What changed between Early Access and GA — and what’s still ahead
MAY 4, 2026 – After several months of Early Access development, MyDataWork is now generally available. Analysts, BI developers, data engineers, data scientists, and the leaders who manage them can sign up directly at mydatawork.com to start organizing their analytical work.
For readers new to MyDataWork: the platform is a workspace where data professionals catalog the analytical work they do — the files, scripts, dashboards, notebooks, and queries scattered across the tools they use every day — and connect that work to the use cases, stakeholders, and outcomes that give it business meaning. Where there used to be fragmented work in personal drives and tribal knowledge in people’s heads, there is now a connected, queryable, current picture of analytical activity.
This post covers what changed during Early Access and what is still ahead. The summary is that nearly every limitation called out in our Early Access announcement has been resolved, and a number of capabilities have been added that were not part of the original Early Access scope. The platform that is going generally available is meaningfully more capable than the one Early Access users first encountered.
What’s resolved from Early Access
The Early Access announcement explicitly named four limitations. Three of them are now resolved.
Team collaboration is shipped. Early Access provided single-user workspaces only. The general availability release introduces Team plans built on a “bulletin board” model: each team member has a personal workspace by default, and assets can be explicitly shared to a team space when collaboration is wanted. This design preserves individual control over work-in-progress while giving teams a place to coordinate on shared analytical assets. Team plan administrators see aggregate adoption metrics — total assets, sharing rates, reuse multipliers, top reused assets — without surveilling individual contributors.
AI capabilities are fully available. Early Access included a reduced subset of AI functionality. The GA release ships the complete AI surface: an AI Assistant for use case generation and asset analysis, automation candidate identification through Leverage analysis, migration intelligence to guide tool transitions, and contextual suggestions throughout the workspace. Daily AI credits are calibrated to plan tier, and additional credits can be purchased as needed.
Cloud source integrations are expanded. Early Access supported core analytical platform integrations. GA adds dedicated cloud connectors for GitHub, dbt Cloud, Databricks, and Snowflake — covering the dominant cloud-hosted tools where analytical assets live. Cloud sources require no installation, authenticate via standard token-based access, and scan automatically on a configurable schedule.
The fourth Early Access limitation — Mac and Linux Connector availability — is still in progress. Windows Connector is the only locally installed Connector at GA. Mac and Linux versions are on the post-GA roadmap. Cloud-based assets work on any operating system through the cloud source integrations.
What’s new that wasn’t part of Early Access
A number of capabilities emerged during the Early Access period in response to user feedback, broader product thinking, or both. These were not part of the original Early Access scope.
Lineage with directional arrows. The dependency map between analytical assets now shows direction — which assets feed which, distinguishing source from output. Earlier versions showed connections without direction, which made it harder to reason about data flow. The arrows render across the lineage tab, asset detail previews, and exported PDFs and PowerPoints.
Connector source diagnostics. When the Connector encounters an unreachable folder, an unrecognizable asset path, or a permissions issue, the affected assets are flagged in the asset list with a “Source unreachable” badge. This makes it easy to identify which parts of the catalog need attention without scanning the entire workspace manually. Assets that have been intentionally removed are flagged separately so users can distinguish between “we lost access” and “we removed this on purpose.”
Bulk asset management on source removal. When a user removes a cloud source, the platform now asks whether to keep or remove the assets that came from that source. Removed assets are soft-deleted and can be restored individually at any time. This addresses a real pain point — testing or removing a source previously left orphaned assets cluttering the workspace.
Cross-workspace Connector coordination. A single Connector installation can serve multiple MyDataWork workspaces over time. When a user logs into a different workspace from the same machine, MyDataWork detects the existing Connector and offers a one-click “Add this workspace to Connector” path. This handles the common case where one machine is shared across testing, multiple accounts, or transitions between workspaces.
Insights tab. A new view aggregates patterns across the workspace: which use cases drive the most value, which assets are most heavily depended on, where work is concentrated, and where opportunities for reuse or automation exist. The Insights view is designed for the leader perspective — pattern recognition across the analytical portfolio rather than asset-by-asset detail.
Soft-delete architecture throughout. Asset removal is now soft by default, preserving relationships to use cases, lineage edges, and stakeholder records. Removed assets can be restored individually at any time, with all their connections intact. This makes the platform more forgiving for users who are still figuring out how they want to organize their work.
What’s still ahead
Several capabilities are on the post-GA roadmap and worth naming here so users know what to expect.
Mac and Linux Connectors. Windows-only at GA. Mac is the higher priority of the two and is targeted for the first post-GA quarter.
Additional cloud source integrations. GA ships GitHub, dbt Cloud, Databricks, and Snowflake. Looker, Sigma Computing, Hex, BigQuery, Tableau Cloud, and Power BI Service are candidates for future releases based on user demand.
Connector upgrade experience. The current Connector installer overwrites the existing installation cleanly but does not yet detect existing versions or notify users about upgrades in progress. A more polished upgrade experience is on the post-GA list.
Per-source bulk asset management for local folders. GA includes bulk asset cleanup when removing cloud sources. The equivalent capability for local folder sources will follow.
Mobile experience. The current platform is optimized for desktop. Mobile-friendly views are a longer-term consideration based on how customers actually use the product after launch.
This is not an exhaustive list — it reflects the items most likely to be relevant to current and prospective users. Customers with specific needs are encouraged to reach out at contact@mydatawork.com.
What this means for you
The platform that is going generally available has been shaped by months of real-world feedback from analysts and analytics leaders across multiple industries. The capabilities present at GA are the capabilities that survived contact with actual usage — the features that proved themselves valuable in practice, refined into a coherent product.
For analysts who want to organize their own work: the Solo plans give individual contributors everything they need to catalog assets, document use cases, track outcomes, and visualize the dependencies that make their work coherent.
For teams that want to coordinate without surveilling: the Team plans deliver shared visibility into adoption and reuse without compromising the individual ownership that makes analytical work feel sustainable.
For organizations preparing for agentic AI initiatives, planning tool migrations, or navigating mergers and reorganizations: the foundation MyDataWork provides — a continuous, accurate inventory of analytical work and its dependencies — is the layer that makes those strategic initiatives plannable rather than reactive.
The fastest way to evaluate is to start. Sign up at mydatawork.com, download the Connector, and see how your work organizes itself within the first session.