Data science is not a new discipline in business. Insurance companies have been using advanced data techniques for decades. Social media companies use data science for everything from facial recognition in user photos to micro targeting ad messaging.

Historically, using data science in PE was not considered feasible for several reasons:

  • The data necessary for complex deal analysis was not readily accessible.
  • Software to effectively process the data was not available.
  • Data scientists were not integrated into the deal process.
  • Analysis could not be done in the short timeframes demanded by the deal context.

Over the past five years, the opportunity landscape for bringing data science into the deal process has changed dramatically. Advanced capabilities such as cloud computing, open-source software, and greater access to trained professionals have made it possible to quickly analyze various massive data sets in a tightly time-bound deal context. PE firms now have the opportunity to implement sophisticated methods of analyzing data to uncover value both pre- and post deal.

What is data science?

Data science is a multidisciplinary approach that brings together scientific thinking, statistical methods, computer engineering, and innovative technologies to collect, combine, and assess significant amounts of structured and unstructured data in ways that can provide better insights and predictions to help answer business questions and drive strategic decisions.

Data science

The convergence of four factors is opening up new ways to identify new opportunities value for PE firms.


Data scientists with advanced skills in engineering, computer science, and math are now partnering with deal experts to translate business data and models into new insights that can drive decision making.

Types of data

Data from third-party sources is being integrated with corporate information to conduct richer analyses into the wider market context. Examples of external data might include historical and forecasted market data, industry benchmarks, economic outlooks, population and demographics data, aggregated location data from smartphone apps, satellite data, phone usage data, data from social media sites, and others. These new data sources allow for more advanced data models and better predictive analysis.


While traditional PE firms continue to rely on Excel-based analysis, new tools can now be used to rapidly assess massive data stores to provide deeper insights.


Artificial intelligence (AI) and machine learning programs can be used to build predictive models that learn through cycles of data continually improving accuracy. This can be done during the pre deal diligence phase to test and refine hypotheses or post close to support ongoing business decision making.

According to KPMG’s research (Exhibit 1), there are a handful of larger PE firms that have already implemented or are currently exploring the use of Big Data and cognitive computing and machine learning. Most firms are still in the “awareness raising” phase, which indicates a real opportunity to gain an advantage for the firms that act quickly. Data science can give PE firms a myriad of ways to assess potential deals, predict ROI based on shifting levers, and adjust their change activities over time.

Exhibit 1 bar graph

View the Global report or visit the KPMG US website to read the original publication.

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