Current edition boundaries
DataRead Free 0.1.0 focuses on single-file preparation, issue review, spreadsheet editing, export and manually selected basic graphs. DataRead Advanced 1.0.0 adds expanded statistical exploration, graphics, diagnostics and reporting. DataRead Professional 2.0.0 adds multi-file Batch Processing and a guided baseline Machine Learning Lab with supported evaluation workflows. These are the only editions represented as current releases in this website.
Enterprise is a concept only
DataRead Enterprise has no released version, installer, price, listing or licence. All 25 proposals below are exploratory concepts only. They are not current product features and are not included in any present purchase.
Proposed capability areas
- Expanded regression, hypothesis testing and A/B testing. Proposed experimental and inferential analysis beyond the current Advanced edition's supported statistical workflows. [Advanced Analytics]
- Time-series forecasting and anomaly intelligence. Proposed analysis of sequential data, future-value estimates and unusual changes over time. [Forecasting]
- AutoML, hyperparameter tuning and model comparison. Proposed automated testing and ranking of candidate models and configurations; distinct from Professional's guided ML workflow. [Machine Learning]
- Neural networks and GPU-assisted training. Proposed neural-network workflows and optional acceleration on supported hardware. [Deep Learning]
- Advanced deep-learning architectures and computer vision. Proposed image and visual-data analysis using more advanced model architectures. [Deep Learning]
- Text analytics, language processing and sentiment analysis. Proposed extraction of themes, entities, classifications and sentiment from text data. [Language Intelligence]
- Connections to existing language models for data questions. Proposed connections that let users ask natural-language questions about supported datasets and analysis results. [Generative AI]
- Local language models, document Q&A and RAG. Proposed local-model options and retrieval-based question answering over approved documents and project materials. [Generative AI]
- Language-model training and fine-tuning. Proposed tools to prepare training material and adapt supported language models for specific tasks. [Generative AI]
- Reusable analysis workflows and experiment tracking. Proposed saved, repeatable analytical workflows with recorded configurations, runs and experiment outcomes. [Automation]
- Model registry, versioning, APIs and deployment tools. Proposed central model records, version control, controlled interfaces and deployment support. [Model Operations]
- SQL, database and expanded data-source connectors. Proposed connections to additional structured data stores and enterprise source systems. [Data Integration]
- Model explainability, governance and audit controls. Proposed model explanations, documented approvals, traceable changes and governance records. [Trust and Governance]
- AI agents and automated multi-step analysis. Proposed task-oriented agents that coordinate multiple approved analysis and data-preparation steps. [Advanced AI]
- Synthetic data generation and recommendation systems. Proposed privacy-aware artificial datasets and systems that identify relevant options or recommendations. [Advanced AI]
- DataRead Vault: project catalogue, snapshots and backups. Proposed local-first project organisation, recoverable snapshots and managed backup records. [Data Management]
- Automated data-pipeline orchestration. Proposed scheduling of imports and transformations, dependency management, failure handling and automatic retries. [Data Engineering]
- Data observability and end-to-end lineage. Proposed monitoring of freshness, schema changes, quality signals and the path data takes through projects and outputs. [Data Reliability]
- Enterprise data catalogue and semantic layer. Proposed searchable data definitions, business glossary, ownership metadata and consistent shared metric meanings. [Data Management]
- Enterprise privacy and security management. Proposed role-based access, sensitive-data classification, masking, encryption and configurable retention controls. [Security and Privacy]
- Production ML monitoring and MLOps. Proposed monitoring of deployed models for drift, performance changes, latency and operational issues after release. [Model Operations]
- Machine-learning feature store. Proposed reusable, documented and versioned model input features for consistent training and prediction workflows. [Machine Learning]
- Real-time data streaming and intelligence. Proposed processing of continuously arriving data with event-triggered analysis, alerts or predictions. [Data Engineering]
- Decision intelligence, simulation and optimisation. Proposed what-if scenarios and constrained optimisation to compare alternatives and support operational decisions. [Decision Support]
- Knowledge graphs and graph-enhanced AI. Proposed modelling of entities and their relationships to enrich search, discovery and context-aware analysis. [Knowledge Intelligence]
Change and cancellation rights reserved
The publisher may revise the roadmap names, descriptions, architecture, dependencies, scope, sequencing and priorities. Items may be combined, divided, postponed, redesigned, replaced, reduced, cancelled or never developed. Public appearance on a roadmap does not create a contractual obligation, customer entitlement, support commitment, upgrade promise or delivery guarantee. No release date, price or availability is offered.
When a feature becomes current
A feature should be considered available only when the publisher has implemented and tested it, identified the supported version, published accurate release notes and updated the relevant product documentation and licence terms. Future releases may change existing functions or requirements.
Do not use roadmap content as a purchase decision guarantee
Evaluate a current edition using its actual version-specific features, guides, system requirements, marketplace listing and final licence. Questions about proposed Enterprise concepts do not imply that they can be demonstrated, tested or supported today.