Dataiku: data science, ML and AI platforms critical for laggard asset management firms

The technology disruption that has transpired over the last 15 years as a result of data-driven digital transformation across retail banking, insurance, brokerage, and payment systems institutions has been relatively slow to pervade the asset management space, especially buy-side entities.

Against that backdrop, large buy-side firms and systematic hedge funds have been pioneers in integrating quantitative and data science techniques into the traditional, fundamentals-driven investment process. They are, as a result, ahead of the technology adoption curve relative to their smaller peers. In fact, the asset management industry as a whole lags behind most others in data, analytics, and digitization, even though investment data is a foundation of its business model and customer offerings.

Asset management ranked 24th among 34 industries in digitization, after both other financial institutions (such as retail banks) as well as separate industries such as transportation, pharmaceuticals, telecom, and media, according to a report by BCG and Morgan Stanley from mid-2018.

Dataiku’s white paper covers:

  • Why investing in data science capabilities is necessary for today’s asset management firms
  • Overarching trends in the asset management landscape that reinforce this need
  • A detailed case study illustrating how buy-side firms can jumpstart their AI efforts and make more informed investment decisions
  • Buy-side-specific data science platform evaluation criteria

In the case study, Dataiku discusses the criteria employed by a collection of boutique asset management firms to evaluate commercial data science platforms and enable Enterprise AI focusing on its own platform. They note that the bulk of the IT operating expense was allocated to procuring data, not commercial data science platforms.

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