Answers up front.

MyDataWork is a workspace for data practitioners to manage analytical work across the tools they already use and connect it to business purpose, stakeholders, dependencies, outcomes and value. It does not replace Excel, BI, SQL, notebooks, workflow tools, warehouses or a data catalog; it provides the persistent management layer around work performed in those systems.

It brings together the Asset Catalog, use case tracking, lineage visualization, AI-assisted recommendations, portfolio reporting, and integrations with tools like Jira, in one place built specifically for how analysts actually work.

MyDataWork is designed for data analysts, analytics engineers, and analytics managers who want a practical way to organize their work, demonstrate its value, and hand off context to colleagues. It is particularly useful for analysts who manage recurring work across many files and tools, work across multiple business domains, or need to communicate the impact of their data work to stakeholders and leadership.

Yes. Click Help ▾ → “Take the guided tour” in the top-right of the app any time. The guided tour highlights each step on the live screen and walks you through the five things that get you to first value: connect your work → frame a use case → link the assets that feed it → see it roll up on the dashboard → try an AI action. At each step you can do the task right there in the live app or just read and move on — you stay in control — and it's fully skippable and re-runnable. There's also an optional “Scope an agent” tour for Agent Studio.

No. A modern data catalog centers on the governed data estate: data assets, definitions, lineage, quality, policy, access and increasingly business and AI context. MyDataWork centers on the analytical work practitioner: the use case, cross-tool assets, stakeholders, practical dependencies, activity, outcomes and value.

An analyst may use a catalog as a trusted data resource while using MyDataWork as the workspace around the work itself. They overlap by design; the difference is what each system is built around.

Not quite — though the comparisons are understandable. MyDataWork shares some surface-level characteristics with personal knowledge management tools, project trackers, and collaborative documentation platforms. But it is designed around a specific problem none of those tools were built to solve.

vs. personal knowledge management tools: the fundamental difference is that MyDataWork knows what your files actually are. Rather than asking you to describe your work in notes and build your own structure, MyDataWork reads the metadata from your actual working files — SQL scripts, Excel models, Python notebooks, Alteryx workflows, Tableau workbooks, and cloud assets — and builds the asset catalog and lineage map automatically.

vs. project trackers like Jira: trackers are excellent at tasks, tickets, and sprints, but they don't understand the file-level context behind data work — which Excel models feed which dashboards, which SQL scripts power which reports. MyDataWork captures that automatically from the files themselves, and integrates with Jira directly so teams don't have to choose between the two.

vs. documentation platforms: wikis are passive — someone has to write and maintain everything. MyDataWork is active: it detects relationships between files automatically, flags external dependencies that aren't yet tracked, surfaces outliers in reported values, and uses AI to identify automation and modernization opportunities.

What it's actually competing with: most analysts have no dedicated system for this layer of their work, so it lives in folders, spreadsheets, tickets, documents, messages, naming conventions and memory — context lives in spreadsheets, email threads, and institutional memory that walks out the door. MyDataWork is built for that majority: something that works immediately, understands their toolchain, and connects their work to business outcomes without becoming a project in itself.

When analysts document their use cases and connect them to the files and data sources they depend on, they are implicitly documenting which data platforms, datasets, and tools are generating real business value. Aggregated across a team, that tells a compelling story: which systems are actively used, what decisions they inform, and where investment is paying off.

For IT leadership, data governance teams, and platform owners, MyDataWork provides something they rarely have: bottom-up evidence of data utilization and business impact, built organically from the daily work of the analysts who depend on their systems. This supports budget justification, governance reporting, and strategic planning adding bottom-up work evidence that top-down inventory and governance views may not provide on their own.

Architecture & Value is a leadership view — on Team and Enterprise plans — that rolls your analytics estate into one picture, built entirely from what your team already tracks in MyDataWork: your tools, your use cases, the value you’ve recorded, and how heavily each platform is used. No new data collection, and no AI credits — it’s deterministic.

Four lenses: a Capability map (value delivered by each business function), a Tool portfolio (every platform placed by the value it supports against how heavily it’s used, on a TIME-style Invest / Migrate / Tolerate / Eliminate quadrant — positioned relative to your own estate, a lens for discussion rather than a directive to cut or migrate), Dependencies (where value concentrates and which platforms are load-bearing), and Change impact (pick any tool and see the value and use cases that depend on it before you touch it).

It’s the view for the team lead — available to a workspace admin (the team lead, or someone the lead nominates to hold that role). The analysts on the team don’t need to touch it; their work shows up because they already organize it in MyDataWork. It opens on a view of work shared to the team — private work stays private — and an admin can opt into a full-workspace view, which is audited. It shows value and utilization only, never cost data, and the leadership export (PDF / PowerPoint) carries those caveats on every slide. It’s decision support that complements the enterprise-architecture and finance tools you may already run — and for many teams, the only place this picture exists at all.

No. MyDataWork is designed to work alongside the tools your team already uses — Alteryx, Tableau, Power BI, Python, SQL, Excel, and others. It does not replace them or require any changes to how your team works today. Analysts continue working in the tools they know; MyDataWork simply helps them organize, document, and communicate the context around that work.

MyDataWork connects to twelve cloud sources and to local files.

Cloud sources (Setup → Connect your data → Cloud Sources; one-click setup, no installation, read-only credentials):

• Data warehouses — Snowflake, BigQuery, Redshift (read INFORMATION_SCHEMA / metadata APIs for catalog, schema, table, and column structure), and Databricks (parses notebook source for table references).
• Repositories & transformation — GitHub (file source parsed for tool type and table references) and dbt Cloud (latest run manifest.json for models, sources, and the dependency graph).
• BI & analytics — Power BI (read-only Scanner API), Tableau (Metadata API), Sigma, Looker, and ThoughtSpot — cataloging dashboards, workbooks, and datasets plus their source-table lineage.
• Data science — Dataiku (projects, datasets, and recipes with dataset table references).

Local files via the Windows Connector. Recognized types: Excel (.xlsx/.xlsm), CSV, SQL, Python (.py/.ipynb), Tableau (.twb/.twbx), Power BI (.pbix/.pbit), Alteryx (.yxmd/.yxwz/.yxmc), Dataiku exports (.zip), ThoughtSpot (.tml), and Looker LookML (.lkml).

Many other tools — Hex, DataRobot, Fivetran, Matillion, Coalesce, Prophecy, Datameer, Airbyte — write their outputs as tables into Snowflake, Databricks, BigQuery, or Redshift; once the warehouse is connected, those outputs are cataloged with no tool-specific integration. Tools that keep data only in their own backend (Smartsheet, Airtable, Monday, Notion, Salesforce, HubSpot, NetSuite) need to be replicated to the warehouse first. MyDataWork does not currently offer direct connectors for Google Drive, OneDrive, SharePoint, or Dropbox — files kept there are picked up when their sync folders sit on your local disk and are scanned by the Windows Connector.

MyDataWork builds cross-tool lineage automatically in two ways.

(1) Shared warehouse tables. When an upstream asset (SQL, Python, Alteryx, Dataiku, dbt, or a warehouse like Snowflake, BigQuery, or Redshift) and a downstream asset (Power BI, Tableau, ThoughtSpot, Looker, Sigma) reference the same table identifier, an edge is drawn. A schema-qualified match (e.g. prod_db.sales.orders) shows as Confirmed; an unqualified table name shows as Possible.

(2) File-level matches. When an Alteryx workflow or notebook reads a local file that is also in your catalog (e.g. customer_demand.xlsx), MyDataWork links them as a Likely edge.

Because of this, Excel and CSV files are no longer always manual leaf endpoints. You can still add a manual edge anywhere. Every edge carries its confidence tier (Confirmed / Likely / Possible) plus a plain-language evidence string on hover. Same-tool pairs are treated as siblings of a shared source (shown in the related-assets card) rather than a parent/child edge.

Two ways. Connect them directly (Setup → Cloud Sources) to catalog the dashboards, workbooks, and datasets themselves plus the source tables they read — the richest picture. Or rely on your warehouse connection, which catalogs the source-of-truth tables those dashboards read (a shared, schema-qualified table shows as Confirmed lineage; an unqualified name as Possible). The warehouse route shows the underlying tables; the direct connector adds the dashboards and their lineage.

Any file-based data asset your analysts work with: Excel and CSV files, SQL scripts, Python notebooks, Alteryx workflows and macros, Tableau and Power BI workbooks, ThoughtSpot TML files, Dataiku DSS exports, Looker LookML files, and supporting documentation. Assets are connected to the use cases they support, the people who own them, and the lineage that shows how they relate to each other.

Yes. The MyDataWork Connector is a lightweight Windows application that indexes the files on your computer or network drive. It parses your files on your own machine to capture structure — file names, paths, sizes, creation and modification dates, plus tool type and schema-level references such as table, column, and sheet identifiers — and syncs only that structural metadata to your workspace. Your file contents and data values never leave your computer. The Connector is digitally signed (code-signed) by MyDataWork, LLC through a public certificate authority (Azure Trusted Signing), so Windows verifies the publisher's identity when you install it.

MyDataWork automatically filters files so you focus on current work while keeping everything accessible. Four options: Current (30 days, the default — your active work), Recent (30–90 days), All files (complete catalog, no filtering), and Archived (90+ days). You see the most relevant files first while retaining access to your full asset history.

Yes. In the Assets tab, any data source detected in a file that doesn't match a cataloged asset is flagged with an amber “Not in catalog” badge — for example a SQL script referencing an untracked database table. The Lineage tab surfaces these as ghost nodes (dimmed, dashed) automatically, and the Assets-tab preview has a “Show external dependencies” toggle. When exporting your portfolio you can optionally include external dependencies in the lineage diagrams — useful when sharing with IT teams who need the full dependency picture.

Yes — significantly. When an analyst leaves or a project changes hands, the context behind recurring work — which files matter, why they exist, what decisions they support — is often lost. MyDataWork captures that context as a natural byproduct of daily work, making onboarding faster and reducing dependence on institutional memory held by individuals.

Structure, never values. For a warehouse table we hold that the table exists, what it’s called, where it lives, that it has a column named order_date of type DATE and one named region of type VARCHAR, when it last changed, and what the analyst wrote in the column comment. We do not hold any of the dates in that column, or any of the regions — not one row, not a sample, not a summary, not a count of distinct values.

Column names and types are captured from Snowflake, BigQuery, Redshift, Databricks (Unity Catalog), Power BI, Tableau, Looker, Sigma, ThoughtSpot, dbt, Dataiku, Excel and CSV headers, and Parquet/Avro footers. Names and types only.

Where a source can’t publish something, we say so rather than leaving a blank. A CSV declares column names but no types, because the format has none — that’s a fact about CSVs, not a gap in your catalog. Tableau field metadata beyond published data sources needs Tableau’s Data Management add-on. Setup → Metadata shows this per source, alongside how many assets carry column schema and when each source was last refreshed.

Local files. If you install the desktop connector, it opens files on your own machine to read their structure — sheet names, column headers, and the tables a query references — and sends us only that structure. The service never receives your file. We say “we never receive or store your contents” rather than “we never open a file,” because the second wouldn’t be true.

Only structural metadata is retained. The local Windows Connector parses your files on your own machine to extract structure — file names, paths, sizes, modification dates, and schema-level references such as table and column identifiers — and sends only that metadata to your workspace; your file contents and data values never leave your computer.

Cloud sources do read file source content or warehouse/BI metadata in order to extract structure — for example GitHub file content, Databricks notebook source, dbt manifests, warehouse INFORMATION_SCHEMA, and read-only BI/analytics metadata APIs — but the fetched content is parsed in the moment and discarded; only the resulting structural metadata (system, topics, table identifiers, column identifiers, dependency references) is persisted. No business data values, no table rows, no query results, and no formula or cell contents are ever read or stored. See our Security & IT page for the per-connector breakdown.

MyDataWork organizes the structure of your work, not the data inside it — so the personal information it holds is limited and purposeful.

What it holds: your account details (name, email, and a securely hashed password), the names and emails of any team members an admin adds, and any stakeholder names or emails you choose to record against a use case.

What it never touches: the personal or sensitive data living inside your files and tables. Because every connector reads structural metadata only, the customer records, employee data, or other PII inside your actual data are never read, transmitted, or stored.

How it's protected: passwords are hashed with PBKDF2-SHA256; card numbers are never stored (Stripe handles payments); data is encrypted at rest and in transit; and where we need to remember something tied to your email — like your one free assessment — we store a one-way hash, never the address itself.

AI features: the stakeholder names and emails you record are stripped before anything is sent to our AI provider — the AI sees the shape of your work, not your contacts. File contents are never sent. You can export everything (Setup → Data portability) or delete your account and all data at any time. We don't sell data or use it for advertising. Data Processing Agreements are available — contact contact@mydatawork.com.

In the Objectives & Progress tab of any use case you set three values: a baseline (where things stood before the work began), a current value, and a target. MyDataWork calculates progress automatically and shows a progress bar — no formula needed. The baseline is the most important value to set correctly, and it can only be truly accurate once, at the very beginning.

Progress uses professional value-measurement templates that go beyond monetary estimates: Time Savings, Quality Improvement, Efficiency Gains, Risk Reduction, and Cost Efficiency. Each provides recommended units, best-practice guidance, realistic improvement ranges, and industry-standard measurement approaches — a live, honest record ready to share at any moment.

Three actions: Archive (soft delete — removes a use case from active view while preserving all data and history), Restore (brings archived use cases back), and Delete (permanent removal). Switch between Active and Archived tabs in the Use Cases section. Useful for completed projects you want to keep for reference, seasonal work, or keeping a clean active workspace while preserving institutional knowledge.

The Dashboard tab is the first tab in every workspace and surfaces six panels designed to be read in seconds:

• Use case staleness — freshness buckets (under 30 / 30–60 / 60–90 / 90+ days).
• Portfolio totals — estimated vs realized value plus realization rate (use cases with no linked assets are excluded so the headline reflects measured work).
• Stakeholder coverage — how many active use cases have an assigned stakeholder.
• Value distribution — realized values against the workspace mean and median.
• Tool distribution — asset count by tool family.
• Workspace Agent activity — findings surfaced and acted on in the last 14 days.

The Dashboard reflects what you see on the Assets tab, so the numbers always reconcile. When demo data is loaded, a small demo/real toggle (dashboard-only) lets you flip between views.

Yes — the Workspace view (Dashboard → Workspace sub-tab) is a home base for the analytical assets connected to your work. It lists every asset you can see, grouped by tool and ordered alphabetically, showing how recently each changed and how many use cases it belongs to. Local files open on your own machine in their default app; cloud assets open in-app. It's a a workspace view over your organized asset inventory — it doesn't change what's cataloged or who can see what.

An AI-powered review of your workspace, runnable on demand from Setup → Asset Estate Assessment. The first run is free on every plan — one free assessment per email, forever. Subsequent runs use AI credits like other AI features.

What it produces: a five-section report — Estate overview, Connections worth making explicit, External dependencies, Use case opportunities, and Health observations — plus a ranked “Concrete next actions” list. Exportable to PDF.

What it reads: asset metadata and use case text only. File contents are never read. Run it after you've connected real assets and configured at least 2 active use cases. Every finding names a specific asset, use case, or stakeholder from your workspace — generic template phrasings are dropped if no workspace-specific evidence backs them.

Both are AI-powered and can be scoped to your whole workspace or a specific client/project — but they do different jobs.

The Workspace Agent is a detector. It runs rule-based checks and surfaces discrete, specific findings worth acting on — a running worklist you run any time. The Asset Estate Assessment is an analyst. It produces a point-in-time synthesis — a connected, narrative review of your whole estate's health. In short: run the Agent for an ongoing to-do list; run the Assessment for a periodic, comprehensive read.

A data-quality layer watches for value measurements that look statistically unusual relative to your other work. Once you have at least 4 use cases with non-zero values, if an estimated or realized value is significantly higher or lower than the rest, a gentle amber prompt asks you to confirm before you share it. The Portfolio tab also compares the mean and median realized value (once you have at least 3). These checks are informational only and never block you — drawing on the principles of robust statistics.

Yes. Each use case has a Notes URL field on the Overview tab. Paste a Google Doc, Notion page, Confluence URL, or any HTTPS link. When set, an “Open notes” button appears and a small indicator shows next to the use case in the list view.

Guided head-starts for a new workspace. In Setup → Solution Packs, pick a scenario and one or more topic areas, and MyDataWork adds a curated set of best-practice starter use cases — editable examples you tailor, not AI-generated — grouped into an Initiative named for the pack, plus an empty asset group. Six packs are available today: FP&A Modernization, Power BI Portfolio Review, Agentic Analytics Readiness, Consulting Discovery Accelerator, Alteryx Modernization Readiness, and Analytics Value Realization Review. Solution Packs are free on every plan (including Explorer), use no AI credits, and re-running a pack never duplicates what you already have.

A project or program that groups related use cases — the use-case counterpart of an asset group. Initiatives are personal to you, and each use case belongs to one initiative, so dashboard value roll-ups never double-count. Manage them on the Use Cases tab's By initiative view, and filter the Dashboard, Portfolio, and Insights to a single initiative.

Yes, in two complementary ways. Asset groups organize your files: create a group (e.g. one per client), assign assets (an asset can belong to more than one group), and scope any AI analysis to that group — the Workspace Agent, Estate Assessment, use case proposals, recommendations, and the Leverage modes all run against just that client or project. Initiatives organize your use cases and roll up value by initiative on the Dashboard, Portfolio, and Insights. Scoping never changes the credit cost. Especially useful for consultants and teams managing several clients in one workspace.

Go to Setup → Data portability. Export downloads a JSON file containing your complete workspace: all assets, lineage, stakeholders, use cases (with objectives, baseline/current/target values, progress notes, priorities, effort, target dates), communication logs, saved AI recommendations, sharing details, and action plans. Import merges a JSON file — existing items are updated by matching on path (assets) or title (use cases), new items are added, and nothing is deleted. Plan asset limits are enforced on import. Lineage edges import directly and don't require a manual rebuild.

No. AI is enabled by default for new workspaces. An admin can turn it on or off any time in Setup → AI Assist. AI only runs when you explicitly ask for it, and each AI action draws from your plan's daily AI-credit allowance — Explorer 3/day, Solo 5/day, Team 20/day per user, with the option to buy more.

MyDataWork uses a daily credit system, and the allowance is per user: Explorer 3/day (60-credit total cap across the 90-day trial), Solo 5/day, Team plans 20/day for each member — not a shared daily pool.

Credit-consuming features include use case proposals (1 credit each), AI recommendations, leverage analysis modes, the Assistant, and the Workspace Agent. The Workspace Agent gives you up to two free attempts that don't use daily credits; after that it's 1 credit per finding phrased, capped at 8 credits per Analyze run.

You can purchase additional credits: 10 for $5, 25 for $10, or 50 for $15. Purchased credits go into a shared workspace pool that any member draws from once their own daily allowance is used up, and only a workspace admin can buy them. Daily credits reset each day and don't roll over; purchased credits carry over until used and never expire. Your first use case proposal is free as a one-time trial. Solution Packs and Initiatives cost no credits.

Propose Use Cases (1 credit each) generates specific, actionable proposals from your actual files and workflows.

Leverage Analysis Modes: Find reuse opportunities (where use cases overlap and work could be consolidated); Identify automation candidates (processes suited to intelligent automation, with value, complexity, and next steps); Discover marketplace data (external datasets on your configured cloud platforms); and Migration Assist (opportunities to migrate or modernize tools, with effort estimates and confidence ratings).

AI Recommendations analyze your use case descriptions and linked assets to surface improvement ideas. Workspace Agent is a proactive observer that scans on demand and surfaces six categories worth your attention. MyDataWork Assistant is a context-aware chat assistant available on every screen via the teal button in the bottom-right.

Run it on demand from the Workspace Agent tab. Click “Analyze” and it checks six things, grouped into three categories:

• Cleanup — use cases with assets and progress but no stakeholder; active use cases not updated in 90+ days.
• Activity — recently-added assets (last 14 days) not yet linked to a use case (with a suggested best fit); assets you removed in the last 30 days still referenced by an active use case or lineage edge.
• Insight — high-value use cases with low realized value not updated in 60+ days; assets linked to three or more active use cases (your “hidden infrastructure”).

Each finding includes a one-click deep-link. You can dismiss with a reason or give a thumbs up/down, and the agent auto-resolves a finding when its condition no longer applies. The checks are rule-based; AI is used only to phrase each finding in plain language. You get up to two free attempts; after that, 1 credit per finding, capped at 8 per run.

Yes. The Leverage tab's “Identify automation candidates” mode uses AI to surface specific processes that could be candidates for intelligent automation. Each opportunity includes what could be automated, which assets are involved, an estimated value and complexity, and a concrete next step. You can share any opportunity by email directly from the app, and all opportunities are saved so you can revisit them without regenerating.

Yes, and that is the point of it. There is a great deal of genuinely useful public data — inflation, employment, weather, energy prices, demographics, company filings — published free on open APIs. What stops teams using it is the step before the work: knowing which of thousands of published series is about your problem, and whether your data has a column it could be joined on. MyDataWork answers that from your metadata in seconds, so the sources worth using actually get found. It does not ingest public data or blend it into yours.

The Leverage tab’s External data mode scans a use case and suggests free public datasets that may improve it — FRED, U.S. Census, BLS, NOAA, EIA, SEC EDGAR and World Bank. It works on every plan including the free Explorer trial, and needs no cloud provider configured.

Each suggestion carries a match strength — Strong match, Worth testing or Speculative — and a plain-language reason that cites the specific assets it read. The tier describes how well the source fits the shape of your work. It does not claim the enrichment will make your results better — only running it can show that.

You can preview a source, take a connection recipe for your own platform, and accept the ones worth keeping. An accepted source is catalogued as an external-dataset asset with an enriches relationship, so it appears in lineage and change-impact like any other dependency — and it does not count against your plan’s asset limit.

Yes. All seven are included by default, and you can narrow that to any subset — either from the panel in the Leverage tab or in Setup → Public data sources. Both places list what each source covers, how often it updates, and what it takes to get access: FRED, BLS, Census and EIA need your own free API key; NOAA and SEC EDGAR need no key at all, only an identifying email in the request header; World Bank needs nothing.

Your choice is remembered, and narrowing the scan does not discard results you already have — a scan limited to one source replaces only that source’s result.

No. The match is made from metadata only — asset names, tool types, the linked use case and its objective, topics, and structural schema (field names and types). We never read data values, file contents or query results, and none of your data is sent to a third-party API.

The blending happens in your environment, not ours. MyDataWork generates a connection recipe — the API call, a landing table, the suggested join and the attribution the source requires — and you run it in your own warehouse or BI tool, with your own API key. We never execute it and never store the result.

Yes. Before any AI runs, match readiness scores the use case on two things, free and with no credit used: topic match — is there a relevant source at all? — and connection key — is there a shared column, usually a date and often a place, to match the two datasets on?

These fail independently, and the message differs. Weather may clearly suit a demand forecast, but if no asset declares a date or region column the enrichment cannot land, so we tell you that and what to add. Where a use case simply has no external-data shape, such as a reporting-delivery process, we say so plainly and decline the scan instead of returning suggestions that look confident and are not.

You also choose which public sources are scanned — all seven by default, or a subset you pick in Setup or in the tab itself.

Readiness, previews, recipes, accepting and dismissing are all free. Only the scan itself uses an AI credit.

Yes. The Leverage tab's “Discover marketplace data” mode recommends external datasets available through your cloud providers. First set your platforms in Setup → Cloud Providers (AWS, Google Cloud, Snowflake, Microsoft Azure, Databricks); recommendations then include the dataset name, the platform, why it's relevant to your specific use cases, and where to find it. Recommendations are saved across sessions.

Yes. The Leverage tab's Migration Assist mode analyzes your assets or use cases and identifies opportunities to migrate or modernize — for example moving a complex Excel model to Python, replacing an Alteryx workflow with dbt, or converting ad-hoc SQL into structured dbt models. You can analyze individual assets or a full use case end-to-end. Each recommendation includes the current tool, the recommended alternative, migration effort (Low/Medium/High), estimated benefit, key risks, a confidence rating, and a concrete next step. Results are saved and shareable by email.

Yes. Agent access (Setup → Agent access) lets you give an AI agent you choose read-only access to the structure of your data work — your use cases, their linked assets, and the lineage between them — over the Model Context Protocol (MCP), an open industry standard (for example Claude Code, your team's own agent, or another MCP-capable tool).

You stay in control: you create a key for one specific agent, choose exactly which use cases it can read, and preview precisely what that exposes. The agent receives metadata only — asset names, tool types, structural schema, lineage, and use-case outcomes — never your data values or file contents, and never stakeholder names.

You choose an access mode per key. Live mode lets the agent read your current context on demand (paid plans, daily read cap; works with MCP clients that accept a bearer key). Export mode lets you download a point-in-time context file and hand it to your agent (the only mode on the free Explorer plan; works with any agent, including Claude Desktop). Every grant change and every agent read is recorded, and you can revoke a key at any time.

Yes. The Agent activity panel at the bottom of Setup → Agent access shows the record: how many times your agents have read your context over the last 7, 30, or 90 days, a per-day chart, which key each read used, and which tool the agent called — plus a recent-activity feed that also covers your own grant changes, previews, and exports.

A key you delete keeps its history, so removing a key does not erase the record that it read your work. Day buckets are UTC, matching the daily read cap, so the “today” figure always agrees with the “reads left today” number shown at the top of the panel.

You see your own agent activity only. In a Team workspace, other members see theirs — a key exposes only its owner’s context, and the activity record follows the same boundary.

No. Agent reads (in Live mode) and exports draw on their own dedicated daily allowance — the Live reads and Exports caps shown in the Agent access panel — not your AI credits. The AI credit pool that powers the chatbot, Leverage analyses, and recommendations is never touched by agent activity.

Agent Studio is where you scope an agentic use case before anyone builds it. You catalog the MCP sources your organization exposes, pick the tools an agent could use, arrange them on a simple canvas (trigger, inputs, agent, actions, outcome), and let AI help draft the surrounding definition — the value case, agent persona, reasoning approach, guardrails, and human-in-the-loop checkpoints. The result is a shareable definition you can export as a PDF or PowerPoint.

It deliberately stops short of building or running anything. Agent Studio does not deploy agents, does not call tools, and does not read your business data — when it catalogs an MCP source it reads only what that server advertises: tool names, descriptions, and input-parameter shapes. It also includes an anti-“agent-washing” check: if the work is stable, rules-based, and deterministic, it says so rather than forcing an agent where a script would be better.

They point in opposite directions. Agent access lets an AI agent you choose read the structure of your data work over MCP. Agent Studio is the reverse: it catalogs the tools your systems expose over MCP, so you can design an agentic use case around them. Both are governed by the same metadata-only boundary — neither reads your data values or file contents — and because Agent Studio only ever reads what a server advertises, cataloging a source cannot cross into execution. You scope the use case here; your team routes the actual tool access through their own governed MCP control point when they build it.

In Team plan workspaces, each member has their own personal workspace where their assets stay private by default. When a member shares an asset to the team, it appears in the team's Shared tab with attribution showing who shared it. Each asset's detail panel also shows an “Added by” field. For cloud sources the connecting user is always recorded; for the Windows Connector, the member who installed it and imported the files is recorded — each member's imports stay private to them until they choose to share.

Team workspaces use a shared bulletin-board model. Assets are private by default; when a member is ready they click “Share to Team” and it appears in the team's Shared tab, where others can browse it and click “Copy to mine” to bring an independent copy into their own workspace. Use cases, initiatives, lineage, stakeholders, AI recommendations, and Insights all remain personal — only assets are shareable. The sharer (or admin) can unshare at any time; copies already made are independent.

Team workspaces have a single admin (the Owner). Adding members: the admin creates accounts directly by entering name, email, and a starter password to share — there is no email invite flow; members log in and can change their password on the Account page. Seat limits apply by plan (2–5 for Team Starter, 6–10 for Team Growth, admin counting as one seat). Transferring ownership: admins can transfer to any current member (the previous admin becomes a regular member). Removing members: the removed member's content remains with the workspace — only their access is revoked.

A Team Metrics panel in Setup shows aggregate adoption only: total assets, how many are shared, total copies made, sharing rate, reuse multiplier, the most-reused shared assets, and a 30-day activity timeline. Per-member breakdowns are intentionally not provided — admins get the adoption story; members keep their autonomy.

If the admin leaves without first transferring admin to another member, the workspace can become orphaned. If that happens, email contact@mydatawork.com and we can reassign admin access. To avoid it, admins should transfer ownership before they leave or are offboarded.

Yes. MyDataWork includes a Jira integration (available on all paid plans) to create structured tickets and push progress updates from your use cases, with professional templates for automation projects, reporting enhancements, and data-quality initiatives. Each ticket includes objectives, stakeholders, linked assets, acceptance criteria, and a link back to your workspace. Set it up in Setup → Integrations with your Jira URL and API token. The integration is one-way — MyDataWork sends to Jira; your workspace remains the source of truth.

Yes — MyDataWork works fully in mobile web browsers for viewing and managing your assets: view your portfolio and executive dashboard, browse discovered assets, check use case progress, export portfolio summaries, review lineage, and purchase AI credits or manage account settings. Desktop-only: initial connector setup, asset discovery scans, and file-system browsing.

Connector setup requires our Windows Connector, which only runs on Windows PCs. Once configured on your desktop, you can view and manage all discovered assets from any mobile device.

Asset discovery requires access to your local files and network drives, available only through the desktop Windows Connector. Once assets are discovered, you can view and manage them from any device.

Use landscape mode on tablets for detailed asset information and lineage diagrams. Portrait mode works well for portfolio overviews and use case tracking.

MyDataWork offers a free Explorer plan for individuals plus paid Solo and Team plans:

• Explorer (free, 90 days): 1 seat, 30 assets, 1 cloud source, 3 AI credits/day with a 60-credit total cap across the trial. No credit card. Default for new self-service signups.
• Solo Monthly: $50/month — 1 seat, 300 assets, 5 AI credits/day.
• Solo Annual: $480/year — same as Solo Monthly, saving $120 vs monthly.
• Team Starter: $2,100/year — 2–5 seats, 2,000 assets shared by the workspace, 20 AI credits/day per member, team collaboration (private by default).
• Team Growth: $3,900/year — 6–10 seats, 5,000 assets shared by the workspace, 20 AI credits/day per member, advanced team features.

All paid plans include a 14-day free trial with no charge until the trial ends. AI credits can be purchased separately: 10 for $5, 25 for $10, 50 for $15.

Yes. For larger teams we offer negotiated Enterprise plans with custom seat counts and higher asset, AI-credit, and agent-access limits, billed by invoice (no credit card required). Contact contact@mydatawork.com to discuss your needs.

They're two distinct offerings:

• Explorer plan is our 90-day free workspace for individuals. No credit card. 1 seat, up to 30 user-added assets, 1 cloud source, 3 AI credits/day (60-credit total cap). New self-service signups start here by default.
• 14-day free trial applies to our paid Solo and Team plans — the standard “try before you're billed” trial. After 14 days, billing begins on the chosen plan.

You can upgrade from Explorer to a paid plan at any time during your Explorer trial or the 30-day frozen period that follows.

Register directly at app.mydatawork.com and select a plan. New signups start on the free 90-day Explorer plan by default; paid Solo and Team plans include a 14-day free trial, so you can explore all features before your trial ends.

Two different actions with very different outcomes. Canceling your subscription stops future charges and ends paid access, but your workspace data is retained for 30 days so you can resubscribe and pick up where you left off (Account → View plans & billing → Manage billing). Deleting your account is immediate and permanent — your account, workspace, and all data are removed right away, with no retention and no recovery. If you simply want to stop paying, cancel; delete only if you want to permanently remove yourself and your data.

From the Account page, click “Delete my account” at the bottom. A confirmation dialog explains that deletion is immediate and permanent, data is not retained, and any subscription is automatically canceled. Requirements: super-admin accounts can't be self-service deleted; a Team admin with other members must transfer ownership or remove all members first; and active annual subscriptions are non-refundable, so deleting mid-cycle forfeits the remaining paid time.

Annual plans are non-refundable. If you cancel an annual subscription you retain access until the end of the current period, then lose access (with 30-day data retention). Deleting mid-annual-cycle immediately cancels and forfeits the remaining prepaid time with no refund. On a monthly plan you can cancel anytime and retain access through the end of the current billing period.

AI credits reset daily and are per user — Explorer 3/day (60-credit trial cap), Solo 5/day, Team 20/day per member. If you run out, purchased credits (10/$5, 25/$10, 50/$15) are drawn from automatically — they sit in a shared workspace pool any member can use once their daily allowance is exhausted; only admins can purchase them. Purchased credits are available immediately and carry over until used. You can also wait for the daily reset.

Some features have rate limits to protect platform resources: lineage rebuilds 5/day, Jira integration calls 20/hour (shared across push and progress updates), and exports 10/day. If you hit one, you'll see a message and can retry after it resets. Normal workflows rarely hit these — they exist mainly to prevent accidental runaway usage.

Contact us at contact@mydatawork.com. We aim to respond within 2 business days.

Yes. MyDataWork for Teams is available in AWS Marketplace as an annual contract for up to 20 users, including a monthly pool of AI credits and a 14-day free trial. You subscribe with your AWS account, and the charge appears on your existing AWS bill — no new vendor to set up and no separate payment method.

After subscribing, AWS returns you to MyDataWork to create your workspace. If the email you use already has a MyDataWork account, we attach the new workspace to it rather than making you start again.

The product is identical. What changes is how you buy and how you manage the subscription. Billing runs through AWS, so seats, renewal and cancellation are handled in your AWS Marketplace console rather than the app’s billing page — the AWS Subscription page under Account links straight there. AI credits are pooled monthly across the workspace rather than allocated per user per day. Priority support is included.

Cancel in your AWS Marketplace console, under Manage subscriptions. Because AWS owns the billing relationship, cancelling inside MyDataWork is not possible for these workspaces. Cancelling stops the contract renewing; your access continues to the end of the term you have already paid for, and your workspace data follows the same retention rules as any other plan.