Marcel Dybalski

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.

Dybalski's seven-layer model runs from source systems through ingestion, storage, processing, curation, and serving to business consumption. They warn that a team may have bought storage while basic processing and curation are still missing.

Read original

They favour reusable data products and judging a team by its influence on decisions rather than its ticket count.

Dybalski describes business logic spread across dozens of SQL queries and teams unsure which metric is authoritative. They propose asking what decision a dashboard enables before building it.

Read original

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.

Dybalski's DataOps self-check asks whether manual steps go undocumented, duplicate data copies accumulate, or fixes rely on one person remembering what to do. In the data-mesh model, a platform team supplies shared infrastructure while domains own their data products.

Read original · Original 2

They also warn that a metric may omit context or chance, so analysts should avoid treating a measured pattern as a complete explanation.

For a supposedly broken dashboard, Dybalski lists rushed CRM entries, missing account owners, duplicate accounts, and dates left as “TBD.” They also note that a CRM may track email opens and call duration while missing client-side politics or market conditions.

Read original · Original 2 · Original 3

Open this one first: it lays out the framework that other posts build on.

Dybalski argues that buying a warehouse or lakehouse solves nothing on its own.

• They outline seven layers, from source systems (ERP, CRM, legacy databases, SaaS) through ingestion, storage, processing and orchestration, curation, serving, and consumption in BI and AI models, plus cross-cutting pillars such as access control, lineage, pipeline cost management, and CI/CD for data.
• Specific images drive the argument: "just pull the data" turning into a three-week project, analysts rebuilding the same metrics 47 different ways, and governance deferred with "we'll add that later."
• Their closing claim: tools are 20 percent of the equation; strategy, architecture, and execution make up the other 80.

For brands planning a campaign

A webinar built around a layer-by-layer readiness assessment gives the audience an immediate diagnostic. The line "you've bought Layer 5 and Layers 1-4 don't exist yet" is a sharp hook for a platform-modernisation session aimed at leaders who treat tool selection as the whole project.

A good starting point for readers who have heard the term and want to understand the operating model.

Dybalski summarises IBM's definition rather than offering a personal one, tracing the concept to factory floors: one team owns the path from raw data to trusted answers, with repeatable steps and automated checks to catch problems before they reach reports.

• They frame the work as a loop (plan, build, test, deliver, monitor, improve) and offer three self-check questions: Are manual steps undocumented?
• Are duplicate copies of the same data in use?
• Do fixes depend on one person remembering what to do?
• Their conclusion: checked boxes signal an operational discipline gap, not a tools gap.

For brands planning a campaign

The three self-check questions work as a live poll or handout in a co-marketing webinar. They turn an abstract methodology into something attendees can answer about their own team in under a minute.

Dybalski frames data mesh as a shift from one central team and one giant warehouse, where every domain waits in line, to domain ownership, data as a product, a self-serve platform, and federated governance.

• They compare it to moving from one congested highway to a network of efficient local roads.
• The platform team still provides storage, compute, and catalog tools, letting domains stay autonomous without rebuilding infrastructure; governance is set globally but enforced locally and automatically.
• They end by asking readers who have already made the switch what surprised them.

For brands planning a campaign

Well suited to an audience weighing centralisation against domain ownership. The closing question is ready-made for audience participation, and the four pillars provide a slide structure the speaker can use straight away.

Useful for leaders who recognise the symptoms and want to understand the cause.

Dybalski describes a familiar pattern: the job title says Analytics, but the work becomes ticket after ticket; requests pile up faster than they can be built; business logic spreads across 47 different SQL queries; and no one agrees on the real metric.

• They propose three shifts: stop accepting every request and ask what decision a dashboard enables; stop building one-off solutions and create reusable data products; stop counting tickets closed and start counting decisions influenced.
• The goal, they write, is better decisions rather than more dashboards.

For brands planning a campaign

A strong team-design topic for a webinar aimed at analytics leaders. The three shifts create a concrete before-and-after structure, while "decisions influenced over tickets closed" is a memorable takeaway audiences can quote internally.

Useful for readers who field complaints that "the numbers are wrong."

Dybalski describes a near-daily pattern from a working analyst's perspective: a stakeholder panics about missing data, someone investigates, and the cause is usually a rushed deal creation, an empty account owner field, duplicate accounts from failed autocomplete, or closed dates still marked TBD.

• They give the frequency as "8.97 out of 10 times," a deliberately odd figure that drives home how rarely the roll-up is the problem.
• Their summary: dashboards do not lie; they reflect.
• They close by asking readers which crisis turned out to be a typo.

For brands planning a campaign

A useful data-quality segment for operational teams. The closing prompt invites audience stories that can be collected ahead of a session and used as live examples.

Read it for a look at how they make a contrarian case.

Dybalski writes that, as an analyst, the job was "basically to lie for a living," because people dislike randomness and invent stories to explain messy reality.

• They identify three failures: dashboards ignore context because a CRM tracks open rates, not whether a prospect was just berated by their boss; metrics take credit for luck when a revenue spike is attributed to strategy; and analysis falls for the Turkey Trap, where a thousand days of upward trend mean nothing the day the market shifts.
• They argue that analytics should not be used to build heroic narratives.

For brands planning a campaign

A contrarian opener for a session on measurement scepticism. The three failure modes—context, luck, and regime change—are distinct enough to anchor a segment on what a KPI dashboard cannot show.

A good entry point for readers interested in the ideas rather than the frameworks.

Dybalski recounts a parole study in which approval rates fell to nearly zero before a break.

• The decline was first attributed to hunger, then challenged as scheduling bias: harder cases had been pushed to the end of sessions.
• Their point is that both explanations reveal the same thing: the driver of the decision was missing from the official dataset.
• They apply the lesson to a lost deal: the team reviews email open rates, call duration and proposal timing, while the CRM records nothing about internal politics on the client side, random timing, emotional state or market noise.
• Analysts optimise what they can measure and ignore what’s real; bigger datasets don’t fix that.

For brands planning a campaign

A good fit for an opening or closing keynote segment: the example is memorable without being technical. It sets up the argument that instrumenting more of the process is not the same as capturing what actually drives outcomes.

Around those layers, they place the concerns that hold a platform together: access control, data classification, lineage, pipeline cost, CI/CD, and monitoring. They put tools at about 20% of the outcome, with strategy, architecture, and execution making up the other 80%, and say most modernisation projects stall in processing and orchestration.

A numbered, layer-by-layer breakdown followed by a separate list of cross-cutting concerns, framed by a contrarian claim about what leaders assume.

For brands planning a campaign

A ready-made framework for a webinar or workshop on platform architecture. The layer diagram gives the audience shared vocabulary, while the 20/80 framing opens a discussion about where teams stall—not which vendor they choose.

Examples: Source 1 · Source 2

They compare the shift to moving from one congested highway to a network of local roads. The stated goal is autonomous teams that generate value faster without losing governance, with global policy enforced locally and automatically rather than through command-and-control.

A before-and-after contrast, four numbered pillars, and a physical analogy.

For brands planning a campaign

Fits an audience weighing centralised against decentralised data ownership. The four pillars translate directly into a session agenda, while the highway-versus-local-roads analogy gives a non-specialist audience a concrete way in.

Examples: Source 1

They identify three building blocks: people aligned on shared goals, repeatable steps that run without manual fixes, and automated checks that catch problems before reports ship. They frame DataOps as a continuous loop of plan, build, test, deliver, monitor, and improve, then offer a self-check covering undocumented manual steps, duplicate copies of the same data, and fixes that depend on one person remembering what to do.

Summarises a third-party definition, then turns it into a six-step loop and a short diagnostic checklist.

For brands planning a campaign

A practical operations angle for data leaders. The self-check questions can be run live, prompting immediate, recognisable answers from the room.

Examples: Source 1

Drawing on their time as an analyst, they say most reported data issues traced back to CRM entry problems: rushed deal creation, empty account owner fields, duplicate accounts from failed autocomplete, and closed dates left as TBD.

• Their summary: dashboards do not lie; they reflect.
• The underlying rule is garbage in, garbage out.

Opens with a quoted stakeholder complaint, then walks through the diagnosis and recurring causes.

For brands planning a campaign

A grounded, unglamorous topic for revenue operations and analytics leads. It supports a session on data-entry discipline and process—often the actual bottleneck, rather than tooling spend.

Examples: Source 1

They reframe the team as a dashboard factory, not a data team. Their proposed shift starts with asking what decision a dashboard enables, building reusable data products instead of one-offs, and measuring decisions influenced rather than tickets closed.

Names a trap, lists its symptoms, then contrasts old and new behaviours in paired statements.

For brands planning a campaign

Speaks to how data leaders structure and measure their teams, making it a better fit for an analytics leadership audience than a tooling buyer. A useful co-marketing angle on team operating models.

Examples: Source 1

Hunger was the first explanation; scheduling bias was later proposed, with harder cases pushed to the end of sessions.

• They argue that both explanations show the same thing: the variable shaping the decision was absent from the official dataset.
• Applying this to business analysis, they note that a CRM tracks open rates and call duration but not internal client politics, timing, emotional state, or market noise.
• Bigger datasets, they argue, do not fix the problem.

Uses a well-known research anecdote, applies it to a familiar business scenario, then identifies the gap.

For brands planning a campaign

A discussion-friendly theme for analysts and decision makers, and a strong basis for thought leadership on the limits of attribution and what a measurement system can and cannot see.

Examples: Source 1

They list three failures: dashboards ignore context; analytics takes credit for luck when revenue spikes; and trend lines fall for the Turkey Trap, where a thousand days of upward movement say nothing about the day the market shifts. Using analytics to write heroic narratives is the wrong use of it.

First-person confession, followed by three named failure modes and a warning about narrative.

For brands planning a campaign

A provocative opener for a talk or article on decision quality and overconfidence in metrics. It pairs naturally with the argument about missing variables and gives a session a sharper edge.

Examples: Source 1

Where Dybalski lays out seven platform layers, Sharan traces the pattern through successive SIEM architectures and reaches a comparable conclusion: stop forcing everything into one repository and build a metadata layer that makes distributed data queryable in place. Both teach through numbered pillars and descriptions of the architectures they now propose.

Sharan focuses on security data rather than business analytics: SIEM, security data lakes, EDR and NDR feeds, and a vendor-specific Security Data Mesh built at Query. Their posts also draw on a personal account of 25 years in the field, while Dybalski covers platform layers, DataOps and data team design in general business terms.

Read original · Original 2

Both write in numbered frameworks for executives and argue that a tool or model is only as good as the process and oversight behind it. Murali’s conversation with a Microsoft data platforms executive covers context, governance and moving AI from pilot to production, close to Dybalski’s platform and DataOps themes.

Murali focuses on the commercial and delivery side — outcome-based contracts, process ownership and hybrid pricing for AI services — alongside interview-based content with enterprise executives. Dybalski writes about data platform architecture and day-to-day operations, not consulting business models.

Read original · Original 2

Gambill’s kill switches for new tools (business pain, governance and lineage, vendor viability) offer a governance test of the kind Dybalski describes in the cross-cutting layer of a platform. Gambill’s warning about AI-generated code swamping senior reviewers also echoes Dybalski’s point that tooling alone does not change outcomes.

Gambill’s recent posts focus on AI-assisted engineering: review bottlenecks, accountability for generated pipelines, compute cost spikes and pager duty. Dybalski covers a wider platform canvas, including data mesh, DataOps and data team structure, with less emphasis on AI code generation.

Read original · Original 2

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.

Choosing Sharan narrows the room.

• The posts focus entirely on security data — SIEM, EDR, NDR, security data lakes, bucket and tenant data at the edge — so a data platform or analytics consultancy would be teaching an audience it does not normally sell to.
• Analytics and BI buyers who match Dybalski’s posts would find little here.
• Sharan also names a company’s own version of the architecture, describing what “we call this architecture a Security Data Mesh” and promoting it with a diagram in the follow-up post.
• A consultancy selling judgement rather than a product would share a stage framed around one vendor’s approach, a different kind of neutrality from their tool-agnostic stance.

Examples: Source 1 · Source 2

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.

Murali’s material focuses on how consultancies price, staff and govern AI work, with data platforms, context and governance as supporting arguments.

• A data strategy webinar for platform and analytics buyers would get a commercial-model discussion instead of teachable architecture.
• Much of the material points back to one firm’s own offer: the hybrid model is described as what is being built at a named company.
• A partner session could therefore read as one consultancy pitching another’s delivery model rather than offering neutral thought leadership.
• The Microsoft interview post centres largely on its guest’s ideas, so it shows the format more than Murali’s own frameworks.

Examples: Source 1 · Source 2

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.

The reviewed Gambill material is narrower and more occasion-driven than Dybalski’s.

• Both posts centre on upcoming appearances — a conference talk and a Friday livestream — pairing argument with promotion.
• A consultancy would be buying into a personality-led event rather than a reusable teaching framework.
• The material also assumes a senior architecture audience: its claims target people who own pull-request queues and explain compute spikes to a CFO, excluding much of a general data leadership group.
• Dybalski covers the whole stack — platform layers, the DataOps loop, mesh ownership, decision quality — while Gambill explores one slice in depth: tool selection under AI pressure.

Examples: Source 1 · Source 2