Sep 23–26, 2019

Managing data science in the enterprise

Mac Steele (Domino Data Lab), Nick Elprin (Domino Data Lab)
1:30pm5:00pm Tuesday, September 24, 2019
Location: 1A 10
Secondary topics:  Culture and Organization

Who is this presentation for?

Data science managers and executives overseeing data science orgs

Level

Intermediate

Description

The honeymoon era of data science is ending, and accountability is coming. Not content to wait for results that may or may not arrive, successful data science leaders must deliver measurable impact on an increasing share of an enterprise’s KPIs. Attendees will learn how leading organizations take a holistic approach to people, process, and technology to build a sustainable competitive advantage.

Coverage will include:

- How to select the right data science project: Many organizations start with the data and look for something “interesting” rather than building a deep understanding of the existing business process and then pinpointing the decision point that can be augmented or automated.

- How to organize data science within the enterprise: There are trade-offs between centralized and federated models; alternatively, you could use a hybrid approach with something like a center of excellence.

-Why rapid prototyping and design sprints aren’t just for software developers: Leading organizations put prototyping ahead of the data collection process to ensure that stakeholder feedback is captured, increasing the probability of adoption. Some organizations even create synthetic data and naive baseline models to show how the model would impact existing business processes.

-Why order-of-magnitude ROI math should be on every hiring checklist: The ability to estimate the potential business impact of a change in a statistical measure is one the best predictors of success for a data science team.

-The difference between “pure research” and “applied templates”: 80% of data scientists think they’re doing the former, but realistically, the vast majority are applying well-known templates to novel business cases. Knowing which is which and how to manage them differently improves morale and output.

-Defining a stakeholder-centric project management process: The most common failure mode is when data science delivers results that are either too late or don’t fit into how the business works today, so results gather dust. Share insights early and often.

-Building for the scale that really matters: Many organizations optimize for scale of data but ultimately are overwhelmed by the scale of the growing data science team and its business stakeholders. Team throughput grinds to a crawl as information loss compounds from the number of interactions in a single project, much less a portfolio of hundreds or thousands of projects.

-Why time to iterate is the most important metric: Many organizations consider model deployment to be a moonshot, when it really should be laps around a racetrack. Minimal obstacles (without sacrificing rigorous review and checks) to test real results is another great predictor of data science success. Facebook and Google deploy new models in minutes, whereas large financial services companies can take 18 months.

-Why delivered is not done: Many organizations have such a hard time deploying a model into production that the data scientists breathe a sigh of relief and move on to the next project. Yet this neglects the critical process of monitoring to ensure the model performs as expected and is used appropriately.

-Measure everything, including yourself: Ironically, data scientists live in the world of measurement yet rarely turn that lens on themselves. Tracking patterns in aggregate workflows helps create modular templates and guides investment in internal tooling and people to alleviate bottlenecks.

-Risk and change management aren’t just for consultants: Data science projects don’t usually fail because of the math but rather because of the humans who use the math. Establish training, provide predetermined feedback channels, and measure usage and engagement to ensure success.

Prerequisite knowledge

A basic understanding of data science

Materials or downloads needed in advance

N/A

What you'll learn

• Learn how to design and run data science organizations to have a sustained, scalable, predictable impact on business outcomes • Understand why data science may have more to learn from product management than software engineering
Photo of Mac Steele

Mac Steele

Domino Data Lab

Mac Steele is the director of product at Domino Data Lab, where he leads strategic development of the company’s data science platform. Based in San Francisco, he works closely with leading financial services, insurance, and technology companies to build a mature data science process across their entire organization. He has extensive experience leading advanced analytical organizations in both finance and tech. Previously, Mac worked in the Research Group at Bridgewater Associates, the world’s largest hedge fund, where he developed quantitative models for the firm’s emerging market portfolio; he also built the core data capability at leading fintech company Funding Circle. Steele holds a degree (summa cum laude) from the Woodrow Wilson School of Public and International Affairs at Princeton University.

Photo of Nick Elprin

Nick Elprin

Domino Data Lab

Nick Elprin is the cofounder and CEO of Domino Data Lab, a data science platform that accelerates the development and deployment of models while enabling best practices like collaboration and reproducibility. Previously, Nick built tools for quantitative researchers at Bridgewater, one of the world’s largest hedge funds. He has over a decade of experience working with data scientists at advanced enterprises. Nick holds a BA and MS in computer science from Harvard.

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