In the past 12 months, Two Six has analyzed over $20 billion in individual transaction data. As Picache explains it, their data science approach has already become embedded in decision-making in private equity; it’s a big data play that the private equity types “get.”

Big data will be increasingly important for asset managers, but many organizations are unsure how to proceed. This three-step process can help.

Negative news is one reason why people consistently underestimate the progress humanity is making, complains Steven Pinker. To discern the true state of the world, he says, we should use numbers. In “Enlightenment Now”, he does just that. The result is magnificent, uplifting and makes you want to rush to your laptop and close your Twitter account.

Investments are continuing to flow into funds that use Artificial Intelligence (AI) to make trading decisions, but in the past few months we’ve seen just how important it is to still have human involvement and good fundamental reasoning behind these strategies.

The real power is not in pure quant investing, but in a combination of quantitative and fundamental, stock-specific research—a ‘quantamental’ approach.

Most people do not understand that AI, especially the AI used in finance today, lacks the application of deep subject matter expertise to create the clean data and relationships that are the foundation of any successful investment strategy or AI. Winning games is one thing, but the real world is not a game that follows immutable rules in a strictly defined space. In the real world, humans change the rules, break the rules, or the rules don’t even exist. Current AI is nowhere near navigating real world situations without a great deal of human intervention.

Finance is perhaps AI’s most daunting challenge. Training a computer to correctly identify a Labrador is different from getting it to suss out a bond market. Markets move in mysterious ways, influenced by news events, economics, politics, regulation, and human judgment. “In the financial world,” says Gary Collier, Man AHL’s co-chief technology officer, “the ground is always shifting.”

If IBM can't use machine learning to match disease symptoms and genetics with treatments after spending over 6 years and $15 billion, how will companies use it to pick investments that match future supply with future demand, where there is an extra degree of difficulty in the reflexivity of social science?

Amazon uses "a ton of metrics" to measure success, explained Bezos. "I've noticed when the anecdotes and the metrics disagree, the anecdotes are usually right," he noted. "That's why it's so important to check that data with your intuition and instincts, and you need to teach that to executives and junior executives."

Along with acquiring new datasets that contain valuable signals, firms need to create a disciplined process that quantifies mental models and qualitative research. In other words, they have to find a mathematical model for the “gut feeling.” That way managers can reference and iterate on successful work, using technology to provide easy scalability.