The Power BI Whisperer | Power BI, Fabric Principal Consultant @Simple BI Inc

Let’s get to the point: you’ve got data problems, and I’m here to solve them. 
 
Imagine running an operation where every decision is backed by clear, actionable insights.  

No more guesswork.  
No more surprises.  

Just a seamless flow of information that drives your company forward. 

I'm David Giraldo, Principal Consultant of Data Analytics at Simple BI.  

In every corner of the manufacturing world, I’ve seen: 
 
• Unreliable demand forecasts 
• Constant stockouts that leave customers frustrated 
• Capacity constraints bottlenecking your production, eating into your profits  

Over the past few years, I've helped companies like Microsoft*, Westlake Chemical*, Waste Management*, MSA The Safety Co*, Sub-Zero Group*, Tricorbraun*, and Kroger Pharmacy* not just navigate these issues but turn them into strengths. 
 
Here’s how I solved them: 
 
1. We start with your data.  
 
Whether it’s buried in SAP, outdated systems or siloed in 1,000 Excel files across your organization, we dig in and bring it all together.   

From there, we design advanced analytics solutions using Azure Data Platform, Microsoft Fabric to give you a 360-degree view of your operations. 
  
2. We build clarity.  

We create reports in Power BI that deliver the exact insights you need to: 
 
• Optimize your supply chain 
• Predict demand more accurately 
• Manage your inventory in real time 
 
3. We drive action.  

We work with your team to automate processes and simplify operations so you can focus on what matters:  

Growing your business without the operational headaches. 
 
My goal is simple:  

I help manufacturing leaders turn data into decisions that don't just keep businesses running, but position them to thrive in an increasingly competitive market. 
 
Ready to see how your data can work as hard as you do? 

Let's chat. Send me a message here on LinkedIn or visit www.simplebi.net. 
  
*Through third-party

David Giraldo

Stop polishing Power BI charts.  

Own the plant’s decisions: 

Most teams spend 80% of their time building and “refining.”  

Then they wonder why nothing on the floor changes. 

The blocker isn’t a missing feature. It’s where you spend your hours. 

My time split when I build manufacturing reporting that actually moves numbers: 

👉 60% with the people who decide and the routines they use 

👉 30% on definitions and data quality that won’t embarrass us 

👉 10% building only what the decision needs  

What that looks like on the floor:  

I sit in the 7:05 production huddle.  

We list the 3 decisions they make under pressure:  

↪ Which line runs which SKUs
↪ Whether to pull a maintenance job forward 
↪ Whether to stop or run on a quality blip 

Those become decision pages, not “dashboards.” 

The first screen survives the 15-second glance: 

👉 Top: today vs plan on the constraint 

👉 Middle: biggest loss right now 

👉 Bottom: where to look first and who owns it Two clicks to the events. No exports needed. 

Build rules I live by: 

• No page without a meeting that will use it weekly. 

• If “Other” leads your Pareto, fix codes before visuals. 

• Every visual needs a verb. Decide, escalate, schedule, or investigate. 

• Events within two clicks or it’s decoration. 

• If it won’t change a schedule, a work order, or a setpoint this week, it doesn’t ship. 

Want dashboards that drive decisions, not just decorate data?  

Drop your current time split: building vs context vs recommendation in a DM.

Saved over $500k for clients with 25+ reporting and data analytics solutions | Azure, Power Platform & Fabric Consultant

The only KPI that matters in manufacturing at 7 am:  

Time to decision.  

Standup is short and noisy.   

Thirty seconds per line, radios in the background, interruptions guaranteed.   

A typical “overview page” slows the room because it mixes status, history, and explanations.   

People stall, scroll, and argue. 

To fix it, we build “decision pages.” 

It’s one screen built for a single call a supervisor must make in under 60 seconds, using data they already trust. 

Example: the line loading page.  

Top shows today versus target (by hour and shift) green or yellow or red so there’s no debate.  

Middle lists the losses that moved money in the last 24 hours: downtime on the constraint, changeover time, scrap by SKU.  

Bottom shows next actions with owners and times.  

The supervisor can decide whether to hold, switch SKU, or add overtime without hovering or clicking.  

If they have to scroll, the huddle stalls and production waits. 

Ban hover-only details, clever navigation, and buried drill paths.  

Those belong in deep-dive reviews, not in standup where the constraint cools while people hunt. 

This is how Power BI starts driving behavior: faster calls, fewer “whose number is right” debates, and a team that moves before the bottleneck costs you another hour.

Most plant pages look great at a desk. Then the shift standup hits and they collapse under 15 seconds of reality. 

I build for the routine first.  

Here’s the huddle test every page must pass: 

👉 Answer the only three things a supervisor needs in 15 seconds: today vs plan on the constraint, biggest loss right now, next action with an owner. 

👉 Two clicks from “why is red red?” to the event. No exports. No scavenger hunt. 

👉 Refresh to the decision cadence. If the call is daily, daily is fine. Streaming is for the few decisions that truly need it. 

👉 No meeting, no page. If a page isn’t used in a named ritual — 7:05 production, shift handoff, maintenance planning, weekly review — it’s wallpaper.  

Guardrails that keep it honest:  

• Every visual gets a verb: decide, escalate, schedule, investigate.  

• If “Other” leads your downtime Pareto, fix codes before visuals.  

• Finance signs the math so ops stops fighting the number and starts fixing the loss. 

Dashboards don’t change plants. Routines do. Build for the moment the decision is made, or don’t ship it.

Saved over $500k for clients with 25+ reporting and data analytics solutions | Principal Fabric Analytics Consultant

Most dashboards optimize refresh time. The good ones optimize decision time: 

Picture a steering meeting.   

Seven people stare at a dashboard while someone hunts the number. That’s 45 seconds of drift before the conversation even starts.  

You can measure that drift.   

Use a Decision-Ability Index:   

DAI = Time-to-Answer (seconds) + Ambiguity Count.   

Lower is better. 

Run a 10-minute test with one real user. Ask only: 

• Are we on or off target right now? 
• What changed since last period? 
• Where would you act first? 

Count ambiguity events precisely: unit mismatch, filter uncertainty, undefined term, hover-only info, conflicting totals, unclear timeframe. 

Use this scorecard in your next meeting ⬇️

I have 13 years of experience in data. This is the lesson I’m coming back to almost every day: 
 
Operate BI like a product you run, not a deliverable you ship. 

The tool stack changes. The teams change.  

 What holds is an operating model that makes trust repeatable. 

 What that looks like in practice: 

 👉 One owner per dashboard, model, and KPI. Visible in the header. No owner, no prod. 

👉 A KPI contract next to the number: definition, inclusions, exclusions, effective date, change log. 

👉 SLOs you can measure: refresh by 7:30 AM local, time-to-detect < 5 minutes, time-to-recover < 60 minutes. 

👉 Release notes and a 24-hour rollback plan for any metric change. No silent edits. 

👉 A “What changed since last period” strip up top. Plain language, not tooltip archaeology. 

👉 Deprecation windows. Anything “temporary” gets an expiry date in 90 days. 

👉 Cost guardrails: every Fabric workload tagged to an owner, off-hours off by default. 

👉 Git-backed promotion: PR checks for schema diffs, performance gates, and RLS tests before prod. 

I use your platform to enforce it: 

• Fabric Monitoring hub for incident detection and SLO tracking. 

• OneLake shortcuts with a published schema contract, never unmanaged mirrors. 

• A single thin semantic model per domain. No shadow models. No report-level business logic. 

• Warehouse or Lakehouse as the query home, not both. Compatibility views only during migration. 

This is all about discipline.  

The result is fewer surprises, faster fixes, and numbers people trust without a meeting to interpret them. 

If you do one thing this week: add an Owner, Last Changed, and Next Review badge to your top report. Then publish the KPI contract.  

Everything else gets easier after that.

Power BI is not a CRM. 

A common pattern I see:  

A sales dashboard adds a “Change stage” dropdown and a notes box. That single tweak creates double entry, four permission paths, and a week of reconciliation after the first conflict. 

Dashboards are for answers. Systems of record are for actions. 

Execs won’t type in a report. They scan and delegate. Design for clicks out, not data entry in. 

Draw a hard line: 

👉 Read-only in BI: KPIs, trends, flags. 

👉 Two-click handoff: buttons that open the right CRM or ERP form, with context prefilled. 

👉 Closed loop: the operational system writes back event metadata (who, what, when, status). Your dashboard shows progress, not edits.  

Only exception: approvals live in the system of record with a visible audit trail. Never inside the report. 

Use this kill switch before any writeback: 

• Who owns the field and table? 
• Where is the audit log, and who reviews it? 
• How do we roll back and notify users? 

If any answer is fuzzy, the answer is no. Build the handoff. 

PS. Where are your write-backs sneaking in?

Your Warehouse and Lakehouse disagree.  

And users notice: 

Two homes per domain means two refresh schedules, two security models, two lineage graphs, two caches to warm, and four places to debug when numbers drift. 

Pick a query home per domain. Call it query gravity. Everything else connects to it. 

Here’s how to decide in 60 seconds:

There’s a moment in every data budget review when the room goes quiet: 

“Who owns this cost?” 

Eyes drop to laptops. Someone mentions “shared capacity.” Another blames “automation.” Nobody raises a hand.  

That silence is the real bill.  

Not the dollars – the hit to trust.  

If you can’t say who owns it, you can’t say why it exists.  

And if you can’t say why, you can’t ask for more. 

Leaderless spend feels like drift. Pipelines keep running because nobody wants to be the person who pauses them. Warehouses hum overnight because it’s safer to waste money than risk being blamed for a failure.  

The fear is rational. The outcome is not. 

So I make three commitments visible and non‑negotiable: 

👉 One name on every workload. If it has no owner, it stops. Not forever — until someone is willing to own it. 

👉 After hours, lights go off. If you need them on, say why and for how long. Put your name on that too. 

👉 If spend doubles in a day, a human gets pinged and something stops immediately. Justify it, or it stays off. 

People worry this will slow them down. It does the opposite. When ownership is clear, decisions are fast. When off hours are expected, exceptions mean something.  

When spikes trigger action, you don’t spend your week explaining a bill you didn’t cause. 

Cost isn’t a finance problem. It’s an accountability problem that shows up as dollars. 

If your spend doubled tonight, who gets the alert – and what stops first? 

Post 5 

“Looks fine to me” isn’t an operating model. 

You know the drill:  

The app is green, the meeting starts, someone asks why yesterday’s sales didn’t land. 

Health checks and vibes don’t answer that question. SLOs do. 

Run Fabric with three SLO you can measure: 

👉 Pipeline success rate 

👉 Median time‑to‑publish 

👉 Time‑to‑detect 

Here’s how to do it right:

SLOs for Fabric (Full Guide)

If you release dashboards on Fridays, you’re training the business not to trust you: 

Friday pushes create weekend fire drills and Monday rollbacks. The numbers change, nobody’s on, and trust takes the hit. 

Trust isn’t a facelift. It’s a release habit. 

Here’s the policy I set on client projects: 

1️⃣ Freeze windows:  

No releases during financial close, payroll, board weeks, or big campaigns. Publish the freeze calendar in the BI app. 

2️⃣ Fixed cadence:  

Two release windows a week, e.g., Tue 10:00 and Thu 14:00. Emergency fixes only outside that. 

3️⃣ Preflight checks:  

Last refresh succeeded, role-level security tested with real users, top 5 KPIs within tolerance vs last release, page load times measured. 

4️⃣ One-line changelog on-page:  

“Jan 3 — Updated Fiscal YTD to exclude credits; renamed ‘Net Sales’ to ‘Net Revenue’.” 

5️⃣ Canary first:  

Release to a pilot audience for 24 hours. If no alerts or support spikes, promote to everyone. 

6️⃣ Rollback in 90 seconds:  

Keep the last version ready. Know exactly how you’ll revert and notify. 

7️⃣ Post-release watch: 

Monitor refresh duration, errors, and support queue for 48 hours. If noise jumps, investigate before shipping again. 

This does three things: 

• Reduces surprise. Nothing “just changes” before a Monday meeting. 

• Lowers risk. You ship smaller, testable increments. 

• Builds credibility. Stakeholders see discipline, not drama. 

If you want adoption, stop chasing the Friday dopamine hit.  

Ship on a cadence the business can rely on.

Stop shipping dashboards on Fridays

Data Analytics - Turning Coffee into Insights, One Caffeine-Fueled Query at a Time! | Healthcare Data | Financial Expert | Driving Business Growth | Data Science Consultant | Data Strategy

As a data analytics professional, I have a passion for turning raw data into meaningful insights that drive business decisions. With my experience in data visualization, statistical analysis, and data modeling, I can help your organization harness the power of data to optimize operations, improve customer experiences, and achieve your strategic goals.

🔍 Discovering Insights

My expertise lies in exploring complex data sets, uncovering hidden trends and patterns, and providing actionable insights that support informed decision-making. I can help you answer questions like "What are our customer's buying habits?" or "What factors are impacting our sales performance?", and use data to guide your business strategy.

🎨 Data Storytelling

I believe that data storytelling is an art form, and I love using creative and engaging data visualizations to communicate complex information in a way that's easy to understand. Whether it's through interactive dashboards or custom reports, I can help you see your data in a new light, and use it to tell the story of your business.

🧠 Continuous Learning

I'm a lifelong learner, and I'm always exploring new data tools and techniques to stay at the forefront of the industry. Whether it's machine learning, artificial intelligence, or predictive analytics, I'm excited to apply the latest advancements to help you gain a competitive edge.

🤝 Let's Connect

If you're looking for a data analytics professional who can help you unlock the power of your data, let's connect! Whether you're a startup or a large corporation, I'm excited to collaborate with you and help you achieve your business goals. Feel free to reach out at Ravenaravs@gmail.com

Ravena O

Still confused between LLM, RAG, AI Agents, and MCP?
You’re not alone — most teams mix these up and design the wrong systems.

This visual breaks it down clearly ⬇️
Here’s the mental model that actually works:

🟡 LLM (Large Language Model)
• Pure reasoning + text generation
• No external knowledge beyond training
• Best for summarisation, Q&A, ideation

🟡 RAG (Retrieval-Augmented Generation)
• LLM + external context (docs, DBs, PDFs)
• Reduces hallucinations
• Ideal for enterprise knowledge, internal search

🟡 AI Agents
• LLM + planning + memory + tools
• Can take actions, not just respond
• Used for workflows, automation, copilots

🟡 MCP (Model Context Protocol)
• Standard way for models to talk to tools & data
• One unified interface → many systems (GitHub, Slack, files)
• Critical for scalable, tool-driven agent ecosystems

👉 Think of it like this:
• LLM = Brain
• RAG = Brain + Books
• Agent = Brain + Hands
• MCP = Nervous System connecting everything

Why this matters

• Most AI failures are architecture mistakes, not model issues
• Agents without MCP don’t scale
• RAG without governance breaks fast

Learn more (high-quality resources):

• RAG explained: https://lnkd.in/dUfV33DE
• AI Agents overview: https://lnkd.in/dE9A5G29
• MCP (Model Context Protocol): https://lnkd.in/dNMmQnSf
• Agent design patterns: https://lnkd.in/df4Yeuug

💡 If you’re building GenAI systems in 2026, this stack isn’t optional — it’s foundational.

What are you building right now: RAG, Agents, or full MCP-based systems? 👇

Ravena  O

Small Language Models are the future of AI Agents
—but only if you know how to use them correctly.

Gartner predicts that by 2027, organizations will use small, task-specific AI models 3× more than general-purpose LLMs.

NVIDIA confirms it clearly:
“Small Language Models are the future of AI Agents.”

So what actually makes SLMs so powerful for agentic systems?

I put together a simple handbook to make this easy to understand.

Here’s a quick breakdown.


What are SLMs?

Transformer-based neural networks with fewer parameters (millions to low billions) that prioritize efficiency over broad generalization.

They deliver:
🟠 Faster inference
🟠 Lower memory usage
🟠 Easier deployment in production

🟠 How are SLMs created?

Using three core techniques:

Quantization – Reduces bit precision (32-bit → 8-bit) while preserving accuracy
🟠 Pruning – Removes less important parameters to shrink model size
🟠 Distillation – Transfers knowledge from large “teacher” models to compact “student” models


🟠 4 strategies to maximize SLMs in AI agents

🟠 Intelligent Routing – Simple tasks go to SLMs, complex ones to LLMs
🟠 Pipeline Collaboration – SLMs preprocess, LLMs refine outputs
🟠 Parallel Verification – SLMs draft quickly while LLMs verify
🟠 Conditional Activation – Escalate to LLMs only when confidence is low


🟠 Real-world implementations

🟠 Uber – SLMs handle query preprocessing and answer validation in Agentic RAG
🟠 OpenAI – SLMs power guardrails, relevance checks, and intent classification
🟠 Microsoft – SLMs improve cloud supply chain fulfillment accuracy

🟠 Why enterprises are adopting SLMs

🟠 Privacy-first – On-prem deployment for healthcare triage and legal reviews
🟠 Cost-efficient – 50,000+ receipts/day processed at ~1% of LLM cost
🟠 Hyper-specialized – Fine-tuned models for internal coding and support routing

🟠 Case studies

🟠 Uber: https://lnkd.in/geNJNhvK
🟠 OpenAI: https://lnkd.in/gD5-v58d
🟠 Microsoft: https://lnkd.in/gZ5hGuNy

If this post helped you rethink Agentic AI,
🟠 Like
🟠 Share
🟠 Or comment how you’re using SLMs in production

CC: Rakesh Goel

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