Using Big Data Analytics to Enhance Private Equity’s ROI

Two Six Capital, based in San Francisco, has pioneered data science for private equity to find value where others do not. As of October 2018, the company has helped investors fund a combined enterprise value exceeding $27 billion. Two Six Capital’s data-powered investing platform enables a disruptive approach to PE investing. It utilizes big data and machine learning to generate revenue projections based on unit-level company growth and derives predictive analytics that measure the quality and lifetime value of customers and products. Often applied in consumer, technology, financial, industrial, and business services sectors, the platform is applicable to any company with a critical mass of customers, product SKUs, and/or retail channels.
person scrolling graphs on a tablet using stylus

"Posit open source and professional products allow Two Six Capital data scientists to slice and dice billions of rows of data on the fly, eliminate manual processes, explore unseen business drivers, and communicate findings effectively to colleagues, co-investors, and business partners in a format they can easily understand and, then, communicate to other stakeholders.”

Sajjad Jaffer
Founder, Managing Partner at Two Six Capital

The Challenge

The private equity industry is now an asset class that counts more than 7,500 active firms globally, with an ecosystem of banks and intermediaries alongside it. In such a crowded space, investors can no longer rely on an “information advantage” to acquire assets outside of a competitive auction or target industries primed to benefit from long-term trends. The new challenge is to enable a persistent and unique investor advantage based on a data-powered, “inside-out” view of a company’s operating drivers.

The Solution

Two Six Capital’s data-powered analytics platform, which includes Posit’s professional integrated development environment Posit Workbench, equips investors with differentiated insights to endorse or disprove their investment theses and inform their growth and resource allocation decisions. It was developed specifically for due diligence and value creation and engineered to ingest raw data at scale, while rapidly generating analytics under tight timeframes. The raw data from companies’ CRM, PoS, ERP systems, and other sources are ingested into the firm’s in-house ETL, which is hosted over a virtual private cloud. The data then moves to databases where it is validated, cleansed, and manipulated before machine learning and statistical-modeling applications produce insights through hard-copy deliverables or in real-time over dynamic dashboards enabled by Posit’s open-source web application framework Shiny.

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