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ESG reporting isn't disappearing, it's maturing, and most companies don't need another carbon calculator. They need better data.Marcin Kubica sits down with ESG expert Wojtek Pastyrczyk to show Vega, our emissions reporting tool built natively on Microsoft 365. See a live demo of scope 1 and 2 data entry, audit trails, and Power BI dashboards, and hear how AI-powered invoice processing can automatically turn a PDF utility bill into GHG Protocol-aligned CO2 data.

Have a data, analytics, automation or AI challenge you'd like us to dig into? Get in touch, we'd love to hear about it.Talk to Wojtek about Vega and ESG reporting: https://www.linkedin.com/in/wojciech-pastyrczyk/Talk to Marcin about your business challenge:https://www.linkedin.com/in/marcinkubicaquality/Follow Clouds on Mars on LinkedIn: https://www.linkedin.com/company/clouds-on-mars/?viewAsMember=trueSubscribe for more episodes of Clouds on Mars Products#ESGReporting #SustainabilityData

After eight back-to-back meetings, a project manager still needs to write up notes from all of them. Then update JIRA, draft follow-up emails, and check what action items are still pending. The admin is the job - not the project.

This episode covers PM Manager - an AI assistant built specifically for project managers, designed to handle 60-70% of the administrative work that currently consumes most of their day.

Marcin Kubica and Aleksandra Gepert, a project manager with 14 years of experience, discuss what makes the PM role genuinely hard - and why generic AI tools like Copilot or ChatGPT don't solve it. The assistant is context-aware, plugged into the project manager's actual environment, and learns how each person works over time.

You'll take away:

• Why general-purpose AI tools fall short for project managers: they lack project context and require manual effort to use
• What four core integrations (calendar, documentation, email, task management) look like when filtered by project context
• How auto-generated meeting minutes, gap detection, and inconsistency flagging work in practice
• Why the assistant acts as a coach for less experienced PMs and an accelerator for seasoned ones
• What it means for organizations: visibility into project health and into who their strongest players are

Key topics covered:

• The admin burden on project managers: why context switching across multiple projects creates constant overhead
• How PM Manager auto-generates meeting minutes and flags action items, gaps, and inconsistencies from transcripts
• Automatic task creation in JIRA based on what was discussed - not what someone remembered to log
• The difference between a broad AI copilot and an assistant that knows your project, your role, and your permissions
• What "learning to work with AI" means in practice: how your communication style shapes the quality of outputs

Project managers are not administrators. The admin work expanded because there was no better option - someone had to close the loop. PM Manager doesn't make project management easier by simplifying the work. It removes the parts that shouldn't have been the PM's job in the first place, so they can focus on what actually moves a project: relationships, decisions, and unblocking teams.

Watch full episode: https://www.youtube.com/watch?v=jI46Dq211DI&list=PL053kgqQv_fspiVbwCStY8rTcjldz4DtO&index=6 Website: https://cloudsonmars.com Follow the host: https://www.linkedin.com/in/marcinkubicaquality/

One of Clouds on Mars's clients had over 5,000 business rules to validate incoming data. Bad data kept coming in anyway. The rules were checking format. Nobody was checking context.

This episode covers GODI - Governed Operational Data Improvement - a framework built to catch data errors that pass technical validation but are wrong in the context of how a business actually operates.

Marcin Kubica and Aleksandra Gepert walk through a real case: a procurement team entering a 300-day lead time for a component that always arrives in 14 days. The system passed it. Planning triggered emergency orders. Production was at risk. GODI is built to catch exactly this kind of error - early, before it cascades.

You'll take away:

• Why structural validation rules are never complete - and why they become outdated as business changes
• How GODI's four layers work: from deterministic business rules, through historical context, to category comparison and closed feedback loop
• What three levels of user response (accept, correct, escalate) feed back into the ML model and make it sharper over time
• Why organizational readiness matters more than technology in an implementation like this
• What 90% recommendation accuracy and 3% false positive rate looks like in practice

Key topics covered:

• The difference between format-correct data and contextually correct data
• How a single data entry error (300 days vs. 14 days lead time) can trigger emergency shipments and production holds
• GODI's four-layer detection model: business rules, historical context, category comparison, learning loop
• Why escalation cases - where the operator isn't sure - are the strongest learning signal for the model
• Post-mortem culture vs. built-in learning: why lessons from incidents rarely make it back into the system

Data quality tools tend to focus on rules. GODI focuses on judgment. The difference is that rules catch what someone anticipated in advance. GODI catches what didn't fit the pattern - even when no rule was written for it. Over time, with human feedback at every step, the system gets better at distinguishing what's genuinely wrong from what just looks unusual. That's a different kind of reliability than any static rule set can provide.

Watch full episode:https://www.youtube.com/watch?v=NZbJr-jTK-o&list=PL053kgqQv_fspiVbwCStY8rTcjldz4DtO&index=5 Website: https://cloudsonmars.com Follow the host: https://www.linkedin.com/in/marcinkubicaquality/

A quality controller at a large automotive company spends an entire day reconstructing what happened in the previous shift. The data exists - it's just scattered across machines, MES systems, and spreadsheets. By the time the picture is clear, the window to prevent the next problem has already closed.

This episode covers ORBIT - Operational Real-Time Business Intelligence and Troubleshooting - a solution built by Clouds on Mars for manufacturing environments where downtime and quality issues carry significant financial consequences.

Marcin Kubica and Patrycja Moc walk through the operational realities of factory floor monitoring: why fragmented data leads to late detection, what Statistical Process Control actually means in practice, and how real-time streaming combined with AI chat changes the speed of response.

You'll take away:

• Why the cost of downtime in manufacturing ranges from thousands to millions per hour - and what that implies for monitoring investment
• How SPC (Statistical Process Control) rules detect process drift before it causes a failure
• What real-time data streaming via Microsoft Fabric enables that batch reporting cannot
• How an integrated AI chat helps operators and team leads diagnose issues without waiting for a specialist
• Why ORBIT doesn't require replacing existing systems - it connects to what's already there

Key topics covered:

• Fortune 500 companies lose $1.4 trillion per year to downtime (Siemens 2024 report): the cost context for real-time monitoring
• Western Electric Rules and SPC: early warning logic that flags process drift before limits are crossed
• Real-time dashboards vs. next-day reports: what changes when operators see data as it happens
• AI chat on the plant floor: querying machine documentation and past root causes to speed up diagnostics
• Proof of concept approach: how to start with a narrow process and scale

The most damaging production problems don't start as emergencies - they start as trends that no one caught in time. ORBIT is built around that reality: it doesn't just visualize what happened, it applies statistical rules to detect where a process is heading before it fails. For plants that still rely on manual analysis and experience-based judgment, this is the difference between reacting in minutes and reconstructing events the next morning.

Watch full episode: https://www.youtube.com/watch?v=C2OejqwU5AY&list=PL053kgqQv_fspiVbwCStY8rTcjldz4DtO&index=4 Website: https://cloudsonmars.com Follow the host: https://www.linkedin.com/in/marcinkubicaquality/

Most BI dashboards tell you what happened last week. PULSE tells you what's happening now, and what you should do about it before the cost hits. That's not a feature upgrade. That's a different category of tool.

This episode covers PULSE, Clouds on Mars's answer to a question most analytics teams haven't fully articulated yet: what comes after insight?

Marcin Kubica and Estera Kot walk through the analytics maturity curve - from descriptive, through diagnostic and predictive, to prescriptive - and explain why the final stage (autonomous action) has been technically possible for a while, but organizationally out of reach for most companies.

You'll take away:

• Why the gap between "data insight" and "business action" is where most value is lost
• What the three real blockers to prescriptive analytics are (process maturity, data chaos, human resistance)
• How PULSE moves from alert → root cause → action plan → execution in a single flow
• Why the system blends Gen AI as an interface with ML-based anomaly detection under the hood
• How the closed-loop feedback model makes recommendations more precise over time

Key topics covered:

• The analytics maturity model: why most companies are still stuck at descriptive
• Why a BI dashboard is like a book you don't have time to read
• PULSE demo: competitor price cut → alert → action options → execution in three clicks
• Action catalog: how companies standardize responses to recurring business situations across geographies
• The difference between a copilot that answers questions and one that actually executes decisions

The technology for autonomous prescriptive analytics has been ready for some time. What's missing in most organizations is not the AI, but a well-defined business process to map it onto, and clean enough data to feed it. PULSE doesn't replace the analyst. It removes the hours spent getting to the point where a decision can be made.

🌐 Website: https://cloudsonmars.com 🔗 Follow the host: https://www.linkedin.com/in/marcinkubicaquality/

#cloudsonmars #AI #BI

A patient visits 3.2 specialists on average, and zero of them share records. That's not just a paperwork problem. In one hospital, it translated into 34% of MRI scans being repeated for no medical reason, and a small fortune walking out of the building every month in the form of expired medications. 

🧭 What this episode is about 
Marcin Kubica and Estera Kot, CTO of Clouds on Mars, take apart the cost structure of a modern hospital - roughly 55–60% medical staff, 15–30% pharmaceuticals and supplies, the rest equipment, diagnostics and admin, and show where data and AI actually move the needle. They walk through MedHub, the Clouds on Mars solution that connects cardiology, oncology, radiology, neurology and emergency around one patient timeline, surfaces redundant tests, and opens controlled access to smaller partner clinics. The conversation is aimed at hospital directors, CFOs, CIOs and clinical leaders evaluating digital transformation of patient care. 

🔍 Key topics 

• where the real cost lives in a hospital, and where AI shifts the economics 

• redundant tests as a measurable line item, not a vague problem 

• supply-chain optimization for pharmaceuticals: stock levels, expiration dates, cold chain 

• whether a hospital actually needs a second MRI, or just better scheduling and a better radiologist workflow 

• a single patient timeline across departments, and what changes when doctors finally trust the data they see 

• federated access for partner clinics and external doctors - with patient consent, privacy and encryption built in 

• anonymized hospital data as a new asset for researchers and pharma, and a potential revenue line 

💡 Insight 
The healthcare cost problem is rarely about people working too slowly. It's about the same patient being diagnosed three times because three systems don't talk. Connect the data, give clinicians one view, and the savings show up before any new equipment is bought. 

▶️ Listen / watch / connect 
▶️ Full episode on YouTube: https://www.youtube.com/watch?v=jHLljXCapLc&list=PL053kgqQv_fspiVbwCStY8rTcjldz4DtO&index=3 
🌐 Clouds on Mars: [link] 
📱 Estera Kot on LinkedIn: https://www.linkedin.com/in/esterakot/Marcin Kubica on LinkedIn: https://www.linkedin.com/in/marcinkubicaquality/ 

#HealthcareAI #DigitalHealth #DataPlatform #HospitalEfficiency #PatientData #CloudsOnMars 

"The cloud" sounds like something that lives in the sky. It doesn't. It lives in specific buildings, in specific countries, in a very specific political reality, and earlier this March, two of those buildings stopped working at the same time. 

What this episode is about? 

Marcin Kubica and Estera Kot, CTO of Clouds on Mars, talk about what the recent strike on AWS data centers in the UAE and Bahrain actually means for businesses that run their operations in the cloud. For a few days, banks couldn't process transactions, payment apps stopped, dashboards went blank, finance teams couldn't close their books. This conversation is for CEOs, CFOs and COOs who never wanted to think about availability zones, and now have to. Estera explains, in plain language, what resilience really means, where the hidden risks sit in a typical cloud setup, and which two questions to walk into your CTO's office with on Monday morning. 

Key topics 

• why "we're in the cloud" is no longer a complete answer, and what changed in March 2026 

• the difference between an availability zone, a paired region, and an actual disaster recovery plan 

• how a single-region setup quietly becomes a single point of failure for revenue, payments and reporting 

• classifying workloads into tiers so the recovery plan reflects business priorities, not technical preferences 

• why manual failover at 3 a.m. is the wrong answer, and what infrastructure-as-code and Azure Chaos Studio change about that the supply-chain reality after a physical incident: hardware, shipping, airspace, weeks not hours

  Insight

  The lesson from March 2026 isn't "the cloud is unsafe." It's that resilience was always a configuration choice, and most organizations made it once, years ago, without revisiting it. The cost of fixing that is small. The cost of finding out you didn't is the kind of week nobody wants to repeat. 

 Listen / watch / connect 

▶️ Full episode on YouTube: https://www.youtube.com/watch?v=q9Bc-wqqs_A&list=PL053kgqQv_fspiVbwCStY8rTcjldz4DtO&index=2 

🌐 Clouds on Mars: https://cloudsonmars.com/ 

📱 Estera Kot on LinkedIn: https://www.linkedin.com/in/esterakot/

Marcin Kubica on LinkedIn: https://www.linkedin.com/in/marcinkubicaquality/

#CloudResilience #DisasterRecovery #BusinessContinuity #Azure #DataPlatform #CIO #CloudsOnMars 

Your organization pays more every year for a reporting tool that's been around forever, and the team still can't agree which report to base the decision on. Replacing the stack usually sounds like a year-long project and a six-figure invoice. This episode is about why that calculation just changed. 
Marcin Kubica talks with Estera Kot, CTO of Clouds on Mars, about what actually drives Tableau → Power BI migrations in 2026, and how automation rewrites the economics of projects that used to mean hundreds of manual hours.
 
They walk through LOOM -  the Clouds on Mars product built specifically for this scenario, and show the full flow live: upload a Tableau workbook, get a complexity analysis, generate a Power BI project (PBIX + semantic model in TMDL), and optionally deploy data straight into a Fabric Lakehouse. 
What you'll get from this conversation: 

• when manual migration still makes sense and where the automation threshold really sits (spoiler: it's lower than most teams think) 

• how LOOM combines deterministic heuristics with an optional AI layer and what that means for data privacy, on-prem deployments and Azure OpenAI / Anthropic / local Ollama models 

• why "lift and shift" is rarely the right end state, and where consultants still add real value after the conversion 

• how unification of the BI estate around Power BI and Fabric unlocks governance, reuse and faster decisions 

A practical conversation for CIOs, CDOs, data platform leads and BI architects evaluating Tableau exit strategies or planning a broader analytics consolidation. 
▶️ Watch the full episode on YouTube: https://www.youtube.com/watch?v=l8OqMaV0ZiQ  
💬 Connect with Estera & Marcin on LinkedIn - questions and use cases welcome. 
Estera: https://www.linkedin.com/in/esterakot/
Marcin: https://www.linkedin.com/in/marcinkubicaquality/
#PowerBI #Tableau #MicrosoftFabric #DataAnalytics #BImigration #AI #CloudsOnMars