key metrics on more than 60k companies. Over half the companies reported in a language otherthan English. This created and enormous data extraction and validation challenge. The key metrics that Preqin captured needed to be standardized in order to provide clients with comparable information. However, those data pointswithin a document appeared in many formats, including in tables, HTML, charts and text.
- Populating data would have taking 60 professionals
- Would have taken a year to initiate the database
Preqin partnered with Daloopa to extract and standardize data using Daloopa's scalable, AI-first approach. Daloopa is a New York-based tech start-up founded in 2018 that leverages its proprietary technology to provide language-, format- and industry-agnostic data extraction solutions. After a review of competitor solutions, Preqin selected Daloopa because of its unique ability to perform extraction at scale, in a time series, while providing 99.8% accuracy. Daloopa also enabled a robust audit trail for each document so Preqin could validate the exact location a particular data point was cited. Preqin found that incorporating Daloopa’s technology into its data extraction workflow resulted in a 4x lower cost compared with a manual extraction process,while providing a higher accuracy rate due to a reduction in human error.
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