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 ![Holistics Data](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_1d631c6cdbdd4670e6886c650da67608/holistics-data.png "Holistics Data")
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# Holistics Data Discussions

Holistics is an AI analytics platform for teams that need governed, trustworthy answers across dashboards, self-service, and AI. It's built on a code-based semantic layer, so the metrics your team relies on stay consistent whether they're viewed in an embedded dashboard, explored by a business user, or surfaced through AI. With Holistics, you can: - Trust your AI analytics: Holistics AI reasons over your governed metric definitions rather than raw tables, so answers stay consistent with the numbers your team already uses. - Give business users real self-service: Business users can answer their own questions inside the governed semantic layer instead of filing analyst ticket. The layer is expressive enough to handle complex follow-ups—cohort retention, period comparisons, ratios across grains—so exploration doesn't dead-end in spreadsheets. - Manage analytics like software: Every model, metric, and dashboard is defined as code, version-controlled in Git, and promoted through environments. Pull requests, code review, and rollbacks all work the way they do for application code. How Holistics is built differently Most BI tools now ground their AI in a semantic layer, but the layer underneath can only express first-order queries (slice, filter, group). AI hits a ceiling on real analytical questions like period comparisons, cohort retention, and ratios across grains, and self-service stops at simple breakdowns. Holistics is built on a deeper foundation: 1/ A programmable semantic layer. Most BI tools define their semantic layer in YAML configs, which are schemaless, error-prone, and require Jinja workarounds for any reuse. Holistics uses AML, a typed modeling language where models, dimensions, measures, and relationships are first-class language constructs—with modules, extends, partials, conditionals, and IDE tooling. A real language built for analytics modeling, not generic key-value configs. 2/ A composable query language on top. Most semantic layers treat metrics as SQL strings that can't combine or reuse. AQL treats metrics as first-class composable objects, so period comparisons, cohort retention, and ratios across grains stay inside the metric layer instead of leaking into derived tables and spreadsheets. 3/ Analytics-as-code infrastructure. Most BI tools are UI-first with Git bolted on as an afterthought (if at all). In Holistics, every definition is version-controlled in Git from day one, reviewable through pull requests, and promotable through environments. Code is the source of truth. Key capabilities - Governed AI analytics that reasons over defined metrics, not raw tables. Programmable semantic layer (AML), a typed modeling language for metrics, dimensions, and relationships - Self-service exploration for non-technical users, safely bounded by the governed layer - Analytics-as-code: Git version control, pull-request review, and environment promotion from day one. - Interactive dashboards and reporting with filters, drill-downs, and scheduled delivery - Embedded analytics with embeddable AI chatbot, custom themes white-labeling and row-level security for customer-facing use cases

 ![Holistics Data](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_1d631c6cdbdd4670e6886c650da67608/holistics-data.png "Holistics Data")

Holistics Data Discussions

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## What is Holistics Data Software used for?
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What is Holistics Data Software used for?

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Embedded Business Intelligence Software

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