# Marcel Dybalski

Marcel Dybalski shares practical ideas on data platforms, analytics, DataOps, and data mesh, with a focus on helping teams use data to make better business decisions.

- Profile: https://b2binfluencer.marketing/creators/marcel-dybalski/
- LinkedIn: https://www.linkedin.com/in/marcel-dybalski
- LinkedIn followers: 34,203
- Topics: data-quality
- Based on 14 dated posts, latest 2026-10-11

## Summary

Marcel Dybalski is most useful for practical data strategy talks on platforms, DataOps, data mesh, and metric limits.

- Best for: A data platform or analytics consultancy wanting a data strategy webinar for data teams.
- Not for: A streaming and ingestion vendor wanting product demos, benchmarks, short videos, and paid trial signups.
- Check first: Confirm audience job function, seniority, company size, geography, and engagement data before spending.

### Takeaways

- A data platform is more than the storage tool; it has seven layers.
- Data teams should build for decisions, not dashboard volume.
- Reliable data work depends on ownership and repeatable operations.

## Main ideas

- **A data platform is more than the storage tool**: Dybalski argues that choosing a warehouse or lakehouse is only one part of a data platform. The work also includes getting data in, processing and shaping it, making it usable, and supporting the experience where people act on it. Governance, monitoring, and cost management must support those layers too.
- **Data teams should build for decisions, not dashboard volume**: Dybalski criticises teams that respond to every reporting request without asking what decision the report will support. They favour reusable data products and judging a team by its influence on decisions rather than its ticket count.
- **Reliable data work depends on ownership and repeatable operations**: Dybalski presents DataOps as a continuous way of working, not a tool purchase: teams plan, build, test, deliver, monitor, and improve. Their data-mesh post extends the emphasis on ownership, proposing that domains manage their own data products while sharing a self-service platform and common governance.
- **A dashboard reflects its inputs; it does not explain every cause**: Dybalski argues that reported data problems often begin in the process that records the data, not in the dashboard. They also warn that a metric may omit context or chance, so analysts should avoid treating a measured pattern as a complete explanation.

## Work to read

- ["Your data platform is not just Databricks. It's 7 layers deep."](https://www.linkedin.com/posts/activity-7471814952916283393-Z7ws)
- ["I studied IBM's definition of DataOps so you don't have to."](https://www.linkedin.com/posts/activity-7443563789502861312-mXTN)
- [Data mesh explained through four pillars](https://www.linkedin.com/posts/activity-7476888373698387968-nFTU)
- ["You're not a data team. You're a dashboard factory."](https://www.linkedin.com/posts/activity-7422907696816807936-UQ2H)
- ["The dashboard is broken!" No, it reflects what you fed it](https://www.linkedin.com/posts/activity-7483048959565914113-yi8M)
- [Metrics, luck, and the Turkey Trap](https://www.linkedin.com/posts/activity-7475903861569007616-8zTC)
- [Why the variable that decides the outcome is often missing from the data](https://www.linkedin.com/posts/activity-7480954000306139136-6XTK)

## Partnerships

The eight LinkedIn posts, published between January and July 2026, contain no sponsored content or disclosed brand collaborations, and provide no sponsorship policy, media kit, rate card, or partnership inquiry route. They offer educational explainers and commentary on the seven layers of a data platform, DataOps, data mesh, and critiques of dashboards and metrics that treat data teams as report factories. Two posts point to an article in the comments, and most ask readers to follow the profile. These promote their own writing, not a documented commercial offer. Brands considering Marcel Dybalski for a webinar or thought-leadership partnership should ask directly about deliverable formats, disclosure practice, pricing, and turnaround.

## Alternatives

- Dhiraj Sharan: Pick Sharan when the room is focused on security operations rather than general analytics — SOC leads, security architects, detection engineering managers — or when the consultancy’s practice includes SIEM modernisation or security data lakes. The two posts advance one specific position: security data keeps outgrowing whatever repository holds it, so centralising everything in a SIEM is a losing move. The proposed fix is a federation layer that records each source’s location, schema and access APIs, allowing queries to run where the data already lives. That five-year thesis has a named end state, giving a webinar one contestable claim to build around rather than a tour of a whole stack. Sharan also asks people who have re-platformed a SIEM what drove their decisions, setting up a peer conversation a host can moderate.
- Arvind Murali: Pick Murali when the consultancy wants the webinar to focus on the consulting business model — how engagements get priced and staffed as AI absorbs delivery work — rather than a client’s data platform. The posts ask who should own an outcome-based contract, arguing that a firm that does not own the workflow has no business owning the outcome. They propose a hybrid commercial model: time-and-materials or fixed fee for discovery, fixed fee plus outcome-linked pricing for implementation, and shared-risk managed services in production. Murali also demonstrates a usable format: one post turns an interview with a Microsoft data-platform executive into four executive micro-masterclasses, closer to the shape of a webinar or podcast than Dybalski’s written frameworks.
- Christopher Gambill: Pick Gambill when the webinar is about a tool decision — whether to adopt a new data or AI framework — rather than a platform-building decision. The posts offer a ready-made test for the host: three kill switches before touching a bleeding-edge tool, covering existing business pain, governance and lineage in a central catalog, and five-year vendor viability. The second post counters AI-velocity claims with a concrete example: instant code generation shifts the burden onto senior staff. One team wrote 40 custom transformation pipelines in three days, then senior architects spent three weeks reviewing them and found ignored cluster sizing, heavy data skew and broken downstream tables. Gambill also references conference talks and a live session with a co-host, signalling experience presenting to an audience in the format a webinar requires.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/