Hello.

My name is Laurie Voss. I do great things with data and I'm looking for my next role. This deck is about why you might want to talk to me.

If you're reading this it's probably because I sent you the link, so you already know who I am. If not, you can read my resume.

What is this?

Defining the problem and the solution.

Data management challenges
Business case for investing in data
How I can help

Companies are drowning in data

The problem has evolved:

15 years ago: how do we store all this data
10 years ago: how do we process it all
5 years ago: how do we query and visualize
Still the problem: what does this mean we should do?

Part 1: Defining the problem

Stages of data management

Collect the data
Query the data
Display the data
Choose the right questions
Turn the answers into strategy

1: Collect the data

Define fields, formats, granularity
Instrument your code
Implement/buy data warehouse
Address compliance and privacy
Monitor data flows and quality

Labor-intensive but uncomplicated. Usually already solved.

2: Query the data

Clean, normalize, enrich data
Correlate events across systems
Aggregate or down-sample data sets
Implement access controls
Enable ad-hoc exploration

Usually partially solved, often some systems or data excluded.

3: Display the data

Define product-specific metrics
Identify internal consumers of data
Provide dashboards
Automate frequent reports
Feed into third-party tools

Often poorly solved. Labor-intensive reporting, long lead times, misleading approximations, long wish lists.

4: Choose the

right questions

Be aware of existing and potential sources of internal and external data
Selectively explore potential patterns, seek out "unknown unknowns"
Improve data collection in response to directions of inquiry
Prioritize questions based on internal needs and company strategy

Frequently reactive, "nobody's job".

5: Turn answers

into strategy

Internalize company strategy
Pro-actively find emerging patterns
Provide tactical help to all departments
Identify new opportunities, challenges
Inform strategic decisions with data
A narrative, not a dashboard
A recommendation, not a report

The promise of data, seldom achieved.

Why invest

in a data team?

Part 2: business case for data management

Output from a full-time data team feeds into every part of a company:

Executive
Finance
Product & Design
Sales & Marketing
Support
Engineering

Executives

Value of data for

Locate new markets
Understand customer base
Inform pricing decisions
Support fund raising
Identify upcoming challenges

Finance

Value of data for

Accurate data
Real-time dashboards
Improved forecasting
Automated reporting

Product & Design

Value of data for

Find UX pain points
Run A/B tests
Identify new use cases
Design for real users, not hypothetical personas
Suggest workflow optimizations

Sales & Marketing

Value of data for

Identify and segment customers
Inform messaging and positioning
Find up-sell and opportunities
Minimize churn
Cohort analysis

Support

Value of data for

Provide context and customer history
Inform documentation priorities
Quantitatively prioritize bug fixes
Identify "problem" customers

Engineering

Value of data for

Go beyond reactive monitoring
Forecast scaling needs
Predict "hot spots" before failure
Identify silent failures

A data team

is not a cost center

Ad-hoc, inaccurate, or partial reporting can be expensive. A data team pays for itself.

My skill set

Part 3: Where I come in

A lifetime* of working at the intersection of technology and strategy has given me the experience to sift through data and distill insights that can inform your actions and improve your company.

I take piles of data and transform them into persuasive arguments. (You're reading one right now.)

* You can read my resume

San Franciso

Street History

Examples

A simple web app that constructs a narrative history of San Francisco by zipping you around on a Google Map tour of the city. I got over 136,000 people to read a 50-slide deck about urban history by making it fun. I also wrote the app itself, and you can try it out.

Stuff Everybody Knows

Examples

A presentation for junior web developers that boils down my 22 years of career experience into 2 hours. 2 hours is an absurdly long technical talk, but it's compelling enough that I've been invited to give it over 30 times. You can see slides from a recent presentation.

Share of Registry

Examples

I created a novel metric to visualize true popularity of frameworks in the ever-growing npm ecosystem. It has since been used by major corporations and browser manufacturers to inform and direct strategy for the web industry. You can read the article that introduced the metric.

npm Ecosystem Survey

Examples

I designed, ran, and analyzed the data from the npm Ecosystem Survey in 2018 and 2019, turning it into a series of well-received talks and analyses, revealing previously invisible trends in JavaScript development. You can read the 2019 survey results.

JSConf EU 2019

Examples

The culmination of years of research into trends in npm's registry data, my opening keynote at JSConf EU took multiple sources of data, gave developers context of the state of JavaScript in the world, and made recommendations about where we as an industry should go. You can see the video.

What I will do

Take your organization from stage 1 to stage 5 by recruiting and running your data team.

Improve data collection and querying
Implement useful visualization
Automate processing and reporting
Embed into your strategic team
Increase efficiency across all teams
Supply recommendations, not data

Who you are

Established, growing team (50+ people)
- Or growing team within larger corp.
Found product-market fit, looking to increase efficiency across the board
A product that grows the web: publishing, development and design tools, hosting and deployment services
Diverse team in an ethical company
Located in SF Bay Area or remote-friendly

Thanks for reading

If you'd like to get in touch:

Email: laurie.voss@seldo.com

Twitter: @seldo

You can also read my resume.