A HYBRID APPROACH TO DATA SCIENCE PROJECT

MANAGEMENTElaine Lee

O P E ND A T AS C I E N C EC O N F E R E N C E_

BOSTON 2015

@opendatasci

Building a Data-Driven WorldTM

Open Data Science Conference

A Hybrid Approach to Data Science Project ManagementElaine Leeelee@civisanalytics.com@elaineklee

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Organizations want to be data-driven but many obstacles stand in their way:• Communication not trickling up to executives and key decision makers• Silos between departments, making it difficult to share and

collaborate on analysis• Data ingestion (ETL or Extract-Transform-Load) is difficult and time-

consuming• Lack of meaningful, yet customizable visual reporting• Inability to flexibly scale up or down technological needs at a

reasonable cost• Inadequate or overwhelming learning resources about data science

A Common Problem With Many Faces

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Where should Enroll America direct its insurance signup efforts?

Mapping the Uninsured in America

5Civis Analytics | Proprietary and Confidential

As a company, Civis traces its origins to the 2012 Obama for America analytics team.

We built a scientific understanding of each voter. Our data science influenced every strategy and tactic: voter targeting, messaging, media buys, and fundraising.

This meant the campaign could allocate resources where impact would be greatest.

We ran the first individualized presidential campaign

Civis Analytics | Proprietary and Confidential Open Data Science Conference#ODSC

6Civis Analytics | Proprietary and Confidential

Today, we leverage data science to help our clients in politics, non-profits, and the corporate world.

Civis Analytics | Proprietary and Confidential Open Data Science Conference#ODSC

Open Data Science Conference#ODSC Open Data Science Conference#ODSC

An easy-to-use,end-to-end, incredibly extendable, data science platform in the cloud for teams who want to make great data-driven decisions to drive their organizations forward.

Introducing Civis

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The Civis Approach

ProductConsulting R&D

Applied Data Science• Tackles the toughest data

science problems we can find

Data Science R&D• Generalizes and

automates the solution for many scenarios

Software Engineering• Integrates solutions into

user-empowering software

• Highly collaborative departments• All departments contribute to both our services arm and product

development

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The Civis Approach

Our unique team structure allows us to solve your biggest problems

with custom solutions and the technology to scale them.

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Strategies and philosophies• Teams based on Civis’s product and consulting needs:

• “Built around code”• Semi-annual departmental day-long off-sites to plan upcoming R&D

initiatives• Academia-influenced: evidence-based approaches to finding and

reporting best solutions• Software development-influenced: standups, code review• Favorite tools:

Data Science R&D

R&DModeling

Methodology Unstructured Data Engineering

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Tools

• Share and discuss data science news

• Receive feedback from colleagues using our tools

• Discuss implementation• Lower communication costs

compared to email

Data Science R&D

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Tools

• Prototype new workflows• Used like a log book to record

and present results• Share preliminary results with

members of other departments

Data Science R&D

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Tools

• Department heads set milestones, check progress, and make project staffing decisions

• Collaboratively plan development on new functionality or organizational processes (e.g. recruiting)

Data Science R&D

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Tools

Strategies• Designate “tag team” on R&D as

default R&D resources for client engagements• This is the Modeling Methodology

team• Other R&D teams’ members may

be staffed on engagements depending on expertise required

• R&D team member always serves as the Consulted in the RACI model

• Transparency about challenges is paramount

R&D <-> ADS

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1. Assemble a project team of R&D data scientists and Applied Data Scientists

2. Work with Enroll America to refine requirements and come up with a plan of analysis, ultimately resulting in the design and execution of a phone survey on a sample of individuals, followed by building a predictive model for the rest of the country.

3. The Applied Data Science Manager has weekly calls with Enroll America and status meetings with the project team.

4. The project team delivers the predictions and analysis to Enroll America.

R&D <-> ADS: A Case Study

Mapping the Uninsured in America

The project team completes a postmortem and determines these activities could be automated: model building

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Tools

Strategies• Designate teams at the interface

to triage issues and plan new development:• R&D: “Engineering” team• Tech: “Modeling” team

• Use module or project-specific chatrooms to get answers to ad-hoc questions quickly

• Identify opportunities to form cross-functional teams, e.g.:

• Developing apps using the Platform’s API

• Knowledge sharing on best practices

R&D <-> Tech

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1. After the postmortem for the Enroll America engagement, R&D begins prototyping automated modeling functionality and discussing its implementation with the Tech department.

2. R&D’s Engineering team finishes the prototype and works with Tech’s Modeling team to integrate it as a new feature in the Platform.

3. During integration, ad hoc discussions occur on GitHub and Hipchat to address usability questions, e.g. resource usage and input/output specifications.

R&D <-> Tech: A Case Study

Mapping the Uninsured in America

The integration team successfully builds and integrates the Build Model module in the Platform.

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Our approach to data science consulting and product development is enriched by valuable perspectives of our

employees, who come from a wide array of backgrounds, making our project management

strategies a hybrid of more conventional techniques.

Conclusion