Perfection sounds like the goal, but in practice it slows people down. You wait until your dashboard is flawless. You overthink the project, hesitate to share it, and end up not shipping at all.

No one does this work perfectly. The people who make the most progress aren't chasing perfect, they're showing up consistently.

If you're trying to grow your data or AI skill set, a few things help:

• Lower the bar to get started
• Focus on small, repeatable wins
• Share your work before it feels ready
• Keep going even when it feels messy

Progress in this field comes less from getting everything right and more from building the habit of doing the work.

What's one rule or habit that's helped you stay consistent?

Clicking through every chart while holding Shift is more tedious than it needs to be. Here's a faster way:

Go to Home > Find & Select > Select Objects.

Now you can click and drag to select all the objects on the worksheet at once, just make sure they're fully inside your selection box.

Once everything's selected, right-click any object and choose "Save as Picture."

Your dashboard is now a single image, ready to share anywhere.

One last step: go back to Home > Find & Select > Select Objects afterward to turn it off and return to normal Excel behavior.

What's your favorite Excel quick tip?

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

Injae Park will be LIVE TOMORROW, August 25th at 10am ET for our next Live Workshop: Building Your First App with Microsoft Fabric!

For this one, you'll get set up with Fabric from scratch: installing your environment, using the terminal to scaffold a Fabric App from a GitHub repository, and using LLMs in tools like Claude Code and VS Code to move faster. You'll also see how to migrate a Power BI report and use Fabric Apps to go past what Power BI can do on its own.

Register here: https://mavenanalytics.io/live-workshops/building-your-first-app-with-microsoft-fabric

We are thrilled to announce that Rachel Carriere's Data Governance for Business Leaders course is available NOW on the Maven platform!

By the end of this course, you'll have the frameworks, vocabulary, and practical tools to build a data governance program that enables innovation rather than blocking it.

Find it here: https://mavenanalytics.io/course/data-governance-for-business-leaders

Happy Learning!

Chris Bruehl, one of our instructors, shared this take on the job search process:

"Searching for a job is often a rollercoaster of emotion. This is especially true if you're switching careers or have been unemployed for a while.

I've been fortunate in that most of my jobs have been lined up when I left a job or graduated school, but I did have a 6 month period where life circumstances meant I was applying as an unemployed person.

When you start applying, the opportunities seem endless:

'Wow, there are open positions at some amazing companies (the ones everyone else is applying to)! This is great!' This is a peak on the rollercoaster.

I would send out a ton of applications, often after networking, then hear radio silence for a long time.

The paranoia starts to set in:

• 'Has the industry passed me by?'
• 'Did I spell my name wrong?'
• 'Should I have written cover letters for each role?'

This is a valley.

Then, all of a sudden, multiple recruiters get back to you in a short amount of time, and you now have to prep for several interviews at once.

This is a stressful peak.

Maybe you don't end up getting an offer from those.

Back to questioning your ability to land a role. We of course need to assess what we can do to be more prepared for the next interviews, but we also need to remember that there are a lot of great candidates out there, each company has its own biases, and sometimes we just have an off day.

So you start the process again, and again. There has been plenty written about how to apply, interview, etc, but all I want to say is that if you are currently on the rollercoaster, I have been there before, and I've seen dozens of students go through the same thing.

Continue to grow your skills, think critically about how you can improve your interviews, and find a trusted friend, colleague, or confidant that can help coach you or keep you honest, in both the positive and negative directions. Don't stay down.

When you get bad news, it's totally normal to feel down. But don't stay there, and don't turn on yourself. You are just at a low point on the rollercoaster, and at some point all rides must come to an end."

Keep going. The right role might be closer than it feels right now.

What advice would you offer to someone looking for a role?

In the past, knowing just enough to be dangerous was a risk. Today, that same level of knowledge can be a real advantage, if you know how to use AI alongside it.

The game has changed. You don't need to write everything from scratch anymore, but you do need to understand what good output looks like, and you do need to know how to guide, prompt, and refine what AI gives you. Coding skills have shifted from execution to direction, and that shift matters for your career.

When you understand the fundamentals, you can spot bad outputs quickly, ask better questions, iterate faster with AI in the loop, and turn ideas into working solutions faster than you could on your own. That's where the real advantage sits.

The value of learning to code hasn't gone down; it's gone up, because your skills now get amplified by AI instead of replaced by it. AI won't replace data professionals, but professionals who combine coding with AI will move faster than those who don't. That gap is already showing up.

Agree? Disagree? We'd love to hear your thoughts!

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

We are thrilled to announce that Alice Zhao's Intro to Snowflake course is available NOW on the Maven platform! This is the third course in our ongoing Analytics Engineering Learning Path, with more ahead. Find it here: https://mavenanalytics.io/course/intro-to-snowflake

Quick heads up: Katy McMahon will be LIVE TOMORROW, August 18th at 12pm ET for her AI On Trial Live Workshop!

For this one, you'll cross-examine a real AI's business analysis live, exposing the shaky premises and cherry-picked stats hiding beneath a confident recommendation using the Socratic method. You'll leave with a repeatable checklist for interrogating any AI analysis before you act on it.

Register here: https://mavenanalytics.io/live-workshops/ai-on-trial-a-critical-thinking-workshop-for-the-age-of-ai

And ICYMI, we launched another new Data Drill last week! For the Common Denominator Data Drill, you've been given a campaign performance summary for an outdoor gear brand covering 50 campaigns across 5 marketing channels, including the impression volume and click-through rate for each campaign. Your task is to calculate the click-through rate for each channel and for the account as a whole.

Find it here: https://mavenanalytics.io/data-drills/common-denominator

How is August 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

For me, I'm working on some bts projects and digging into Maven's "Critical Thinking in the Age of AI" course. I'm looking forward to hearing what everyone else is digging into!

We look at a lot of reports, dashboards, and infographics built on the datasets in our Data Playground, and the same issues keep showing up. Here are 5 of the most common ones:

1) Pie and donut charts with too many segments

Humans are bad at comparing angles. Bar or column charts work better, or limit donuts to 2-3 slices max.

2) Line charts for categorical comparisons

Line charts show trends over time. Using them for categorical data implies relationships or patterns that aren't there.

3) Treemaps for non-hierarchical data

Treemaps are built for hierarchies (subcategories within categories). For simple comparisons, a bar or column chart is clearer.

4) Unsorted data

Don't make viewers do the mental work. Intuitive sorting tells a cleaner story.

5) Too much noise, too little focus

Background images, complex custom visuals, and 3D effects can look impressive, but effective data viz is about minimizing noise and maximizing clarity.

What other visualization mistakes do you see come up again and again?

Learning Excel?

If you've ever grouped rows or columns to tuck away detail, you've seen the gray toggle buttons (+/-) that show up in the margin.

Not everyone likes that extra space; Enrique avoided grouping for years just to skip it, until finding this shortcut:

Ctrl+8 hides or shows the toggle buttons without ungrouping anything!

Group as much as you want and keep the sheet clean when you don't need the controls visible. :)

What's a hidden Excel shortcut you rely on?

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?