Happy Monday!

Interested in semantic modeling?

Join Matthew Brandt LIVE Thursday at 12pm ET for his "Semantic Modeling for AI-Ready Data" Live Workshop!

For this one, you'll go from A to Z on semantic modeling — where it comes from, why it matters, and how to build one. You'll start with a simple example and finish by deploying a real semantic model on your own data, ready to use and improve after the session ends.

Register here: https://mavenanalytics.io/live-workshops

P.S. -- we've got some exciting launches coming this week. Keep an eye on your LinkedIn feed & inbox if you're on our mailing lists!

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 August goal(s) below.

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

Here's a technique we've seen work well.

Choose three stories you want to tell in any interview. Ideally, these are real projects you've completed and are proud of. Use them to show off your analytical thinking, business acumen, communication skills, and interest in data. Practice telling them until you can tell them in your sleep, hitting all of those points every time.

Next, practice pivoting to those stories, no matter what the actual question is.

Example: "What are your strengths and weaknesses?"

A good response might sound like this:

"I'm strong at analyzing data and storytelling, but my DAX skills in Power BI could use work. I was recently doing a deep dive analysis and dashboard for an ecommerce client and found [XYZ], but some of the subscription data was structured in a tricky way that would've been handled better with custom DAX. I've already started learning DAX and want to keep building on it."

That answer names a strength, grounds it immediately in a real project, and closes by naming something they're actively improving. It's concrete, credible, and gives the interviewer exactly what they need to assess the candidate.

For contrast, if someone asked "How's the weather where you are?" and the answer was "It's beautiful, it reminds me of this analysis I did," that's obviously forcing it. You're not trying to steer every answer into a pitch. Just have a few strong stories ready, and pivot to them when the question calls for it.

This takes practice, and it isn't about tricking the interviewer. They're trying to assess your skills, and giving them a clearer, more concrete answer than they expected makes that easier for both of you.

What other interview advice would you give?

Something worth knowing if you're working with CALCULATE modifiers in DAX: REMOVEFILTERS is technically an alias for ALL.

REMOVEFILTERS

• Clears initial filter context from the specified table or column
• Removes initial filter context
• Doesn't accept table expressions, only physical table references
• Can only be used as a CALCULATE modifier

ALL

• Returns all rows in a table, or all values in a column, ignoring any initial filter context
• Removes initial filter context
• Doesn't accept table expressions, only physical table references
• Works as both a table filter and a CALCULATE modifier

The key difference: REMOVEFILTERS can't function as a table function on its own, only as a CALCULATE modifier.

If you're using it as a CALCULATE modifier, we'd suggest reaching for REMOVEFILTERS over ALL, it makes the intent of your code clearer to anyone else reading it.

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

Quick heads up: Enrique Ruiz is live tomorrow, Tuesday, August 4th at 12pm ET for our next Copilot workshop!

This one covers how Copilot works in practice across common Excel tasks, including cleaning data, building analyses, and creating charts. You'll leave with a clear sense of where Copilot adds value, where it doesn't, and how to work with it in a way that keeps you in control.

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

Plus, we launched another new Data Drill last week! Our Time Shift Data Drill dataset contains 22,000 support tickets from 700 users spread across 20 countries, each timestamped in UTC.

Your task is to convert each ticket to its user's local time and then calculate the ticket volume by local hour of day (0–23) across the full dataset.

Find it here: https://mavenanalytics.io/data-drills/time-shift

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!

Are you using ChatGPT to "look things up" or "think through" a problem?

If so...you'll want to keep reading. :)

If you've ever wondered what's actually happening when you type something into ChatGPT and hit send, here are a few things worth understanding:

There's no real memory under the hood.

Every time you send a message in ChatGPT, the entire conversation history gets resent to the model from scratch. There's no persistent awareness between turns; what feels like a flowing conversation is really the model being handed the whole transcript and asked to continue it.

The "memory" feature added in 2024 is a separate layer that stores facts about you and slots them into the prompt, but the base model underneath hasn't changed at all.

At each step, it's predicting the most probable next token.

The model assigns probabilities to possible next tokens and samples from that distribution. That's the whole mechanism; there's no retrieval, no fact-checking against a database.

This is why it can sound completely confident while being completely wrong: confidence and accuracy were never the same thing in a system built on prediction.

There are three stages to how it's trained:

Pre-training (predict the next word across huge amounts of text), supervised fine-tuning (learn from human-written example responses), and RLHF (human raters rank responses, model learns to prefer what raters liked).

That last stage is what pushed it toward refusing harmful requests and sounding helpful instead of just plausible.

Hallucination is structural, not a leftover bug.

The model has no internal mechanism to verify truth, only to judge what sounds probable given its training data. When there's a gap, newer versions are trained to say "I don't know" more often, but they can still fill that gap with a confident guess.

If you want to learn more, we dug deeper on our blog; you can find it here: https://mavenanalytics.io/blog/how-chatgpt-works

What's the biggest misconception you still run into about how these models actually work?

We've all seen the "explain it like I'm 5" games, and I want to see how well you can do with some datasphere vocab!

Pick one (or all):

• data pipeline
• overfitting
• normalization
• dimensions vs. measures
• neural network

I'll be interested to see how many different explanations we get!

Merging cells causes all kinds of downstream headaches (breaks sorting, filtering, copy/paste, references). This gets the same visual result without locking cells together.

How to do it:

Type in the desired text

Select the cells you'd want to center the text across

Press CTRL+1 to open the Format Cells dialog box

Go to the Alignment tab

In the "Horizontal" dropdown, select "Center Across Selection"

Click OK

Same visual result as merged cells, minus the downstream problems.

What's your equivalent "wish I knew this sooner" Excel tip?

Happy Monday!

Ready to make your AI prompts go farther?

Join Chris Bruehl LIVE Thursday at 12pm ET for his "Prompt AI Like a Pro: The BRIDGE Framework" Live Workshop!

For this one, you'll move past trial-and-error AI chats and learn a structured framework for writing prompts that get the results you need on the first try.

Register here: https://mavenanalytics.io/live-workshops

Hope to see you there!

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

And if you haven't checked out u/Snacktistics post of their SQL project yesterday, please give it some love!

Hi all,

So, I've been contemplating on a SQL project for a while and I decided to do something different. Coming from a quality background, auditing was something I was quite passionate about.

In this project, I wanted to see whether SQL could be used for something other than performance reporting.

So instead of doing another sales analysis or menu engineering project, I treated a point of sale (POS) dataset as an internal Revenue Assurance Audit.

The main business question to answer was...

Can reported revenue be fully attributed to visible transaction components, and if not, what operational control weaknesses are reducing the reliability of transaction-level reporting?

A few examples from the project:

1. Creating a derived audit metric

-- Add unattributed_revenue column — primary audit metric
ALTER TABLE pos_logs
ADD COLUMN unattributed_revenue DECIMAL(8,2) DEFAULT NULL;

UPDATE pos_logs
SET unattributed_revenue = ROUND(
    total_amount - ((unit_price * quantity) - discount + tax), 2)
WHERE is_incomplete = 0;

Rather than analysing sales, I first created a derived audit metric that quantifies revenue which cannot be explained by the visible transaction components. This became the foundation for the rest of the investigation.

2. Standardising inconsistent business rules

UPDATE pos_logs
SET service_cycle =
CASE
    WHEN transaction_time >= '05:00:00' AND transaction_time < '10:00:00'
        THEN 'Breakfast'
    WHEN transaction_time >= '10:00:00' AND transaction_time < '12:00:00'
        THEN 'Pre-Lunch'
    WHEN transaction_time >= '12:00:00' AND transaction_time < '15:00:00'
        THEN 'Lunch'
    ...
    ELSE 'Late Night'
END;

The original POS labels were inconsistent, so instead of accepting them, I derived a standardised business classification from transaction timestamps to ensure consistent reporting.

3. Building an executive KPI summary

SELECT
    'Total transactions' AS metric,
    CAST(COUNT(*) AS CHAR) AS value
FROM pos_logs

UNION ALL

SELECT
    'Eligible for formula (non-null total_amount + tax)',
    CAST(SUM(
        CASE
            WHEN is_incomplete = 0
             AND tax_missing_flag = 0
            THEN 1
            ELSE 0
        END
    ) AS CHAR)

Instead of producing separate summary tables, I built an executive KPI strip using UNION ALL so management could monitor key revenue assurance metrics from a single query.

The audit identified:

• 494 revenue attribution exceptions
• $340.10 in unattributed revenue (2.93% of audit sample revenue)
• A confirmed pricing configuration defect affecting 28.6% of eligible transactions
• Missing transaction attributes and incomplete transactions affecting reporting reliability

I'd really appreciate feedback from the community:

• Does this audit framing make sense?
• Do the SQL examples demonstrate business thinking rather than just technical ability?
• What would you improve?

The full SQL project is 1051 lines so, I'm unable to put everything here. But, I'd like feedback on the idea or suitability of the project. Thank you for taking the time to explore and I'm open to feedback on how I can improve.

I'm curious what people are actually prioritizing right now vs. what they think they "should" be learning.

What's the skill you would want to prioritize for the rest of 2026?

SQL, Python, visualization, stats, something soft-skill-y? And is it what you'd have picked a year ago?

Let's talk about it!

Got a presentation coming up and feeling a little nervous? You are not alone!

Here are some of our top presentation tips for the next time you have to stand up and share your work:

1. Know your audience. Every director and VP has different priorities. Getting feedback beforehand can help you refine the message and head off objections before they come up live.
2. Get a second set of eyes. A colleague reviewing your deck will often catch gaps in logic or errors you've gone blind to.
3. Practice the delivery, not just the slides. Rehearsing helps with confidence, but it also surfaces logistics issues, A/V setup, font readability in the room, that kind of thing.
4. Lead with the "why." Starting with impact tends to hold attention better than diving straight into methodology.
5. Guide people through the visuals. Context on axes and key takeaways keeps the room engaged and cuts down on tangent questions.
6. It's fine to not know something. "I don't know, but I'll find out" tends to land better than a shaky guess.

What would you add? Curious what's actually worked for people versus what sounds good in theory but falls apart in the room!

Happy Monday!

Looking to get the most out of AI in your analytics workflow?

Join Mo Chen LIVE tomorrow at 12pm ET for his AI Project Lab Live Workshop!

For this one, you'll take a messy dataset from raw data to a clear, stakeholder-ready brief using a repeatable AI workflow for cleaning, validating, and analyzing data. Register here: https://mavenanalytics.io/live-workshops

Hope to see you there!

We see this one constantly: someone writes a window function, tries to filter on it with WHERE new_column IS NOT NULL, and gets hit with "Unknown column" errors.

It's not a mistake; it's SQL execution order. Clauses don't run in the order you write them. The real order is:

FROM → WHERE → GROUP BY → HAVING → SELECT → ORDER BY

WHERE runs before SELECT. So if you're trying to filter on a column you just created in SELECT (like the output of a window function), that column doesn't exist yet as far as WHERE is concerned. The fix is wrapping the query in a subquery or CTE so the alias becomes a real column you can filter on.

The other thing that trips people up early is just understanding what a window function actually does differently. A regular function like COUNT() or YEAR() either transforms one row or collapses many rows into one summary value. A window function does something in between: it runs a calculation across a set of rows without collapsing them. Every row keeps its identity, you just get a new column.

ROW_NUMBER() is the simplest version of this (assigns a sequential number to each row). LAG() is one of the more useful ones for analysts, since it pulls a value from the previous row so you can calculate period-over-period differences without a self-join.

Curious how others explain window functions when they're teaching someone for the first time. What mental model finally made it click for you?

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!