MyDataWork is a workspace for data analysts. It catalogs the files and cloud assets behind your work, visualizes how they connect, tracks use cases and business outcomes, and helps you communicate the value your data work delivers.
Dashboard is the first tab on every workspace. It surfaces six panels of workspace health at a glance — designed to be read in seconds before you dive into the rest of the app.
The dashboard reflects what you see on the Assets tab — your own assets plus any shared to the team. Admins can see the full workspace catalog on the Setup → Workspace view if needed for oversight.
Demo / real toggle — when your workspace has demo data loaded, a small segmented toggle in the upper-right lets you switch the dashboard between the demo view and your real-data view. The toggle is dashboard-only: it doesn’t change what’s shown in Assets, Use Cases, Workspace Agent, or the Assessment. When you remove demo data, the toggle disappears and the dashboard renders from your real data only. If your workspace has neither real assets nor demo data, the dashboard shows a "No real assets connected yet" empty state with shortcuts to Setup or to load demo data.
Overview / Workspace modes — the Dashboard tab has two views, switched by an Overview | Workspace sub-tab bar at the top. Overview (the default) is the six-panel Asset Health Dashboard above. Workspace is a home-base cockpit over your whole catalog: every asset grouped by tool (alphabetically), each showing how recently it changed and how many use cases it belongs to (“in N use cases”, or “no use case” when it isn’t tied to one — a count of use-case links, not a comment on the file). Local files get an Open file button that opens them on your machine (via the Connector, in your default app for that file type); every asset has a Details link to jump to it. Overview stays the default; your choice is remembered per browser.
An AI-powered review of your workspace, runnable on demand from Setup → Asset Estate Assessment. Surfaces connections worth documenting, use case opportunities, and health observations — the kind of analysis a senior analyst would write up after auditing your estate. First run is free on every plan — one free assessment per email, forever. You can re-run the assessment any time afterward; subsequent runs use AI credits like other AI features.
Install the Windows Connector once. It runs silently in the background, scanning the folders you choose and keeping your workspace current automatically.
Best for: Excel, SQL, Python, Power BI, Tableau, Alteryx, CSV files on your PC or synced cloud drives (OneDrive, Google Drive, Dropbox).
Connect directly to cloud platforms using an API token. No installation required — works on any device.
Supported: warehouses (Snowflake, BigQuery, Redshift, Databricks), repositories & transformation (GitHub, dbt Cloud), BI & analytics (Power BI, Tableau, Sigma, Looker, ThoughtSpot), and data science (Dataiku).
The MyDataWork Connector is a small background process that scans your chosen folders and sends file metadata to your workspace. It starts automatically when you log in to Windows.
Connect cloud platforms directly from Setup → Cloud Sources. No installation required. Each source type needs an API token or credentials.
| Source | What's indexed | Credentials needed |
|---|---|---|
| Snowflake | Tables and views from your databases | Account, username, password, database, schema |
| BigQuery | Tables and views across datasets (metadata APIs — no query cost) | Service-account JSON key with the BigQuery Metadata Viewer role |
| Redshift | Tables and views via INFORMATION_SCHEMA | Cluster endpoint, port, database, username, password |
| Databricks | Notebooks and jobs from your workspace | Workspace URL and personal access token |
| GitHub | Data files (.sql, .py, .ipynb, .xlsx, .csv) across repositories | Personal Access Token with repo read access |
| dbt Cloud | Models, sources, and exposures from dbt projects | dbt Cloud API token and account ID |
| Power BI | Workspaces, datasets, reports, dashboards + dataset→source lineage (read-only admin Scanner API) | Azure AD service principal: tenant ID, client ID, client secret |
| Tableau | Workbooks and published datasources + their upstream source tables (Metadata API) | Server URL, site, personal access token name + secret |
| Sigma | Workbooks and their source references | API URL, client ID, client secret |
| Looker | Dashboards and looks | API base URL, client ID + secret (API3) |
| ThoughtSpot | Liveboards and worksheets | Instance URL, username, password |
| Dataiku | Projects, datasets, and recipes | DSS base URL and API key |
Cloud source assets appear in the Assets tab alongside your local file assets, each with a badge showing its source.
When you connect a warehouse (Snowflake, BigQuery, Redshift, or Databricks) to MyDataWork, you’re connecting more than raw tables. You’re cataloging the work products of every tool that writes into those warehouses — which, in the modern data stack, is most of them.
This is one of MyDataWork’s core design choices: integrate with the warehouse to automatically cover the many tools that write into it — and connect key BI and analytics tools directly when you also want their dashboards and workbooks. When your stack evolves, MyDataWork follows automatically. If it lands in your warehouse, it lands in MyDataWork.
These tools persist their outputs as tables, views, materializations, or model artifacts in Snowflake or Databricks. When you connect your warehouse, MyDataWork catalogs those objects automatically — schema, columns, lineage, freshness — with no tool-specific integration needed.
Transformation
Analytics and BI builders
ML and data science
Data movement (the landing zone for your warehouse)
BI tools query your warehouse but persist their own state — dashboards, semantic models, extracts — in their own cloud. You have two options. Connect the BI tool directly (Setup → Cloud Sources) to catalog its actual dashboards / workbooks / datasets and their source-table lineage. Or rely on your warehouse connection, which catalogs the source-of-truth tables those tools depend on (so you can see what feeds each dashboard and what would break if a column changes) but not the dashboards themselves. Direct BI connectors available now:
These tools store their data in their own cloud, not in your warehouse. To catalog them in MyDataWork, replicate their data into Snowflake or Databricks first — typically via Fivetran, Airbyte, or a similar service. Examples:
MyDataWork also catalogs assets from these sources directly — configured in Setup → Cloud Sources (no installation), or via the desktop Connector for local files:
A team uses Snowflake as their warehouse, dbt to transform data, Sigma to build analytics, DataRobot for ML predictions, and Tableau for executive dashboards. They also use Smartsheet for project tracking.
When MyDataWork connects to their Snowflake:
The modern data stack consolidates around the warehouse. By cataloging what lives in your warehouse, MyDataWork covers the work products of essentially every modern data tool — without you waiting for tool-specific connectors, and without losing coverage as your team adopts new tools.
| Question | Answer |
|---|---|
| Does MyDataWork support [my specific tool]? | MyDataWork connects directly to cloud warehouses (Snowflake, BigQuery, Redshift, Databricks), repositories & transformation (GitHub, dbt Cloud), BI & analytics tools (Power BI, Tableau, Sigma, Looker, ThoughtSpot), and data science (Dataiku) — plus local files via the desktop Connector. BI tools can be connected directly to catalog their dashboards / workbooks / datasets and lineage, or picked up via your warehouse connection (which catalogs the source-of-truth tables they read). Most other modern tools (DataRobot, Alteryx, Fivetran, Matillion, Coalesce, Prophecy, Datameer, Airbyte, Hex) write their work products as tables and views into your warehouse, and those outputs are cataloged automatically when you connect it — no tool-specific integration needed. Tools whose data lives in their own backend (Smartsheet, Airtable, Salesforce, HubSpot, etc.) require replication to your warehouse first via Fivetran or Airbyte. See What gets cataloged through your cloud connections for the full breakdown. |
| What if my team adopts a new tool you’ve never heard of? | If the new tool persists its outputs to Snowflake or Databricks, MyDataWork catalogs those outputs automatically — no waiting for a tool-specific connector. This is the design choice: MyDataWork integrates with the warehouse, not the dozen tools that use it. If the new tool stores data only in its own backend, replicate it to your warehouse via Fivetran or Airbyte and the resulting tables become cataloged like any other warehouse object. |
To remove a cloud source, click Remove next to the source in Setup → Cloud Sources. A confirmation dialog gives you a choice:
For GitHub connectors configured with multiple repositories, “Also remove all assets” applies to all repos in that connector.
From metadata alone — without ever reading file contents — MyDataWork infers lineage two ways: by shared warehouse-table references (which SQL scripts, dbt models, and warehouse tables feed the Power BI, Tableau, ThoughtSpot, and Looker reports that query them) and by file-level filename matches (which local files an Alteryx workflow or notebook reads). Every connection carries a confidence tier — Confirmed (a schema-qualified warehouse table or a manual edge), Likely (matched by filename), or Possible (an unqualified table name) — with the supporting evidence shown on hover, so you see not just that two assets connect but how solidly.
Assets in the Assets tab may show status badges indicating their current state:
Hover over a Source unreachable badge to see the specific error type: path not found, permission denied, timeout, or OS error.
| Type | Extensions | What's extracted |
|---|---|---|
| Excel | .xlsx, .xlsm | Sheet names, column headers, row counts |
| CSV | .csv | Column names, row count |
| SQL | .sql | Table references (FROM / JOIN, CTEs excluded), target tables, measures, group-by grain, join keys |
| Python notebooks | .py, .ipynb | Embedded SQL tables, imports |
| Alteryx | .yxmd, .yxwz, .yxmc | Input/output files, source tables, tool & ML-model inventory, join keys (macros (.yxmc) cataloged as Alteryx / macro) |
| Tableau | .twb, .twbx | Connections, source & custom-SQL tables, worksheets, dashboards, calculated fields, datasource names |
| Power BI | .pbix, .pbit | File name and path |
| ThoughtSpot | .tml | Object kind (worksheet / model / answer / liveboard), tables and connections referenced |
| Dataiku | .zip (DSS project export) | Project name, recipe count, dataset count |
| Looker | .lkml (.view.lkml, .model.lkml, .dashboard.lkml) | Object kind, named measures (type + SQL), dimensions, joins, includes, connection, sql_table_name references |
| GitHub | All data file types | File path, size, last modified |
| dbt Cloud | Models, sources | Model name, project, materialization |
| Databricks | Notebooks, jobs | Notebook path, language, last modified |
| Snowflake | Tables, views | Table name, schema, row count, last altered |
ThoughtSpot, Dataiku, and Looker integrations support file-based metadata extraction. Cloud-native instances of these tools require export or local file access.
MyDataWork includes several AI-powered features. AI is enabled by default for new workspaces — an admin can turn it on or off anytime in Setup → AI Assist. (Usage still draws from your plan's daily AI credits.) The AI-powered features are:
All AI features share a daily credit quota, and that quota is per user. Explorer users receive 3 AI credits per day, Solo users 5, and Team plan users 20 — on a Team plan each member independently gets their own 20 per day (it is not a shared daily pool, and nobody has to apportion it). Credits reset daily. The Assistant draws from the same quota.
Buying more credits. Beyond the daily allowance, a workspace can hold a shared pool of purchased AI credits (packs of 10 / 25 / 50 on the Billing page). Any member automatically draws from this shared pool once their own daily allowance is used up. Only a workspace admin can buy credits for the pool (the account owner is an admin, so Solo/Explorer owners can always buy). On a Team plan, non-admin members don’t see the purchase controls — they ask an admin to top up the pool. Purchased credits never expire and survive plan upgrades.
The Leverage tab is your AI-powered analysis hub. It offers four modes, each requiring AI to be enabled:
Scope by group or initiative. Every AI analysis — all four Leverage modes, Propose use cases, the Asset Estate Assessment, and the Workspace Agent — can be scoped to an asset group — or to an initiative (a set of related use cases) — instead of the whole workspace. The “Scope” control lists both your asset groups and your initiatives; a group resolves to the use cases whose assets are in it, an initiative to the use cases assigned to it. Pick a group from the “Scope” control (or the “Scope by group” chips above the selection list) to run a per-project / per-client analysis — for example, a consultant with three clients organized into three groups can assess each separately. The default is always the whole workspace, and scoping never changes the credit cost. Create groups on the Assets tab (“My groups”). Automation opportunities and migration recommendations can each be removed individually with the × control, and past Estate Assessments can be cleared from the history list (the assessment itself is preserved, just hidden).
The Workspace Agent tab runs rule-based checks across your use cases and assets on demand and surfaces specific findings worth acting on across six categories. Click Analyze and the agent runs every rule eligible for your plan, producing suggestions with deep-links to the relevant asset or use case. It's a running worklist of discrete items — run it anytime for an updated set. (For the big-picture, synthesized view, the Asset Estate Assessment pulls these findings into a periodic health report — see below.)
The six rules group into three categories:
Each suggestion can be dismissed with a reason (not relevant, already handled, revisit later) and reacted to with 👍/👎 feedback. Auto-resolve: when a suggestion's condition no longer applies (you assign the stakeholder, link the asset, restore the removed asset), it's automatically marked resolved on the next run.
Your first two Analyze runs are free attempts that don't draw from your daily AI credits. Subsequent runs draw from your daily AI credit pool, with a ceiling of up to 8 credits per run (shown above the Analyze button before you click). In practice runs usually consume far fewer credits because most rules don't find candidates.
Agent access lets you give an AI agent you choose read-only access to the structure of your data work — your use cases, the assets linked to them, and the lineage between those assets — over the Model Context Protocol (MCP), an open industry standard for connecting AI agents to tools. You'll find it in Setup → Agent access. It is a permission you control: an agent sees only the use cases you opt in, as metadata only — never your actual data values or file contents.
The flow is four steps: (1) create a key for one specific agent; (2) choose which use cases it may read; (3) preview exactly what that exposes; (4) connect or export to your agent.
Two access modes, chosen per key:
Authorization header — for example Claude Code or a custom agent. (Claude Desktop's connector authenticates via OAuth, not a bearer key, so it cannot use Live — Claude Desktop users use Export.)Plan gating: Explorer (free) is export-only; Solo and Team unlock live mode with higher caps (Team also scales the number of keys with seats).
Does Agent access use my AI credits? 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. Those caps are a daily anti-abuse ceiling that resets each day and is completely independent of your AI credits: an agent reading your context on demand can only reach its own separate daily cap — it can never consume the AI credits you use for the chatbot or Leverage.
What an agent receives (metadata only): asset names, systems, tool types, paths, topics, structural schema (column names and types), the lineage between assets, and use-case outcomes (objectives, status, and value figures) — never data values or file contents. Stakeholder names and other people data are not exposed. MyDataWork provides the map of where your data lives; reading the data itself happens through your agent's own connections, never through MyDataWork.
Lineage confidence: each lineage link carries an evidence string describing how it was established (for example, “inferred: shared source table:warehouse.orders”) and a coarse confidence band. The confidence number is not a measured probability or similarity score — it is a provenance band (for example, inferred-from-a-shared-table versus manually asserted). The evidence string is the authoritative signal.
Every grant change and every agent read is recorded in your workspace's audit log, and you can revoke a key at any time. Want to see the idea first? The Agent access panel has an optional “See an example” link (when demo data is loaded) that shows, on demo data only, how an agent reasons about the same question with versus without this context.
Two sides to Agent access. The keys above expose your data work to an agent. The other side — MCP Sources, in Setup → Agent access → MCP Sources — goes the other way: you catalog the MCP servers your own systems (CRM, ERP, warehouse, ticketing) expose, so their tools become building blocks you can design with in Agent Studio (below). Cataloging is metadata only: MyDataWork reads a tool's name, description, and input shape — it never invokes a tool or reads a value. There's a one-click “Add example capabilities” to try it with simulated sources.
Agent Studio is where you scope an agentic use case — it's a dedicated tab (after Lineage). You don't build or run an AI agent here (no code, no deployment); you decide which use case is worth pointing an agent at and how the pieces fit, then export a shareable definition your engineering or automation team builds from. Everything is metadata only.
It's a five-step flow: (1) Frame it — name the use case and the problem in a line (or start from a use case you already track); (2) Add tools — from your cataloged MCP sources, read-only tools (get/list) become the agent's Inputs, read-write tools (update/post) become its Actions; (3) Design the flow on a visual canvas — Trigger → Inputs → Agent → Actions → Outcome; (4) Draft with AI — drafts the value case, persona, a recommended reasoning approach, guardrails, and governance from your canvas, with an anti-“agent-washing” check that flags when a plain script would do (fills blank fields only, never overwrites your text; 1 AI credit); (5) Review & export — save the definition and export it as a PDF or PPT (a 6-part spec plus the flow diagram).
Where it fits. Agent Studio is the design layer upstream of the runtime: it scopes the use case, then hands off. The exported definition ends with a “Before you build” note reminding your team to route runtime tool access through their governed MCP control point (validate each source — identity, allow-list, audit — before any tool is invoked). Agent Studio is not a control point and does not verify that a server is safe — it scopes and hands off; your gateway governs the runtime.
Each use case has a Notes URL field on the Overview tab. Paste a Google Doc, Notion page, Confluence URL, or any HTTPS link where you keep notes for this use case. When set, an "Open notes ↑↗" button appears in the panel and a small ↑↗ indicator shows next to the use case in the list view.
Team workspaces are private by default for every role — including admins. Each member’s assets and use cases are visible only to that member; the team admin sees the same private-by-default view of their own work. The asset list defaults to a merged view showing your own assets plus any asset another team member has explicitly shared to the team.
The Assets tab has a toggle — All (default, your own + shared), Mine (only your own), and Shared (only team-shared) — so you can focus on what’s relevant. Admins additionally see a Workspace tab that exposes the full workspace catalog when needed for onboarding, audit, or oversight tasks. Use of the Workspace view is explicit, not the default. The Use Cases tab has an All / Mine toggle (plus the admin-only Workspace tab); use-case sharing is on the roadmap and a Shared option will appear there when it ships.
Export your entire workspace as a JSON file for backup or migration. Go to Setup → Data portability.
The Setup tab has two sub-tabs: Configuration (all the settings described above — Connect your data, AI configuration, Integrations, Workspace management, Data portability, Agent access, and the Asset Estate Assessment; each section is collapsible and starts collapsed so you can scan the list first) and Solution Packs.
Solution Packs are a head start for a scenario. Available packs organize their starters into topic areas; when you start a pack you choose one or more areas yourself, and only those are created. Activating does two things:
Six packs are available, each creating a new asset group whose name you can edit before activating:
Solution Packs are free on every plan (including the Explorer trial) and use no AI credits — activation just creates the editable starters and the container group; nothing is AI-generated.
Re-running a pack never creates duplicates. Activating again reuses the same-named group and initiative (no second copy) and skips any starter use case you already have, so re-running the same areas adds nothing, and choosing a new topic area later adds only its new use cases into the same group and initiative. If you deleted a starter and want it back, re-running restores it.
An initiative is a project or program that groups related use cases — for example, an FP&A Modernization Assessment. It is the use-case counterpart of an asset group (which groups assets): initiatives group use cases, asset groups group assets. Initiatives are personal — you see and manage only your own.
Local Windows Connector: sends file names, paths, sizes, and modification dates to your workspace — never file contents. Your files stay on your machine.
Cloud source connectors work differently depending on the platform:
Across all sources, the consistent truth: no business data values or rows are read, transmitted, or stored. What MyDataWork holds is structural metadata — names, paths, table identifiers, column identifiers, type names, and the inferred relationships between them.
For locally-scanned files, MyDataWork never reads, copies, stores, or transmits the contents of your files. The Windows Connector sends only names, paths, types, sizes, and modification dates to your workspace.
For cloud sources (GitHub blobs, Databricks notebook exports, dbt manifests, Snowflake INFORMATION_SCHEMA), MyDataWork DOES fetch and parse the source content to extract structural metadata — but only the extracted structure (system, topics, table identifiers, column names/types, dependency references) is stored in your workspace. The original fetched content is not retained.
All AI features — including the Asset Estate Assessment, Workspace Agent, Leverage analyses, and the in-app assistant — send only the stored structural metadata (asset names, paths, tool types, topic tags, lineage edges, column identifiers where surfaced) and your use case text (titles, objectives, descriptions, value figures) to the AI model. Stakeholder names and emails are stripped before any AI call — the model sees only a neutral role label (e.g. “executive sponsor”), never a person’s name or email. No business data values or rows are ever sent.
Demo data is fully synthetic. Loading demo from the wizard, the Setup tab’s “Load demo data” button, or the Dashboard’s empty-state button creates fabricated workspace data (assets, use cases, lineage) for exploration. It carries no real customer information and can be removed any time from Setup → Connect your data with no impact on the rest of your workspace.
MyDataWork includes lightweight statistical checks designed to help you present use case values you can stand behind in stakeholder conversations.
This approach is inspired by robust statistics, a field concerned with producing reliable estimates even when data contains outliers or errors.
MyDataWork can push use case summaries to Jira as new issues or comments on existing issues. This is useful when your organization uses Jira for project tracking and you need to keep it updated without maintaining two separate records.
https://yourcompany.atlassian.net) and your API token. For Jira Cloud, use email:api_token format. For Jira Data Center, use a Personal Access Token.MyDataWork works in mobile browsers with no app download needed. Once your desktop is set up, you can manage your portfolio from any device.
| Question | Answer |
|---|---|
| What plans are available? | MyDataWork offers a free Explorer plan and four paid plans. The Explorer plan is a 90-day free trial with no credit card required — 1 seat, 30 user-added assets, 3 AI credits per day with a 60-credit total cap. The paid plans are Solo Monthly ($20/month, 1 seat, 300 assets, 5 credits/day), Solo Annual ($192/year, same caps as Solo Monthly), Team Starter ($900/year, 2-5 seats, 2,000 assets, 20 credits/day), and Team Growth ($1,440/year, 6-10 seats, same caps as Team Starter). Paid plans include a 14-day free trial. Visit mydatawork.com or go to Account → View plans & billing for current pricing and plan details. |
| How do I upgrade? | Go to Account → View plans & billing, or visit /app/billing. Click Start free trial on any plan to begin. |
| How do I cancel? | Go to Account → View plans & billing → Manage billing. Monthly Solo plans continue until end of period. Annual plans are non-refundable. Canceling is different from deleting — see below. |
| What happens to my data if I cancel? | Your workspace data is retained for 30 days after cancellation. You can resubscribe during this period and resume where you left off. Contact contact@mydatawork.com to export during this period. This is different from account deletion, which removes all data immediately. |
| What’s the difference between canceling and deleting? | Canceling your subscription stops future charges but keeps your data for 30 days — you can resubscribe and pick up where you left off. Deleting your account permanently removes your account, workspace, and all data immediately with no recovery option. If you just want to stop paying, cancel your subscription — don’t delete your account. |
| Annual plan refunds on deletion? | Annual plans are non-refundable. If you delete your account mid-cycle, your subscription is canceled immediately and any remaining prepaid time is forfeited. |
| How do I add team members? | As the workspace admin, go to Setup → Members. Enter the team member's name, email, and a starter password. Share credentials with them directly. They should change their password on first login. |
| Problem | Solution |
|---|---|
| Setup file does not open on Windows | The installer is code-signed (publisher MyDataWork, LLC), so you normally won’t see an “unknown publisher” warning. If SmartScreen still prompts (it can briefly, for newly-signed software until reputation builds), click More info then Run anyway. You can also right-click the .exe and choose Run as administrator. |
| No files appear after setup | Wait 30–60 seconds and refresh. The first scan can take up to a minute for large folders. Make sure you selected the correct top-level folder during setup. |
| 'Another process is running' error | The Connector is already running from a previous install. The new installer will stop it automatically — click Next to continue. |
| Open file does not work | The Connector must be running in the background. Check Task Manager. If not found, go to Setup and reconnect your folders. |
| I need to add or change a folder | Go to Setup → click Connect folders. You can update your folder selection at any time without reinstalling the Connector. |
| "Connect folders" shows an error / the Connector is serving another workspace | A single Connector installation can serve multiple workspaces. Go to Setup → Connect Data, find the right-side Connector panel, and click Add this workspace to Connector. The Connector adds this workspace to its config and starts scanning here too — no reinstall needed. Once added, try Connect folders again. |
| I forgot my password | On the login page, click Forgot password? and enter your email. A reset link will be sent — it expires after 60 minutes. |
| I need to upgrade or manage my subscription | Go to Account → View plans & billing to start a free trial, upgrade, or manage your subscription. |
| Cloud source not syncing | Check that your API token has the correct permissions (repo read for GitHub, API access for dbt/Databricks). Click Scan now in Setup → Cloud Sources to trigger a fresh sync. |
| AI features not working | Go to Setup → AI Assist and confirm AI is enabled (status shows "enabled" in teal). If disabled, click Enable AI. Also confirm your daily quota has not been reached — usage is shown in the same section. |
| Automation candidates or marketplace recommendations not generating | Confirm AI is enabled in Setup → AI Assist. For marketplace recommendations, also confirm you have selected at least one cloud provider in Setup → Cloud Providers. |
| I shared an automation opportunity but want to share it again | Previously generated opportunities are saved in the archive section of the Leverage tab under "Identify automation candidates." Expand any past opportunity and use the Share button to send it again. |
| External dependencies not showing in lineage | The Lineage tab shows external-dependency ghost nodes automatically. In the Assets-tab lineage preview, enable the "Show external dependencies" toggle to show them there (default off). |
| Ghost nodes not appearing in exported lineage diagrams | In the Portfolio tab export section, check the "Include external dependencies in lineage" checkbox before clicking Export now. This option defaults to off to keep standard exports clean. |
| Migration Assist returns no results or an error | Confirm AI is enabled in Setup → AI Assist and that your daily quota has not been reached. If analyzing a use case, ensure at least one asset is linked to that use case — Migration Assist needs asset tool type information to generate recommendations. |
| 'Connector not found' on mobile | This is expected. Connectors only run on Windows desktop. You can still view all previously discovered assets, manage use cases, and export from mobile. Set up the Connector on a desktop first. |
| Small text or buttons on phone | Use landscape mode or a tablet for a better experience. MyDataWork is optimized for touch interfaces but detailed views work best in landscape. |
| How do I delete my account? | Go to Account settings and click “Delete my account” at the bottom. The deletion dialog includes an optional feedback form (reason and comments), then requires a confirmation checkbox. Your account and all workspace data will be deleted immediately and permanently — this cannot be undone. Active subscriptions are canceled automatically. Annual plans are non-refundable and remaining prepaid time is forfeited. Team admins must transfer ownership or remove all members first. Super-admin accounts cannot be self-deleted. After deletion you are redirected to the login page with a confirmation message. |
| Send to Jira button is grayed out | Jira has not been configured yet. Go to Setup → Integrations, enter your Jira instance URL and API token, and click Connect. Once connected the button becomes active on all use case Overview tabs. |
| Jira push fails or shows an error | Check that your Jira API token has permission to create issues and add comments in the target project. For Jira Cloud use email:api_token format. For Jira Data Center use a Personal Access Token. Verify your instance URL includes https:// and has no trailing slash. |
| I don't see the value quality checks or Portfolio distribution section | These features require a minimum number of use cases with non-zero values to activate — at least 4 for the per-field outlier hints and at least 3 for the Portfolio value distribution summary. With fewer use cases the checks run silently to avoid false positives. Add more use cases with estimated and realized values to see these features. |
| ‘Rate limit exceeded’ message | Some actions have daily or hourly limits to ensure system reliability: lineage rebuilds (5 per day), Jira pushes (20 per hour), exports (10 per day). Wait for the limit to reset or contact support if you need higher limits. |
The Windows Connector is required only for local file folders. Cloud sources (warehouses, repos, and BI & analytics tools) do not require the Connector.
Download Connector for WindowsStill stuck? Email contact@mydatawork.com. We aim to respond within 2 business days.