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Wyatt Shapiro

Director of Data Platform at Medically Home

Wyatt Shapiro is the Director of Data Platform at Medically Home, a healthcare company.2 Here are some key details about Wyatt Shapiro's professional background:

Career at Medically Home

Wyatt joined Medically Home in 2021 and has progressed through several roles:

  • Started as Senior Data Engineer
  • Promoted to Lead Data Engineer
  • Currently serves as Director of Data Platform2

This career progression demonstrates Wyatt's growth and increasing responsibilities within the company's data and technology teams.

Professional Experience

  • Currently works at Medically Home1
  • Based in the New York City Metropolitan Area1
  • Has over 500 connections on LinkedIn1

Education

Wyatt Shapiro attended the University of Pennsylvania1, though specific details about his degree are not provided in the available information.

Skills and Expertise

While not explicitly stated, Wyatt's role as Director of Data Platform suggests expertise in:

  • Data engineering
  • Data platform management
  • Leadership in technology teams

Other Professional Activities

In 2018, Wyatt collaborated with Sam Korus to create a project called "knowledgedrop", indicating involvement in side projects or innovations related to data or technology.3

Wyatt Shapiro appears to be an experienced data professional who has quickly advanced to a leadership position at Medically Home, leveraging his skills in data engineering and platform management.

Highlights

Jul 17 · linkedin

You hold your breath, steady your hands, and deftly pull a block out from the tower. As you move to place the block on top of the tower, you pray for it to hold long enough to complete your turn.

The game of Jenga ratchets towards instability by design. The only guarantee is the tower will eventually fall and someone will lose.

What if I told you that your organization's analytics may be a game of Jenga.

Ad-hoc models are built, tweaked, passed around, manually refreshed with more data, and tweaked more. Your rickety analytics tool is constructed brick by brick until one day it collapses at the wrong moment.

Sound familiar?

Let's use the classic game to help us build a better process.

Rules of Jenga:

  • If the tower falls on your turn, then you lose - no one actually wins.
  • Once you touch a block, it has to come out - no testing allowed.
  • Once you pull a block, it has to be placed on top - no way to remove it.

Behaviors that ensue:

  • You only make changes to reduce the likelihood of personal failure.
  • You hold back information on what blocks may be safer to pull.
  • Your turns slow down dramatically as the game progresses.

What can we do to change the incentives?

The goal should be to build the tower as tall as possible together. The agreed-upon rules should allow for testing, re-dos, and removals. Then, our behaviors will reflect both the motive to expand collective opportunity and to diminish fear of personal risk.

Remind me - how does this relate to analytics?

Imagine a set of team rules and tools where:

  • You can confidently experiment with changes to a data model in a separated development environment with no chance of messing up production.
  • A peer can thoroughly review the proposed changes without having to painstakingly re-evaluate the whole thing every single time.
  • You can measure what will change (ie. data diff) before an edit is made in production.
  • You can run tests to ensure only valid changes are introduced.
  • You can communicate with your stakeholders on the upcoming changes before they are made.
  • You can deploy small changes easily and if something goes wrong, then you can roll it back.

Now our behaviors evolve to:

  1. More confidence in updates so changes are made more often.
  2. More frequent changes so each update can be smaller and more contained.
  3. Smaller releases so bad updates are detected, reverted, and resolved more quickly.

As an organization chooses to invest in the maturity of the analytics development lifecycle, then the game of Analytics Jenga becomes something different altogether. The once unstable tower of data assets (dashboards, visualizations, models) can now reach new heights and stay there.

Jul 8 · linkedin

You are a new member of the Data team and you are on your first rotation in answering ad-hoc questions from the business. How do you approach the following request?

“What is the number of daily active users?”

You really want this question resolved. After all, it is your first week on the job and you have to show the company you deserve to be here. You search around the existing dashboards using keywords and send the link over to the stakeholder. Huzzah! Task done!

But the person quickly responds “Yep, I knew about that dashboard but it doesn’t give me what I need. I actually need daily active users separated out by each product. And I really only care about the top 3 markets for the last fiscal year.”

At this point, you have more questions than answers.

  • What does top mean? By number of users, by highest usage percent, by revenue?
  • What does market mean? By geography, by sector, by company size/revenue?
  • When does the fiscal year start/end?
  • How are products differentiated here?
  • What action(s) make a user active?

Being the good little data steward you are, you dive deep into relevant documentation (ie. data catalogs, definitions, lineage, etc). This context helps you answer the bulk of your questions. You write some SQL to calculate the numbers, and excitedly send it over in a hurry.

“Hey thanks for the fast turnaround, but this does not seem in the range of what I was expecting. Can you double check what you sent?”

Oof. Instead of nervously rattling off questions in hopes of clarification, you sheepishly turn to your teammate for assistance. They spot check your numbers and quickly sense that something is wrong because your number is an order of magnitude higher than they expect. Once they review in depth, they highlight the issue and provide you with a recommendation to fix it. This critique helps you form intuition by establishing a baseline. You are more likely to detect an abnormal result because you know what normal looks like. You can return to the stakeholder with more certainty in your future responses.

Let’s rewind on the motives of “you the data analyst” and replace it with “Large Language Model” or the LLM for short:

  1. Y̶o̶u̶ ̶t̶h̶e̶ ̶d̶a̶t̶a̶ ̶a̶n̶a̶l̶y̶s̶t̶ The LLM really wants to finish a task.
  2. Y̶o̶u̶ ̶t̶h̶e̶ ̶d̶a̶t̶a̶ ̶a̶n̶a̶l̶y̶s̶t̶ The LLM will make reasonable guesses when faced with an ambiguous request.

Now let’s focus on what made “you the data analyst” better and perform the same replacement:

  1. Y̶o̶u̶ ̶t̶h̶e̶ ̶d̶a̶t̶a̶ ̶a̶n̶a̶l̶y̶s̶t̶ The LLM needs context to produce more relevant answers tailored to the business.
  2. Y̶o̶u̶ ̶t̶h̶e̶ ̶d̶a̶t̶a̶ ̶a̶n̶a̶l̶y̶s̶t̶ The LLM needs critical reviews to improve.

Though I am drawing anthropomorphic comparisons, I think the thesis is thought provoking. The unique contextual data and human-in-the-loop critiques used to onboard an LLM will drive how successful they become within an organization. Much like onboarding our human counterparts!

Related Questions

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How has Wyatt Shapiro's work impacted healthcare technology?
Wyatt Shapiro
Wyatt Shapiro, photo 1
Wyatt Shapiro, photo 2
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Location

New York City Metropolitan Area