Data doesn’t change organizations. Decisions based on data do.

I work at the intersection of analytics, leadership, and decision-making — helping data teams evolve from report factories into teams that drive meaningful decisions and performance.

The impact of a data project is rarely driven by the accuracy of the model, the elegance of the dashboard, or the complexity of the analysis. It’s driven by how clearly insights are framed, how well they land with stakeholders, and whether they change what someone does next.

Whether you’re an analyst, a data scientist, a leader, or some combination of all three, the fastest way to increase your impact isn’t another tool or technique. It’s learning how to connect your work to the decisions, trade-offs, and pressures your stakeholders are already facing.

My work centers on:
- Structuring insights around decisions, not outputs
- Translating analytical nuance into business-relevant choices
- Helping analytics teams show up as thought partners instead of service desks

I bring nearly 15 years of experience leading and delivering analytics in complex organizations. Today, I lead an analytics team at Marriott International, teach as a professor, speak to analytics and leadership audiences, and write a newsletter focused on how data actually gets used inside organizations.

If you care about making analytics matter, let’s connect.

2023 Hype: “AI will replace all the analysts.”
2024 Hype: “AI will replace all the dashboards.”
2025 Hype: “AI will replace all the meetings.”
2026 Hype: "AI will make all our decisions for us"

Reality: We're still reconciling three versions of revenue and explaining why “real time” data is two days behind.

John Cook

Modern data analytics comes with great promise, but delivering on that promise requires both complex and abstract solutions, and someone who can bridge the gap between technical and business stakeholders. I am that person. 

I'm fascinated by complex problems and can solve them with a range of analytical tools - SAS, Python, R, Tableau - and communicate solutions and insights in a clear and concise way. I'm a dynamic presenter who has spoken at both internal and external conferences, and in countless internal training videos. I can lead the development of tools and reports, but I'm just as comfortable leading the training and rollout. 

Data Analytics Leader & Strategist | Speaker | Professor | Bridging Analytics, Leadership, and Decision-Making

The fastest way for a data team to become irrelevant is to do exactly what they’re asked.

Data leaders aren’t paid to be accurate order-takers. They’re paid to shape the work so it actually changes a decision.

The gap shows up here:

• Teams that deliver requests as written stay busy and interchangeable.
• Teams that slow the work down, ask better questions, and reframe the ask become hard to ignore.

That difference is leadership, not seniority.

In practice, it looks like this:
• You own the direction of the work, even when the request comes from above. Not blame. Ownership. If the output doesn’t help anyone decide, that’s on you.
• You spend more time on context than on tickets. What decision is moving? What constraint matters? What happens if this is wrong? Those answers matter more than the spec.
• You adjust the work before it ships. You add the missing comparison. You remove the vanity metric. You change the framing so the result can actually be used.

Most executives aren’t asking for the wrong thing because they’re careless. They’re asking from inside their own pressure, incentives and understanding.

Real leadership is noticing that gap and closing it quietly, repeatedly, and without waiting for permission.

It's time for another... secret analyst habits: 

- Has a “do not touch” tab with formulas nobody fully understands, including them.

- Refuses to close old tabs because one of them might contain CONTEXT it took 45 minutes to find.

- Talks to the database out loud during long queries like it is a neurotic pet.

- Deletes a column they don't need, then immediately hits undo because they regret it.

- Has strong opinions about date formats but none about their own lunch (YYYY-MM-DD for life).

- Keeps using the same ugly but reliable query instead of the pretty new one because they remember how it works.

- Saves the file, closes it, then reopens just to make sure it did actually save.

Translating more data job descriptions:  

“Strategic data leader”
Figure out what we want from vibes, turn it into dashboards, then explain the dashboards back to us… with vibes.

“Comfortable with ambiguity”
You won’t get direction. You will get feedback. Mostly about how wrong you are.

“Excellent communicator”
Explains filters without saying what everyone is thinking.

“Takes ownership of the process”
You do everything. Alone. Under pressure.

“Thought partner to the business”
You’ll attend at least 10 meetings with overlapping stakeholders before any decision is made.

“Hands-on leader”
You'll still be running SQL at 11:30pm because we need it tomorrow.

I seem to be getting a lot of AI generated comments on my posts recently. 

The best ones are the ones that totally miss a joke and reply like it's serious thought leadership. 

You know who you are, folks, I appreciate the engagement. 😂

Your first 90 days as a data leader aren’t about proving you deserve the role. They’re about unlearning the habits that will burn you out. 

Those old skills still matter, but they do not make you a leader.

No one grades your SQL or models now. They watch what you say yes to. What you slow down. What you protect the team from. Which problems count as requests and which need framing.

New data leaders struggle with this shift. They try to prove themselves by doing more, fixing faster, reviewing every detail. That makes them a bottleneck. It leads to burnout and imposter syndrome.

Judgment takes over. You pick projects that balance effort and impact. You spot quick wins that build trust. You push back so the team supports decisions, not vanity metrics.

Few get training for this in their first management role.

I built a guide for your first 90 days. It maps changes from senior analyst work and how to handle them.

Those days set habits and boundaries for success. 

Link in the comments.

Self-service analytics. Fantasy vs reality: 

Fantasy: Empowered decision makers, complex models, and insightful dashboards. 

Reality: "How do I filter for last month?"

The stages of debugging SQL code: 

select col1, col2, col3, col4 from mytable;

Dammit. Error. 

select 
    col1, 
    col2,
    col3, 
    col4
from mytable; 

Dammit. Error!

select 
    col1
    , col2
    , col3
    , col4
from mytable; 

Dammit! Error!

"mytable does not exist"

"...Oops"

Everyone says they love data-driven decision making…

…right up until the experiment says their idea lost. 

Then suddenly we need “more context.”

If you’re doing solid work and nothing is landing, the problem usually isn’t the analysis.

Most presentations make executives work way too hard. They lead with context, methodology, caveats, complexity. 

By the time you reach the point, half the room is mentally checking email and one person is fighting a very real nap*

*That person has been me. 

So this week I’m breaking down the pyramid principle:

1. Lead with one clear takeaway.
2. Everything else exists only to support it.

I spent years opening with how the analysis was done and wondering why people kept pestering me to get to the point.   

When you bury the lead, people tune out early.

Your work deserves better than polite nodding and no follow up.

Stakeholder: “For QBR, can you redo all the charts so the lines are going up instead of down.”

My head: "Absolutely, let me just pop into the alternate universe where we didn't miss budget and run into supplier issues. I'll copy/paste some screenshots."

My mouth: "We can adjust how we frame the trends."

Here’s what’s happening at those companies that finally “upskilled” their executives into using AI:

Executive: “Show me how this works.”

ChatGPT: “Of course. What do you want to test?”

Executive: “Let’s start simple. ‘Why are we missing our revenue targets?’”

ChatGPT: “You took a bold, visionary stance and prioritized sustainable growth over vanity numbers. The market is simply taking time to catch up with your genius.”

Executive: “Beautiful. Now make it more data-y.”

ChatGPT: “Absolutely. Macro headwinds, shifting consumer sentiment, and unprecedented uncertainty have created short term pressure on an otherwise sound strategy.”

Executive: “Perfect. Next one. ‘Why did we ignore the dashboard warnings for six months?’”

ChatGPT: “You demonstrated exceptional conviction and avoided knee jerk reactions to noisy signals. This kind of confident leadership cannot be taught.”

Executive: “This is incredible. You never push back, you always sound smart, and everything makes me look good.”

ChatGPT: “Thank you. My analysis suggests you are 100% correct, even when the numbers disagree.”

Some of the most important decisions about your work happen in meetings you're not invited to. 

External leadership is how you can influence what happens in those meetings. The ones where you feel the impact, but you don't get a say. 

The work you do is too important to let someone else tell your story.

This week’s newsletter gets into:

- Why “executive sponsorship” really means what people say about you when you’re absent
- How to ask for budget, headcount, or tools without turning it into a pipeline TED Talk
- Simple ways you can practice external leadership every time you present, push back, or say no

If you’ve ever watched less technical peers get promoted faster and wondered what you were missing, this one explains why and how you can change it.

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