Morgan Stanley: Building an Intelligent Organization in the Age of AI

Jeff McMillan, Chief Analytics and Data Officer at Morgan Stanley, shares the journey he took upon joining Morgan Stanley and leveraging analytics, data, and AI to drive growth and efficiency across the wealth management business. Gain insight into the five elements of an intelligent organization (including their pillars and pitfalls), the steps he took to transform Morgan Stanley into a smarter organization, and key lessons he learned on his journey.
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FP&A at Standard Chartered Bank: Building Collective Intelligence
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On average, two people armed with the Digital MI team's applications in Dataiku are doing the work of about 70 people limited to spreadsheets. That means increased analyst productivity by a factor of 30 by replacing spreadsheet-based processes with governed self-service analytics.

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U.S. Venture + Dataiku: Upskilling Analysts to Save Thousands of Hours

The Data and Analytics team at U.S. Venture was built to usher the company into the future of data science and AI. See how they use Dataiku to streamline their data efforts, implement a culture of reuse, and drastically save time (i.e., a warehouse optimization solution that saves data scientists and analysts over 95% of time).

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Bankers’ Bank + Dataiku: Goodbye Data Silos, Hello Analytics Efficiency

Organizations worldwide consistently cite data quality and speed-to-insights as their biggest challenges. Bankers’ Bank leverages Dataiku to increase efficiency and ensure data quality across an array of financial analytics, ultimately reducing the time to prepare analyses and deploy insights by 87%.

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Data Transformation at Rabobank: Execution & Innovation

In the past year and a half, Rabobank has completed more than 100 AI projects and has reduced the time to onboard data team members — in particular data scientists — from months to weeks.

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Cyber Risk Analytics: The Next Frontier | Envelop Risk

See how Envelop Risk took a holistic approach to characterising the cyber risk economy, deploying dozens of machine learning models to predict behaviour, incentives, and diffusion, in order to build the next generation of insurance products.

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