TechNet Consultancy

24/03/2026

🔥 New blog published in Towards AI: The Data Model Mistake That Costs Companies $100K+ Per Year (and 90% of Power BI Developers Make It)

I've audited 47 Power BI implementations in the last 3 years. 42 had the same fundamental mistake: everything in one giant flat table.

One real client example: → 127 columns, 4.8M rows, 2.3 GB model → 45-second dashboard load times → 3 measures giving wrong numbers → Total annual cost: $106,500

The fix: A proper star schema. 3 weeks. Results: ✅ Model 83% smaller ✅ Reports 14x faster ✅ DAX 62% less code ✅ All errors eliminated

I share the exact cost calculation, the 3-week transformation, and a 30-minute audit checklist you can run on your own model.

Read the full article here (free access): https://medium.com/towards-artificial-intelligence/the-data-model-mistake-that-costs-companies-100k-per-year-and-90-of-power-bi-developers-make-2528827546ab?sk=248331df6a302c4d7a11684d40cee845

Thanks to AI for publishing! 🙌

17/03/2026

🔥 New blog published in Towards AI: I Built an AI Agent That Monitors Our Power BI Dashboards 24/7. It Caught a $180K Error at 3 AM.

The story: A client's dashboard showed $4.2M revenue. The actual number was $4.02M. Nobody caught it for 11 days.

The problem: Power BI's refresh said "success" — but 3 of 12 data sources had sent incomplete data.

My solution: A Python agent that runs 23 business logic validations every 30 minutes. Cross-source reconciliation. Data completeness checks. Statistical anomalies.

The results after 4 months: ✅ 11,520 monitoring cycles ✅ $340K+ in errors prevented ✅ 34-minute detection (was 3.2 days) ✅ Cost: just $38/month per client

I share the complete architecture, actual Python code, and the 5 rules every Power BI developer should implement 👇

🔗 https://medium.com/towards-artificial-intelligence/i-built-an-ai-agent-that-monitors-our-power-bi-dashboards-24-7-it-caught-a-180k-error-at-3-am-b32db378bd18?sk=8f4e213caf02166e7106fae632fe272c

04/02/2026

Monday morning. 6:23 AM. 📱
Text from my boss: "Revenue dashboard broken. CEO meeting in 2 hours. HELP."
I opened the Excel file: Q3_Revenue_Report_FINAL_v8_USE_THIS_ONE.xlsx
Macros failed. ! errors everywhere. Data from the wrong month.
Someone edited it over the weekend. Nobody knew who. 😱
This wasn't a one-time problem. This was our weekly nightmare.
We had 47 Excel files running a $50 million business: • Updated manually every Monday • Copy-pasted together • Breaking constantly
The sales team spent 15 HOURS every Monday just updating spreadsheets. 😤
That day, the CEO said: "Fix this. I don't care how."
Six months later:
✅ 47 files → 1 Power BI model ✅ 15 hours/week → 30 minutes/week ✅ 12 errors/month → ZERO errors ✅ $116,610 saved per year ✅ Everyone actually trusts the numbers now
But getting there wasn't easy. 🎢
People fought it. Systems broke. We almost gave up twice.
The hardest part? Getting people to let go of Excel.
Here's what we learned:
1️⃣ Don't try to migrate everything at once Start with 3 "Quick Win" files that are high impact but low complexity
2️⃣ Don't just copy Excel into Power BI Reimagine the workflow. Make it BETTER, not just different.
3️⃣ Run both systems side-by-side first Users need to see it working before they trust it
4️⃣ Training never ends New hires need training. New features need training. Keep teaching.
5️⃣ Excel won't die completely Some things belong in Excel. And that's okay!
The biggest lesson?
This wasn't about technology. It was about people.
The tool doesn't matter. The people matter.
I just wrote the complete story: → Week-by-week timeline → Every crisis we faced → What broke → What we'd do differently → Real ROI numbers
Link in comments! 👇
Have you ever dealt with Excel chaos at work? Share your horror story below! 😅

02/02/2026

SQL TIP: Stop using SELECT * 🛑
"It's faster to type!" you say.
Sure. 2 seconds faster to type. 8 seconds slower to execute. 200 times per day.
That's 26 minutes of wasted server time. Daily. From ONE query.
Real example:
Table with 47 columns App displays 5 of them
SELECT * → 3.5 MB transferred SELECT (5 columns) → 250 KB transferred
Result: 8.7s → 0.6s
The rule:
Never SELECT * in production.
Specify exactly which columns you need.
Your database (and users) will thank you! 🙏
Full SQL patterns guide https://sbee.link/wrmc7tudqf
Who else is guilty of SELECT * in production? 🙋

Real production data and the exact SQL mistakes that turn fast systems into 47-second queries. The query took 47 seconds to run.

30/01/2026

10,247 SQL queries analyzed. 73% had preventable performance problems. One pattern appeared in 34% of them.
Last year, I analyzed 12 months of production SQL query logs from a real database serving 5,000 users.
Not sample data. Real queries. Real performance problems.
The insight:
The slowest 50 queries (0.5% of total) consumed 41% of server resources.
And they ALL shared common patterns.
Here are the 7 patterns I found most often:
1️⃣ SELECT * everywhere (34% of slow queries) Transferring 14x more data than needed
2️⃣ Functions destroying indexes (28%) YEAR(OrderDate) forces 8.5M function calls
3️⃣ Type mismatches '12345' compared to INT column = 2M conversions
4️⃣ DISTINCT covering problems (19%) 2.4M rows retrieved, 2.22M thrown away
5️⃣ OR clause chaos Multiple scans + merge + deduplicate
6️⃣ Correlated subqueries 150K × 3 = 450,000 unnecessary operations
7️⃣ Missing filters Getting 8.5M rows to show 50
Real transformation:
Financial dashboard: 43 seconds to load
After 10-minute audit + 30 minutes fixes: → 2.1 seconds (95% faster!)
No expensive tools. No expert needed.
Just awareness of what patterns to avoid.
I wrote the complete framework with: • Audit scripts you can run • Real code examples • Before/after metrics • Priority matrix for fixes
Link ! 👇
https://sbee.link/8ckv4hbtfj
What's the slowest query you've ever seen?

Real production data and the exact SQL mistakes that turn fast systems into 47-second queries. The query took 47 seconds to run.

29/01/2026

Quick question for Power BI folks:
What's the WORST measure name you've ever found in a model? 😂
Mine: [DO_NOT_USE_OLD_BROKEN_MAYBE_v3_FINAL]
It was being used in 8 different visuals. 🤦
Reply with yours! Let's see who wins the "worst naming award" 🏆
(And if you want to avoid creating these disasters, my naming convention guide is here!) https://sbee.link/bh4ngedfvu

How we stopped wasting $93,600 per year searching for measures we’d already built

28/01/2026

The query took 47 seconds.
The developer who wrote it? Fired the next day. 😱
Not because the query was slow...
But because that 47-second delay cost the company a $2.4 million deal.
Here's what happened:
Sales demo. Live. 50 potential clients watching the screen.
Sales engineer clicks "Run Report."
5 seconds... 10 seconds... 20 seconds... awkward silence.
At 35 seconds, someone asks: "Is this normal?"
At 47 seconds, the data finally loads.
But the damage is done.
"If your system is THIS slow with demo data, what happens with our 50 million rows?"
They signed with a competitor the next week. $2.4M gone.
The developer? He didn't intentionally write bad SQL. He just didn't know what he didn't know.
Last year, I got access to something rare: 12 months of production SQL query logs. 10,247 queries. Every ex*****on time. Every performance metric.
I spent 3 weeks analyzing them, looking for patterns.
Here's what shocked me:
73% of queries taking over 5 seconds had at least ONE of seven specific patterns.
Not obscure problems. Simple patterns every developer should avoid.
The 7 Patterns That Kill Performance:
⚡ Pattern 1: SELECT * (found in 34% of slow queries) → One query: 8.7s → 0.6s just by selecting needed columns → 14x less data transferred
⚡ Pattern 2: Functions in WHERE clauses (28%) → WHERE YEAR(OrderDate) = 2024 (23 seconds) → WHERE OrderDate >= '2024-01-01' (0.4 seconds) → 57x faster!
⚡ Pattern 3: Type mismatches → WHERE ProductCode = '12345' (column is INT) → 11 seconds with implicit conversion → Remove quotes: 0.03 seconds (366x faster!)
⚡ Pattern 4: DISTINCT hiding problems (19%) → Usually covering up bad JOINs → 67s → 2.8s by fixing the root cause
⚡ Pattern 5: OR clauses → 18s → 1.8s by using UNION instead
⚡ Pattern 6: Correlated subqueries → 67s → 2.3s with JOIN + GROUP BY → 450,000 operations eliminated
⚡ Pattern 7: Weak WHERE clauses → Retrieving 8.5M rows to display 50 → 4m 18s → 0.8s with proper filters
Real example:
Healthcare company dashboard: 2-3 minute load times
10-minute audit found these patterns 30 minutes of fixes
Before: 28 minutes aggregate load time After: 47 seconds
36x faster. Zero hardware upgrades.
I just published the complete analysis with real code examples, before/after metrics, and an audit script you can run on your own database.
Link ! 👇
https://sbee.link/gfb8x4n3e6
Ever had a slow query cost you (or almost cost you) something important? Share your story below!

Real production data and the exact SQL mistakes that turn fast systems into 47-second queries. The query took 47 seconds to run.

25/01/2026

UPDATE: Your results are amazing! 🎉
After sharing the DAX Library Architecture, you all transformed your models:
"Consolidated 147 measures to 89. Found SO many duplicates!" - Marcus
"New analyst productive in 1 week instead of 4. She said it's the most organized model she's seen!" - Lisa
"CFO questioned a metric. Documentation answered it in 5 minutes!" - David
"Weekly reviews take 15 minutes. Deleted 34 deprecated measures!" - Sarah
The pattern I see:
✅ Start with ONE layer (folders, naming, or documentation) ✅ Implement it fully ✅ Add next layer when ready
You don't need to do everything at once!
Pick your biggest pain point: • Can't find measures? → Start with folders • Inconsistent names? → Start with naming conventions • Mystery measures? → Start with documentation
Then build from there.
If you haven't organized your measures yet, link is in comments.
Drop a 💯 if you've implemented any part!
What was your biggest win?

23/01/2026

POWER BI TIP: The 2-minute folder setup that saves hours 📁
Stop dumping all your measures in one flat list!
Here's the basic structure I use:
📁 _Base Measures (Your building blocks)
📁 Time Intelligence (All your LY, YTD, MTD measures)
📁 KPIs & Metrics (Your business metrics)
📁 Utilities (Helper measures)
That's it. 4 folders. Takes 2 minutes to set up.
But here's what it does:
✅ New team members understand structure immediately ✅ Finding measures goes from 20 min → 30 seconds ✅ Clear hierarchy shows what builds on what ✅ Reduces duplicate measures
Start simple. Add complexity only when needed.
Your future self will thank you! 🙏
Full architecture guide https://sbee.link/yvbq3j8uhr
Who else has their measures organized? Drop a 📁 below!

How we stopped wasting $93,600 per year searching for measures we’d already built

22/01/2026

Quick question for everyone using Power BI:
What's the WORST performance problem you've ever dealt with?
Mine: A dashboard that took 3 minutes and 47 seconds to load ONE visual 😱
Turned out I was using SUMX without variables, calculating the same thing 15,000 times.
One fix: 3m 47s → 4.2 seconds.
What's your horror story? And did you fix it or just... live with it?
(If you're still dealing with slow reports, my audit framework is in the comments!)
https://sbee.link/tu6ay8nc37

The diagnostic framework that helped me fix a 43-second dashboard in 30 minutes — and saved my job