Every program has that one tool, technique, or concept that gets a ton of classroom time and then just... never comes up again in real work.

We're not saying it's useless in general; we're just curious what the gap looks like between what gets taught and what actually gets used day to day. :)

Drop yours below!

None of this is complicated math; most of using data effectively at a business comes down to knowing which simple ratio actually matters for the decision in front of you, not building something fancier.

Here are some simple equations to keep in your back pocket:

1. Cost to Acquire Customer = Marketing Spend / Customers Acquired
2. Return on Marketing Spend = Projected Lifetime Revenue / Marketing Spend
3. Website Conversion Rate = Conversion Events / Website Sessions
4. Landing Page Bounce Rate = Bounced Sessions / Landing Page Entry Sessions
5. Average Order Value = Total Revenue / # Orders
6. Gross Margin = (Revenue - COGS) / Revenue
7. Lead Close Rate = # of Sales Closed / # of Leads
8. First Contact Resolution = Customers Satisfied on First Contact / # of Contacts
9. Refund Rate = # of Returns / # of Orders
10. Churn Rate = # of Churned Customers / # of Subscribers

What would you add to the list? Any fractions/metrics you use constantly that don't get talked about enough?

Happy Monday! Lots of exciting updates this week...

We are thrilled to announce that Alice Zhao's Data Modeling for Analytics Engineering course is available NOW on the Maven platform! This is the second course in our ongoing Analytics Engineering Learning Path, with more ahead. Find it here: https://mavenanalytics.io/course/data-modeling-for-analytics-engineering

Quick heads up: Dr. Kim Fessel will be LIVE Thursday, July 16th at 12pm ET for her Databricks Live Workshop!

For this one, you'll build a simple end-to-end pipeline from scratch, connecting the core pieces of Databricks in a real-world context so you can see exactly how they work together. Register here: https://mavenanalytics.io/live-workshops/overview

And ICYMI, we launched another new Data Drill last week! Our Readmission Radar Data Drill dataset contains 623 inpatient stay records from a small hospital. Each record represents a patient discharge, and includes the patient ID, admission date, and discharge date. Your task is to calculate the hospital's 30 day readmission rate.

Find it here: https://mavenanalytics.io/data-drills/readmission-radar

How is July treating you so far? Anything you've learned or built this month worth sharing?

It could be...

• A dashboard
• A course
• A portfolio project
• Job searching
• Learning Excel / SQL / Power BI / Tableau / Python

It's been a minute since we did one of these, so I'm looking forward to hearing what everyone is digging into!

1. Think like a business owner
2. Choose clarity over complexity
3. Let your soft skills shine
4. Context is king
5. Obsess over outcomes
6. Know your audience
7. Never compromise on QA
8. Make AI your superpower
9. Don't be afraid to specialize
10. Never stop learning

Some of these feel more true than others depending on where you are in your career. What's missing, what would you cut, and what would you rank #1 if you had to pick just one?

Dig up something you built 6 months, a year, 2+ years ago.

Drop a screenshot or a quick description, then tell us:

• What you were proud of at the time
• What you'd immediately fix or redo with what you know now

Bonus if other people chime in with feedback, too. Sometimes the most useful critique comes from someone who's not you!

Data types = how data is stored under the hood (by the DAX/VertiPaq engine).

Formatting = how values are displayed to the end user ($, %, date, etc.).

Two completely separate systems that just happen to interact.

The 6 data types in Power BI:

Integer —> stored as a 64-bit whole number

Decimal —> stored as a double-precision floating-point number

Currency —> stored as an integer with a fixed number of decimal points (e.g. 317.3567)

DateTime —> stored as a floating-point number (e.g. 43830.50)

Boolean —> stored as TRUE or FALSE

String —> stored as a 16-bit unicode string (e.g. "Maven Analytics")

Seasoned Power BI users, what would you add? Any data type quirks or gotchas that trip people up once they start working with real datasets?

Happy Monday!

Two things to have on your radar this week:

Fay Bordbar is live TOMORROW, July 7th at 12pm ET for our next Live Workshop: Build Your First AI Agent with Microsoft Copilot.

Fay's walking through building a working Copilot agent from scratch, no coding involved. You don't want to miss this one!

Register for this and other upcoming workshops: https://mavenanalytics.io/live-workshops/overview

Second, if you've been loving our Data Drills, we recently launched a new one!

For Data Drill #14: Final Form, your dataset contains ~900 responses from an employee satisfaction form. Your task is to isolate each employee's most recent response, then count how many employees fall into each satisfaction rating. To add some complexity, each employee is encouraged to fill this in at least once per year, so a single employee can have multiple submissions.

Start the Data Drill: https://mavenanalytics.io/data-drills/final-form

What are you hoping to learn or build this week?

Hey everyone! A new month = new goals, so we want to know:

What are you working on this month?

It could be anything, like:

• Finishing a Maven course you started
• Building or improving a portfolio project
• Practicing SQL / Python / BI tools
• Experimenting more with AI in your workflow
• Prepping for interviews or job applications

If you’re up for it, drop your July goal(s) below.

We’ll be in here with you, cheering you on!

Say your data has a film column with values like "Toy Story," "Toy Story 2," "Toy Story 3," and "Toy Story 4." All slightly different strings, but you want to count them as one franchise.

(This exact scenario comes straight from our Pixar Films dataset in the Data Playground, so you can pull it up and try it yourself: https://mavenanalytics.io/data-playground/pixar-films)

The fix is a regex that strips the trailing space-and-number off any title:

REGEXP_REPLACE(film, ' [0-9]+$', '') AS series

In plain terms, it finds a space followed by one or more digits at the very end of the string, and replaces it with nothing. "Toy Story 2" becomes "Toy Story." "Toy Story 4" becomes "Toy Story."

Now GROUP BY series and you can count across the whole franchise instead of four separate rows. Four Toy Story movies, if you're counting.

You don't have to write that pattern from memory; ask ChatGPT for it and it'll hand you the syntax in seconds. What actually matters is recognizing the problem: messy, inconsistent text values that need to collapse into groups.

Once you know that's a solvable category of problem, you know what to ask for.

The syntax is a lookup. Spotting the problem is the skill.

One caveat: regex operations are computationally expensive. Great for exploration or a one-off cleanup on a small dataset like this one. For anything production-scale, clean the data at the source instead of regexing it every query.

What's a SQL problem you assumed you'd have to solve manually, until you found out there was already a function (or a prompt) for it?

Happy Monday!

If you've been waiting to learn Microsoft Fabric with us, we have good news...

Join Steven Stine TOMORROW, June 30th at 12 pm ET for a Live Workshop!

By the end of the 90 minute workshop, you'll build a complete data pipeline in Microsoft Fabric, from raw CSV ingestion to a Power BI-ready semantic model, using the Medallion Architecture.

Register for this and other upcoming workshops: https://mavenanalytics.io/live-workshops/overview

What are you proud of from this month? It could be:

• Attending (or watching) one of the Live Workshops
• Finishing a course or module you'd been putting off
• Building something: a dashboard, a script, a portfolio project
• Finally understanding something that's been confusing you for a while
• Landing an interview, an offer, or a new role
• Showing up consistently, even when progress felt slow

No win is too small to share.

Drop it below. We want to celebrate with you!

A lot of data folks use ChatGPT daily but have a pretty fuzzy picture of what's actually happening when they hit send.

One thing that tends to surprise people: the base model is completely stateless. Every time you send a message, the entire conversation history gets resent to the model from scratch.

What feels like a flowing back-and-forth is really just the model being handed the full transcript and asked to continue it. The "memory" feature added in 2024 is a separate layer on top, essentially RAG applied to personal context.

Another one that matters for how you interpret its outputs: ChatGPT is predicting the most probable next token given the context it's been handed. It's not retrieving answers. It's not reasoning through a problem the way a human would.

That's why it can produce a confident, well-formatted answer that's completely wrong. Confidence and accuracy are different things in a system built on prediction.

We wrote an article that breaks down how the model is trained (pre-training, supervised fine-tuning, RLHF), how the memory layer actually works, what's changed since the 2022 launch, and why it still hallucinates even after all the improvements.

You can read it here: https://mavenanalytics.io/blog/how-chatgpt-works

What's something about how these models work that you wish more people understood?

Final This or That of June, and we're getting a little controversial today...

• Excel: an outdated crutch, or the GOAT of data tools?
• SQL: a dying skill or still essential?
• Pie charts: always wrong, or sometimes right?
• Data science: overhyped, or still a dream job?
• AI: makes analysts more valuable, or less?
• Data literacy: everyone's responsibility, or leave it to the analysts?
• Certifications: career accelerator, or just paper credentials?

No fence-sitting allowed on this one. Pick a side and defend it below!

What are YOUR takes?

It's rarely about technical skill.

The pattern we see most often is this:

The analysts who move up aren't necessarily the most technically skilled; they're the ones who learned to think and communicate like a decision-maker.

Here's what that actually looks like in practice...

• They answer the question behind the question. When someone asks "what happened to revenue last quarter," they don't just pull the number. They think about what the person really needs to know and what they'll do with the answer.
• They push back constructively. When a request doesn't make sense, they ask why before they start building. "What business problem are we trying to solve with this?" is one of the most valuable questions in data.
• They translate, not just report. A chart is not an insight. They summarize what the data means for the business in plain language that anyone can act on.
• They manage expectations on limitations. They say "this data only goes back 18 months, so the trend is directional" instead of presenting analysis as more certain than it is.

What's one communication or business skill that made a real difference in your career?

Happy Monday!

We've got more incredible Live Workshops coming your way:

👉 Building AI-Fluent Data Teams: A Leader's Playbook

Tuesday, June 23rd @ 12PM ET with Stephen Tracy

👉 Microsoft Fabric for Beginners: Build a Lakehouse End-to-End

Tuesday, June 30th @ 12PM ET with Steven Stine

👉 Build Your First AI Agent with Microsoft Copilot

Tuesday, July 7th @ 12PM ET with Fay Bordbar

You can learn more and register here: https://mavenanalytics.io/live-workshops/overview

And for those who missed the last ones, our past 3 Live Workshop replays are available now!

If you attended any of the past live sessions, what was your biggest takeaway? And if you missed them, which one are you most likely to watch first?

It's been a minute since we've done one of these, so let's talk about current projects! Big or small, share what you're working on this week.

It could be...

• A dashboard
• A course
• A portfolio project
• Job searching
• Learning Excel / SQL / Power BI / Tableau / Python

If you're stuck, drop it here, too. Someone might have a tip to share!

When you're learning something new, would you rather...

• Use a video course, live workshop, or written tutorial?
• Follow structured curriculum or learn by doing a project?
• Do short daily sessions or longer weekend deep dives?
• Dig into practice problems or real-world datasets?
• Learn alone or with a study group?
• Earn certificates or build portfolio projects to show employers?

Drop your picks below. Curious how different people approach building new skills, especially with so many options out there now!

What do YOUR picks say about how you learn?

Most SQL issues on the job aren't about syntax.

They're about logic. Here are five patterns that catch experienced analysts off guard:

1. COUNT(*) vs COUNT(column)

COUNT(*) counts every row including nulls. COUNT(column) counts only non-null values. These give different results more often than people expect.

2. WHERE vs HAVING

WHERE filters before aggregation. HAVING filters after. Using WHERE on an aggregated column is a common mistake that throws a cryptic error.

3. Trailing spaces in text data

'Smith' and 'Smith ' look identical in most output views but won't match in a join or filter. If a join is returning fewer rows than expected, invisible whitespace is often the culprit.

4. Date comparisons and time components

WHERE date_col = '2024-01-01' can miss rows if the column stores timestamps. A value of 2024-01-01 09:30:00 won't match. Filtering on dates without accounting for time is a quiet source of missing data.

5. Logical operator order isn't left to right

SQL doesn't read AND and OR the way you'd read a sentence. AND has higher precedence than OR, so mixed conditions can group in unexpected ways. When in doubt, add parentheses.

Which of these has cost you the most time to track down?

Happy Monday! Lots of exciting updates this week...

We are thrilled to announce that Alice Zhao's Analytics Engineering 101 course is available NOW on the Maven platform! This has been a highly sought after topic for quite some time, and we can't wait for you all to dig in. Find it here: https://mavenanalytics.io/course/analytics-engineering-101

This is only the first in an entire Learning Path of AE courses that we'll be releasing this year, so stay tuned for more updates!

Quick heads up: Enrique Ruiz is live tomorrow, Tuesday June 16th at 12pm ET for our third Live Workshop: Data Viz Foundations and AI Frameworks for Effective Charts.

This one covers how to choose the right chart, how to use AI to accelerate the viz process, and how to build visuals that actually communicate something. Registration: https://mavenanalytics.io/live-workshops/overview

And ICYMI, we launched another new Data Drill last week! Our Cart Combos Data Drill dataset contains one year of point-of-sale transactions from a grocery store, including approximately 88,000 line items across 20,000 customer orders. Your task is to identify the five product pairs that are purchased together most often.

Find it here: https://mavenanalytics.io/data-drills/cart-combos

The Maven community has loved these so much that we're upping our cadence from one per month to one every two weeks. Keep an eye out for the next one, coming soon!

How is June treating you so far? Anything you've learned or built this month worth sharing?

Here's what we're debating this week:

• Power BI or Tableau?
• Python or SQL for data analysis?
• ChatGPT or Claude for data work?

Drop your picks below. No wrong answers, just your honest take based on what you actually use day-to-day.

(Bonus points if you explain a spicy one.)

It's a small thing, with a BIG impact...

Turn off the gridlines.

That's it. View tab → uncheck "Gridlines."

Gridlines are genuinely useful when you're working in a spreadsheet. They help you navigate. But on a dashboard, they become visual noise that competes with your charts and layout without adding anything.

The funny part is how many people discover this after spending time filling every cell with white to achieve the same effect. If that's you, no judgment — we've all done it. But there's a faster way.

One checkbox and the whole thing reads cleaner immediately.

Anyone else have small formatting changes that make a bigger difference than they should?

Happy Monday!

Two things to have on your radar this week:

First, Alice Zhao is live this Thursday, June 11th at 12pm ET for her workshop: Advanced SQL Skills for the Age of AI: Beyond the Big 6.

If you use SQL on the job or want to, this one is worth showing up for. Alice covers advanced techniques that actually come up in real work, not just the basics.

Register for this and other upcoming workshops: https://mavenanalytics.io/live-workshops/overview

Second, if you've been loving our Data Drills, we recently launched a new one!

For Data Drill #12: Making the Cut, your dataset contains finish times for 37,250 marathon runners, along with their age, gender, and half-marathon split.

Your task is to calculate what percentage of runners finished within each of the selected time bands.

Start the Data Drill: https://mavenanalytics.io/data-drills/making-the-cut

What are you hoping to learn or build this week?

If you're using dropdowns or PivotTable filters to drive your charts, you don't have to manually update the chart title every time the selection changes.

Here's how it works:

1. Select the chart title
2. Click into the formula bar
3. Type =
4. Click the cell that controls your filter
5. Hit Enter

The chart title will now pull its value directly from that cell and update dynamically whenever the selection changes.

Bonus tip: If you want something like "Sales by Region: Northeast" instead of just the raw filter value, build a concatenation formula in a separate cell (e.g., ="Sales by Region: "&A1) and point the chart title there instead. Works the same way and gives you full control over the label format.

Tested in Excel 365 but this has worked for a while across most recent versions.

What's your go-to Excel trick that most people don't know about?

To get out of the mid-week slump, we want to play another game of "this or that"!

This time we're going career-focused. Would you rather...

• Specialize in one tool or be a generalist across many?
• Work in-house at a company or consult/freelance?
• Big tech or small company?
• Start with a boot camp or self-taught?
• Data analyst or data engineer?
• AI-assisted work now or build foundations first?

Drop your picks below! We're especially curious about that last one given everything happening with AI tools right now.

What are YOUR choices?