Marriott International

- Lead a team of 9 associates responsible for reporting and data science supporting the US & Canada Sales, Marketing and Revenue Management teams, as well as our sister analytics teams.

Senior Director, Data Science and Reporting, US & Canada SMR

- Lead a team of 7 associates responsible for managing the data processing, report production and reporting development for our Americas Sales, Marketing and Revenue Management teams. 
Drive efficiency by leading the conversion of legacy code from SAS to Python, and making improvements in automation and task scheduling. 
- Work with Americas C-suite and senior leaders to show how analytics can help support their decisions, and work with technical teams to ensure reports, models and processes support leadership needs.
- Led development of a machine learning segmentation/benchmarking model that allows better comparisons between hotels across a variety of sales, marketing and revenue management metrics.  
- Led development of a machine learning based forecasting model to help forecast Marriott’s recovery from the impact of COVID-19.

Director, Data Science, Data Strategy and Analytics, Americas SMR

- Led overhaul of our monthly RevPAR forecasting process, getting buy-in and action from multiple stakeholders across both revenue management and finance. 
- Integrated new mathematical forecasting methods – using ANOVA, machine learning (Random Forests and GBMs) and time series decomposition to create an accurate company forecast that can be turned around in 48 hours. 
- Led a team of associates developing the “next generation” of our market compression tool, integrating SPG hotels and more complex machine learning methods.

Senior Manager, Revenue Management Analytics

- Responsible for monitoring adoption and usage of Marriott’s internal pricing and inventory decision support system, including the assessing the impact of internal marking and communication efforts.  
- Developed a suite of Tableau dashboards to track system usage and adoption. The dashboard has an audience of over 200 stakeholders and earned me the 2015 Q4 Business Leadership award. 
- Used statistical and machine learning methods, including ANOVA, clustering, regression, correlation and case/control tests to understand the impact of system usage on the financial performance of hotels.
- Efforts drove an increase in system adoption of over 30% in six months.

Senior Manager, Revenue Management Systems Deployment and Data Analytics

- Responsible for both ad-hoc and recurring reporting using SAS, SQL, Excel, Tableau, R and Cognos to manipulate raw data, and for presenting complex analysis to business users. 
- Used machine learning to develop a model of market demand that helped hotels better focus their pricing and inventory mix strategies up to a year in the future – the first such model in the industry. 
- Developed a forecasting model and led a monthly forecasting meeting that helped drive forecast accuracy down 5 percentage points over six months.

Manager - Revenue Management Data Analysis

- Responsible for developing and executing the pricing and inventory strategies for 13 hotels, as well as ad-hoc analytical work and strategy analysis. 
- Developed a deep understanding of customer segmentation, forecasting and supply and demand which helped drive very strong market share results for my hotels.

Revenue Manager

University of Maryland Global Campus

- Develop and teach introduction to Python and machine learning courses for undergraduate data analytics/data science program.

Adjunct Professor

- Perform role of technical expert, and help explain course concepts.
-  Taught classes using Python, Tableau, R, SQL and SAS and concepts including data visualization, data mining, predictive modelling and data management. The models we teach include Linear/Logistic Regression, Random Forests, Gradient Boosting Machines, Support Vector Machines, Neural Networks and deep learning.
- Developed an assignment using Python and Deep Learning to perform image recognition.

Faculty Assistant

Enterprise Rent-A-Car

- Responsible for all aspects of branch operations, from fleet organization and preventative maintenance to sales and demand generation. 
- Grew branch fleet from 80 vehicles to a peak of 120 vehicles by developing and executing sales plans and branch marketing strategies.

Branch Manager

University of Maryland - Shuttle-UM

Charter Planning Manager

Data can change the world, but humans tell the story. 

I’m passionate about helping people communicate better with data. 

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 well insights are delivered to your stakeholders, and how effectively your stakeholders can leverage them. 

Great communication amplifies the impact of great insights. 

Whether you’re a data analyst, data scientist, a leader, or a combination of all three, you can have a bigger impact on your organization by improving how you share your projects. I teach how to structure your communications effectively, how to tailor your message to your audience, and how to use visualizations effectively. 

I have almost 15 years of experience delivering value from data through great analysis and clear communication. Today, I lead an analytics team at Marriott International, and help data professionals and aspiring data professionals improve how they talk about their work through my newsletter discussing communication techniques, and the Penguin Analytics YouTube channel, discussing how-tos for analysis, communication, Tableau, Power BI and more. 

If you love finding and delivering value from data, let’s connect!

I don’t think companies need analysts who “drive decisions,” I think they need analysts who stop the stupid ones.

Not always. Not in every situation. But often.

Most of the messes I’ve seen didn’t come from bad data, they came from overconfident timelines, half-baked assumptions and a room full of smart people nodding because they thought everyone had done their homework.

Analysts see the leaps in logic and the numbers that don't quite add up, and we know no one really wants to discuss them... It's on us to ask the questions everyone is avoiding. 

When you bring a well-thought-out argument based on data and speak up, you'd be surprised how many people appreciate you giving voice to the things they were hesitant to say. 

So be bold, but bring data.

John Cook

Data Analytics Leader & Strategist | Professor | Consultant | Helping Data Professionals Communicate Better

One of the biggest dangers of being "data-driven" is pseudo-precision.

Just because you have data doesn't mean you can calculate anything meaningful from it.

I see this most often when decisions are hard. Someone wants to launch a new product line, or enter a new market, or restructure the team. The decision feels uncomfortable, so they turn to data for certainty.

We can always build something that predict's success. 

Sure. We can create business logic. We can estimate. We can build assumptions into a spreadsheet that spits out a number with two decimal places...

but if the situation is outside what we've done before, those rules are just guesses dressed up in a fancy data dress .

This is where data leaders need to use business judgment, not just technical skills.

It's our job to say: "This data doesn't represent what you're trying to measure" or "We're extrapolating too far beyond what we actually know."

Being data-driven also means knowing when the data can't drive the decision.

Sometimes the most valuable thing we can do is help people understand what they're actually asking for and whether their data can deliver it.

Sometimes data work is building fancy models, optimizing data pipelines and presenting ground-breaking insights. 

Yesterday, it was figuring out why the date stored as text wouldn't covert to a date.

The most important part of any dashboard is whether anyone actually uses it.

I learned that the hard way.

I once built a dashboard with perfect data, views tailored to a QBR, and charts so polished you could have used them to put on makeup.

Buuuuut... It took 20 seconds to load. 

Then you set your filters (and waited another 20 seconds) 

Then you clicked into the dashboard with the data (and waited another 20 seconds)  

It lasted six months. 

We still sent people the Excel data. 

We still created the Power Point. 

I learned user experience wins, even when the “user experience” is a janky spreadsheet everyone pretends to hate.

Ignore the human side and your beautiful work ends up gathering dust on Tableau Server until your admin says they need the disk space back.

Found a field in a dataset called Notes2.

There was no "Notes1." Also no "Notes."

Also no documentation.

I checked the data dictionary. I checked comments in old code. I asked three different teams.

Nobody knows.

Just Notes2… sitting there… confident... Mostly null...

Like this was completely normal behavior for a column.

My working theory: Notes1 made a run for it years ago and everyone decided not to talk about it.

Happy Friday! May you have no escaping columns.

Vague stakeholder requirements, thanksgiving edition. 

"What should we bring to Thanksgiving?" 

"Bring whatever you want!"

"Green bean casserole?"

"Well... not that."

"Mashed potatoes?"

"We have those."

"WHAT DO YOU ACTUALLY WANT?"

 "I'll know it when I see it."

I'm in Nashville this week, so you're going to get some country music themed posts. To start us off... 

Why is SQL like country music? 

They're both full of broken relationships!

I know I normally post about data analytics, AND I'm late to the party on this, but the song Golden is a banger. 

You may say it's not connected to data, but I have tested this theory 1,257 times by giving my daughter access to my Spotify jam.

The last few weeks felt like I was sprinting a marathon...

...But my team said they were fine and apparently I was the only one drowning.

Then Thanksgiving hit and I finally looked at my calendar.

It was meetings.

The special, time-sucking variety that spawn follow-ups, rework, and more meetings.

My weeks looked like someone dumped a box of mismatched LEGO bricks into my Outlook calendar.

It wasn’t workload, or priorities...

It was fragmentation.

Data work can survive pressure, but it can't survive a calendar finely chopped into 30-minute slices.

If your calendar is a mess, you feel a mess. 

More in this week's newsletter.

Happy Thanksgiving!! 

Get TF off Linkedin.

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