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Introduction

Databakery is at the crossroads of several potential needs and use cases: its ease of use, sleek UI, and extensibility make Databakery interesting in many cases beyond pure tech players.

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Usecase details

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Functional Data & Domain Cartography

Define a business domain map (Sales, Marketing, Finance, Claims, Pricing, etc.) with ownership, key KPIs, and core systems. Maintain a functional catalog of business objects (Customer, Contract, Claim, Campaign, Product, Policy, Store…) with attributes, business rules, and linked quality rules. Map roles and responsibilities (Owner, Steward, Custodian) per domain/object, with RACI and governance workflows. Capture data contracts and expectations at domain boundaries (SLAs, freshness, quality thresholds, security constraints).

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Shared Vocabulary

Build a business glossary of terms, definitions, calculation rules, examples, and links to dashboards/metrics. Manage synonyms, homonyms, and translations (e.g. Customer vs. Client vs. Account Holder) to reduce ambiguity. Connect business terms to technical assets (tables, columns, views, APIs) and consuming assets (reports, KPIs, ML features). Govern changes to definitions through proposals, reviews, approvals, and version history.

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Business Data Lineage

Visualize business-level lineage (e.g. Prospect → Campaign Audience → Campaign → Lead → Opportunity → Sale) across systems and processes. Perform impact analysis: “If we change this calculation or pipeline, which KPIs, reports, APIs, and domains are impacted?” Provide a business-friendly complement to technical lineage, helping non-technical stakeholders understand end‑to‑end flows. Identify single points of failure and over‑complex chains (too many hops, too many transformations) across domains.

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Governance, Risk & Compliance

Map sensitive data (PII, financial, health, confidential) with classification, legal basis, retention policies, and access rules. Trace regulatory coverage (GDPR, SOX, HIPAA, IFRS, sector regulations) through policies, domains, and assets. Produce audit-ready views: lineage, responsibilities, quality rules, access controls, exceptions, and review history. Track risk register items tied to specific data domains / assets, with mitigation actions and owners.

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Data Quality & Trust

Document data quality rules at business-object and attribute level (completeness, validity, consistency, timeliness). Expose data quality scores and trends in the catalog/graph views, to guide consumers towards trustworthy assets. Link data issues / incidents (tickets, defects, anomalies) to domains, attributes, and pipelines, with status and workflow. Enable user feedback loops: analysts and business users can flag data problems directly from analytics tools and follow resolution.

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Data Discovery for Consumers

Provide a search and exploration experience across domains, terms, tags, data products, and KPIs. Surface “ready-to-use” data products (certified tables/views, semantic models, APIs, ML datasets) with SLA, owner, and usage guidelines. Show usage context: who consumes this asset, which dashboards / models rely on it, popularity and endorsements. Help users compare alternative sources (two “customer” tables, two “revenue” definitions) and pick the right one.

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Program & Governance Management

Dashboard governance maturity: domain coverage, documentation completeness, steward engagement, adoption metrics. Track role activity and gaps: domains without owners, inactive stewards, unassigned critical assets. Monitor key governance initiatives (e.g. “finance data clean‑up”, “customer 360 build‑out”) with links to affected domains and lineage. Provide governance OKRs/KPIs (e.g., catalog completeness, certified data product coverage, time to find/understand data).

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Project Support & Change Management

Use DataBakery as the standard discovery tool for new projects: understand domains, existing rules, data products, and debt before designing solutions. Conduct impact analysis for projects: cloud migration, BI replatforming, system replacement, new product rollout. Capture target architectures (domains, flows, responsibilities) and link them to current state for “as‑is / to‑be” comparisons. Support change management by communicating data-related changes visually to business stakeholders.

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AI & LLM Governance

Catalog ML/AI datasets (training, validation, test, monitoring) with sources, lineage, sensitivity, and owners. Describe LLM/RAG assets: prompt templates, retrieval collections, embedding indexes, feature sets, and their upstream domains. Map regulatory and ethical constraints on AI (allowed vs forbidden data, consent, bias risks) onto domains and AI assets. Track model dependencies: which data domains and policies are behind each model, to support audits and risk assessment.

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Collaboration & Communication

Enable discussions and comments on domains, concepts, rules, and data products, with mentions and notifications. Provide playbooks / usage guides attached to domains and data products (best practices, known pitfalls, sample queries). Use DataBakery as a central communication hub between business and IT for any topic that touches data. Capture and share decision logs (why a definition changed, why we deprecated a data source, etc.).

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DataBakery for Consulting

Target Consulting Firms & Integrators. Use DataBakery as a standard discovery & governance accelerator across all client engagements. Onboard project teams faster by providing a structured, visual map of the client’s domains, systems, and data products. Maintain reusable blueprints (domain models, lineage patterns, governance templates) that can be instantiated at each client. Support adoption of heavy catalog tools (Collibra, Alation, Atlan, etc.) by offering a lighter, business‑friendly sandbox for early governance work.

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DataBakery for M&A / Strategy

Mergers, Acquisitions, Carve‑outs. Pre‑deal / Due Diligence use cases:

• Run data due diligence: map domains, systems, qualities, and risks of the target in a structured way (beyond a generic data room).
• Assess data maturity & risk of the target: fragmentation, duplication, lack of ownership, regulatory exposure.
• Highlight data-centric value levers and synergies (customer analytics, pricing, cross‑sell, operational optimization).

Post‑merger / Post‑acquisition use cases:

• Build a unified domain map across both entities (or across group and target) to identify overlaps and gaps.
• Drive application and data rationalization: decide which CRM/DWH/MDM or data products to keep, merge, or decommission.
• Design the target data operating model: domains, owners, stewards, central vs local responsibilities.
• Plan and track integration / carve‑out roadmaps from a data perspective: which domains and flows to merge, split, or migrate, in which order.

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DataBakery for Transformation

Use DataBakery to tell the story of data to executives: domain maps, before/after views, critical journeys. Orchestrate governance roll‑out: track progress on roles activation, documentation, and domain coverage. Support training and onboarding: give new hires or new teams a navigable, visual map of the organization’s data landscape.