Frequently Asked Questions

What is MyDataWork?

MyDataWork is a workspace for data analysts and teams both small and large to organize and share the files, workflows, dashboards, notebooks, and SQL behind their data work — and connect that work to the business outcomes it supports. It brings together asset cataloging, use case tracking, lineage visualization, AI-assisted recommendations, portfolio reporting, and integrations with tools like Jira — all in one place built specifically for how analysts actually work.

Who is it for?

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.

Is MyDataWork a data catalog?

Not in the traditional sense. Enterprise data catalogs are typically IT-managed platforms designed to inventory data sources, enforce governance policies, and serve data engineers and platform teams. MyDataWork is analyst-facing and workflow-oriented — it organizes the work analysts do with data, not the data infrastructure itself.

That said, MyDataWork is highly complementary to data catalog investments. Where a catalog answers “what data exists and where does it come from,” MyDataWork answers “what are analysts doing with that data, why does it matter, and what value is it generating.” Used together, they complete the picture from raw data asset to business outcome.

Is MyDataWork just a note-taking, knowledge management, or project tracking tool for analysts?

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.

How it differs from personal knowledge management tools: Personal knowledge management tools are built around the idea that the individual should own and organize their own working context, which is a philosophy MyDataWork shares. But the similarity mostly ends there. 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. You do not write “I have a file that references this database table.” MyDataWork already knows.

How it differs from project trackers like Jira: Project trackers are built around tasks, tickets, sprints, and software development workflows. They are excellent at tracking what needs to be done and who is doing it. What they do not understand is the file-level context behind data work — which Excel models feed which dashboards, which SQL scripts power which reports, which Alteryx workflows support which business decisions. A Jira ticket can record that an analyst is “working on the quarterly forecast” but it cannot tell you which files are involved, what data sources they depend on, whether those dependencies are tracked or invisible, or what the measurable business outcome of that work is. MyDataWork captures all of that automatically from the files themselves. It also integrates with Jira directly — analysts can push structured use case summaries to Jira issues with professional templates and progress updates, so teams using Jira for organizational tracking do not have to choose between the two.

How it differs from collaborative documentation platforms: Documentation platforms like wikis and knowledge bases are built for writing and organizing text — pages, articles, meeting notes, process documentation. They are valuable for institutional knowledge but passive by nature: someone has to write everything, keep it current, and remember to link things together. MyDataWork is active — it detects relationships between files automatically, flags external dependencies that are not yet tracked, surfaces outliers in reported values, and uses AI to identify automation opportunities and modernization options. The context it captures is a byproduct of the analyst’s actual work, not a separate documentation effort on top of it.

What MyDataWork is actually competing with: Most analysts have no system at all — no asset catalog, no use case tracker, no outcome record, no portfolio. They manage context in spreadsheets, email threads, and institutional memory that walks out the door when someone changes roles. MyDataWork is designed for that majority: analysts who want something that works immediately, understands their toolchain, connects their work to business outcomes, and helps them communicate their value without becoming a project in itself. For teams already using Jira or documentation platforms, MyDataWork is not a replacement — it is the analyst-facing layer that those tools were never built to provide.

How does MyDataWork support IT and data platform investments?

This is one of the most important and undersold benefits of MyDataWork. Data platform teams — those who manage data warehouses, BI tools, ETL pipelines, and governance platforms — are frequently asked to justify the cost and complexity of the systems they operate. MyDataWork helps answer that question directly.

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. That information — aggregated across a team or organization — tells a compelling story: which systems are actively used, what decisions they inform, what outcomes they produce, 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 in ways that top-down inventory tools cannot.

Does it replace our existing tools?

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, connect to them directly, 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.

Can it help with onboarding new team members?

Yes — significantly. One of the most common pain points for data teams is knowledge transfer. When an analyst leaves or a project changes hands, the context behind recurring work — which files matter, why they exist, what decisions they support, what to watch out for — 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.

What kinds of assets can MyDataWork organize?

MyDataWork supports any file-based data asset your analysts work with, including Excel and CSV files, SQL scripts, Python notebooks, Alteryx workflows, Tableau and Power BI workbooks, ThoughtSpot TML files, Dataiku DSS project exports, Looker LookML files (.view.lkml, .model.lkml, .dashboard.lkml), 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.

Can I connect my own files?

Yes. The MyDataWork Connector is a lightweight Windows application that indexes the files on your computer or network drive. It reads file names, paths, sizes, modification dates, and creation dates — never file contents — and syncs that information to your workspace. You can then add context, tags, use cases, and relationships on top of your real working files.

How does smart file filtering work?

MyDataWork automatically filters files to help you focus on current work while keeping everything accessible. The system provides four filter options: Current (30 days) is the default view showing files modified or accessed recently — your active work; Recent (30-90 days) displays files from 1-3 months ago that aren’t stale but not current; All files provides complete catalog access with no filtering; and Archived (90+ days) contains historical files not recently updated. This intelligent categorization ensures you see the most relevant files first while maintaining access to your complete asset history.

How does progress tracking work?

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 (where things stand now), and a target (the goal you are working toward). MyDataWork calculates progress automatically and displays 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, before the work changes anything. Record it as soon as you create a use case. The current value is updated as work progresses, and the progress bar recalculates automatically each time.

Progress is expressed using professional value measurement templates that go beyond monetary estimates: Time Savings (hours per week, days to completion), Quality Improvement (error rates, accuracy scores), Efficiency Gains (automation percentage, throughput), Risk Reduction (incident rates, compliance scores), and Cost Efficiency (cost per unit, resource utilization). Each template provides recommended units of measure, best practice guidance for baselines and targets, realistic improvement ranges, and industry-standard measurement approaches.

The result is a live, honest record of where a deliverable stands — ready to share at any moment without reconstruction or estimation.

How does use case lifecycle management work?

MyDataWork provides complete project lifecycle management with three distinct actions: Archive provides soft delete functionality that removes use cases from active view while preserving all data and history; Restore brings archived use cases back to active status; and Delete offers permanent removal for completed or abandoned projects. Users can switch between Active and Archived tabs in the Use Cases section to manage the full lifecycle. This system is particularly useful for completed projects you want to keep for reference, seasonal use cases that aren’t currently relevant, or maintaining a clean active workspace while preserving institutional knowledge.

Where do I see workspace health at a glance?

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

• Use case staleness — distribution of your active use cases across freshness buckets (under 30 days / 30–60 / 60–90 / 90+).
• Portfolio totals — estimated vs realized value across active use cases, plus the realization rate. Use cases with no linked assets are excluded from this total (and from the value chart) so the headline number reflects measured work; the subtext “Includes N of M active use cases” appears when any are excluded.
• Stakeholder coverage — how many of your active use cases have an assigned stakeholder.
• Value distribution — realized values plotted against the workspace mean and median (the robust-statistics check that also powers the Portfolio outlier hint).
• Tool distribution — asset count by tool family.
• Workspace Agent activity — suggestions surfaced and acted on in the last 14 days.

The Dashboard reflects what you see on the Assets tab — your own assets plus any shared to the team — so the numbers always reconcile with the rest of the app. Admins who need to see the full workspace catalog (across all team members’ private assets) use the Workspace view on the Assets, Use Cases, and Suggestions tabs.

When demo data is loaded, a small demo / real toggle in the upper-right lets you flip 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, Suggestions, or the Assessment.

What is the Asset Estate Assessment?

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. You can re-run the assessment any time afterward; subsequent runs use AI credits like other AI features. The free-first-run affordance is keyed by a SHA-256 hash of your email address (the plaintext email is not stored in the eligibility log), so if you’ve used your free run in one workspace, a second one in a new workspace under the same email is routed to the paid path.

What it produces: a five-section report — Estate overview, Connections worth making explicit, External dependencies, Use case opportunities, and Health observations — plus a Concrete next actions list ranking the top items. Exportable to PDF, with the workspace name, who it was generated for, and the timestamp on the export.

What it reads: asset metadata (names, paths, tool types, topic tags, lineage edges) and use case text (titles, objectives, descriptions, value figures, stakeholders) only. File contents are never read — the same boundary that applies to every other AI surface in the app.

When to run it: after you’ve connected real assets and configured at least 2 active use cases (the assessment is use-case-driven; with fewer than 2 active use cases the Run button is disabled). If you run with fewer than 10 real assets you’ll see a soft-floor confirmation; you can still proceed, but thin estates produce thin output.

Demo path: preview the report format any time without using your free run — the View demo assessment link renders a curated example using the same template the real run produces.

Workspace-grounding filter: every finding names a specific asset, use case, or stakeholder from your workspace. Generic template phrasings (“consolidate your KPI dashboards”, “merge your forecasting models”) are dropped during synthesis if no workspace-specific evidence backs them — so the assessment reads like a senior analyst who has audited your estate, not a checklist of generic advice.

How does MyDataWork help ensure my reported values are accurate?

MyDataWork includes a sophisticated data quality layer that 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 entered, if any estimated or realized value is significantly higher or lower than the rest of your workspace, a gentle amber prompt appears asking you to confirm the figure before you share it with stakeholders.

The Portfolio tab also compares the mean and median realized value across your use cases — once you have at least 3 use cases with realized values, when these figures diverge significantly it signals that one use case may be disproportionately affecting your portfolio total.

These checks are informational only and never block you from using the app. They are designed to help you walk into stakeholder conversations with numbers you are confident in, not numbers that were accidentally inflated or understated.

This approach draws on the principles of robust statistics, a field concerned with producing reliable estimates even when data contains outliers or errors.

Can MyDataWork show data sources that my assets depend on but that aren’t in my catalog?

Yes. In the Assets tab, any data source detected in a file that does not match a cataloged asset is flagged with an amber “Not in catalog” badge next to the data source name. This makes invisible dependencies visible — for example if a SQL script references a database table that is not tracked in your workspace, you will see it called out immediately.

The Lineage tab also surfaces these as ghost nodes — dimmed, dashed nodes representing external dependencies. Enable “Show external dependencies” above the lineage visualization to see them. The Assets tab lineage preview has its own independent toggle for the same purpose.

When exporting your portfolio, you can optionally include external dependencies in the lineage diagrams by checking “Include external dependencies in lineage” in the export options. This is useful when sharing with IT teams or stakeholders who need to understand the full dependency picture.

How do AI credits work?

MyDataWork uses a daily credit system for AI-powered features. Each account receives credits based on their plan: Explorer 3 credits per day with a 60-credit total cap across the 90-day trial; Solo plans 5 credits per day; Team plans 20 credits per day.

AI features that consume credits include use case suggestions (1 credit per suggestion), AI recommendations and analysis, leverage analysis modes, the MyDataWork Assistant, and the Workspace Agent (Analyze). The Workspace Agent draws from the same daily AI credit pool as other AI features — 1 credit per suggestion it phrases, with a ceiling of 8 credits per Analyze run (reached only if all six rules find candidates). A user’s first Analyze run is complimentary. See “What does the Workspace Agent do?” below.

You can purchase additional credits if needed: 10 credits for $5, 25 credits for $10, or 50 credits for $15. Credits reset daily and unused credits don’t roll over. Your first AI use case suggestion is free as a one-time trial.

What AI-powered features does MyDataWork include?

MyDataWork includes comprehensive AI-powered features that consume daily credits:

Use Case Suggestions (1 credit each) analyze your workspace and asset patterns to generate specific, actionable use case recommendations based on your actual files and workflows.

Leverage Analysis Modes: Find reuse opportunities identifies where your use cases overlap and where work could be consolidated across projects or team members. Identify automation candidates analyzes your use cases and assets to find business processes that could be candidates for intelligent automation, with estimated value, complexity ratings, and suggested next steps. Discover marketplace data recommends external datasets available on your configured cloud platforms (AWS, Google Cloud, Snowflake, Azure, Databricks) that could enrich your use cases. Migration Assist analyzes individual assets or complete use case workflows to identify opportunities to migrate or modernize tools, with effort estimates, expected benefits, confidence ratings, and concrete next steps.

AI Recommendations analyze your use case descriptions and linked assets to surface improvement ideas grounded in your actual work context.

Workspace Agent is a proactive observer that scans the workspace on demand and surfaces six categories of state worth your attention. It draws from the same daily AI credit pool as the other AI features (1 credit per suggestion phrased, ceiling 8 per run, first run complimentary). It surfaces — missing stakeholders, recently-added assets not linked to a use case, removed assets still in use, stale use cases, high-value use cases tracking below estimate, and assets serving many use cases. See “What does the Workspace Agent do?” below for details.

MyDataWork Assistant is a context-aware chat assistant available on every screen via the teal button in the bottom-right corner. It understands your workspace and current location in the app, providing specific guidance rather than generic help.

What does the Workspace Agent do?

The Workspace Agent runs on demand from the Suggestions tab. Click “Analyze” and the agent checks six things across your workspace and surfaces what’s worth your attention, grouped into three categories.

Cleanup (patient, not urgent): use cases that have assets and progress but no stakeholder assigned, and active use cases that haven’t been updated in 90+ days.

Activity (something changed): recently-added assets (last 14 days) that aren’t linked to any use case yet — with a suggested best-fit use case based on keyword overlap. Also: assets you removed within the last 30 days that are still referenced by an active use case or a lineage edge, so you can spot broken dependencies that followed from the removal.

Insight (strategic observation): active use cases with high projected value and low realized value (less than 25% of estimate) that haven’t been updated in 60+ days, and assets that are linked to three or more active use cases (the “hidden infrastructure” of your workspace where governance attention is warranted).

Each suggestion includes a one-click deep-link to the relevant asset or use case. You can dismiss with a reason (not relevant, already handled, revisit later), give 👍/👎 feedback, and the agent auto-resolves a suggestion when its condition no longer applies — for example when you assign the stakeholder, link the asset, or restore the removed file.

Your first Analyze run is complimentary. Subsequent runs draw from your daily AI credit pool — 1 credit per suggestion the agent phrases in natural language. A single run is capped at 8 credits, the ceiling reached only if all six rules find candidates at once; in practice most runs consume far fewer credits because most rules don’t find candidates. The exact cost ceiling for the next run is shown above the Analyze button before you click.

Can I link external notes (Google Doc, Notion, Confluence) to a use case?

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 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, so you can see at a glance which use cases have external notes attached.

Can MyDataWork help me identify automation opportunities in my data work?

Yes. The Leverage tab includes an “Identify automation candidates” mode that uses AI to analyze your use cases and assets and surface specific processes that could be candidates for intelligent automation — where an AI agent could potentially handle steps you currently do manually.

Each opportunity includes a description of what could be automated, which of your assets are involved, an estimated value and complexity, and a concrete suggested next step. You can share any opportunity via email to a colleague or stakeholder directly from the app. All identified opportunities are saved in the app so you can revisit, review, and share them at any time without regenerating them.

Can MyDataWork recommend external data sources to improve my work?

Yes. The Leverage tab includes a “Discover marketplace data” mode that uses AI to recommend external datasets available through your cloud providers that could enrich or improve your use cases.

To use this feature, first go to Setup → Cloud Providers and select the cloud platforms your organization uses (AWS, Google Cloud, Snowflake, Microsoft Azure, Databricks). MyDataWork will then tailor its recommendations to datasets available on those specific platforms.

Recommendations include the dataset name, which platform it is available on, why it is relevant to your specific use cases, and where to find it in the relevant marketplace. Recommendations are saved so you can review them across sessions and dismiss ones that are not relevant.

Can MyDataWork help me evaluate whether to migrate or modernize my data tools?

Yes. The Leverage tab includes a Migration Assist mode that uses AI to analyze your assets or use cases and identify opportunities to migrate or modernize tools. For example, it might recommend moving a complex Excel model to Python, replacing an Alteryx workflow with dbt, or converting ad-hoc SQL scripts into structured dbt models.

You can analyze individual assets — selecting files grouped by tool type — or analyze a full use case to evaluate all its linked assets together as an end-to-end workflow. This is useful when you want to assess whether an entire pipeline could be modernized rather than evaluating files in isolation.

Each recommendation includes the current tool, the recommended alternative, migration effort (Low, Medium, or High), estimated benefit, key risks, and a concrete suggested next step. A confidence rating is included with each recommendation — lower confidence results include an explanatory note, for example when recommending a newer or less widely documented tool. Results are saved so you can revisit and share them via email at any time.

How do I export and import my 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 estimates, target dates), communication logs, saved AI recommendations, and action plans. Import merges a JSON file into your current workspace — existing items are updated by matching on path (assets) or title (use cases), new items are added. Nothing is deleted during import. Plan asset limits are enforced on import: if the import would push you over your plan’s cap, it is rejected before any changes are made. Lineage edges are imported directly and do not require a manual rebuild.

How does MyDataWork show who owns each asset in a team workspace?

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 indicating which team member originally connected that asset — whether via the Windows Connector or a cloud source.

For assets connected via cloud sources (GitHub, dbt, Databricks, Snowflake), the connecting user is always recorded. For assets connected via the Windows Connector, the user who generated the setup code and installed the Connector on their machine is recorded as the asset owner.

How do Team plan workspaces handle sharing and collaboration?

MyDataWork Team workspaces use a shared bulletin board model. Each team member has their own personal workspace where their assets are private by default. When a member is ready to share an asset with the team, they click “Share to Team” on the asset’s detail panel — it appears in the team’s Shared tab where other members can browse it. If a colleague finds it useful, they can click “Copy to mine” to bring an independent copy into their own workspace.

Each member’s workspace stays their own. Use cases, lineage, stakeholders, AI recommendations, and Insights all remain personal — only assets are shareable. The person who shared an asset (or the team admin) can unshare it at any time; copies others have already made are unaffected.

This model lets team members connect their full working folders without worrying about exposing drafts or work-in-progress, while still making it easy to surface and reuse the work that’s ready for the team.

How do Team plan admins manage their workspace members?

Team plan workspaces have a single admin (the Owner) who manages the team. Admins can view all members, add new members, remove members, and transfer admin ownership — all from the Workspace management view in Setup.

Adding members: The admin creates member accounts directly by entering the new member’s name, email, and a starter password they will share with the member. There is no email invite flow. After receiving the credentials, members log in and can change their password from the Account page. Seat limits apply based on plan (2-5 for Team Starter, 6-10 for Team Growth, with the admin counting as one seat).

Transferring admin ownership: Admins can transfer ownership to any current member using the Transfer admin action on that member’s row. After transfer, the previous admin becomes a regular member in the workspace.

Removing members: Admins can remove any member from the workspace. The removed member’s assets, use cases, and other content remain with the workspace — only the member’s access is revoked.

How do Team plan admins see what their team is doing without surveilling individuals?

Team admins have access to a Team Metrics panel in the Workspace management section of Setup. The panel shows aggregate adoption metrics across the team:

• Summary stats: total assets across the team, how many have been shared to the team bulletin board, total copies made, sharing rate (shared assets ÷ total assets), and reuse multiplier (average copies per shared asset)
• Most-reused shared assets: the top shared assets by copy count, with attribution to the member who shared them
• 30-day activity timeline: shares and copies per day so admins can see momentum over time

Per-member breakdowns are intentionally not provided. The Team Metrics panel gives admins overall adoption visibility without surfacing individual contributor behavior. Members keep their autonomy; admins get the adoption story.

What happens if our Team admin leaves the company?

If the admin of a Team workspace leaves without first transferring admin to another member, the workspace can become orphaned with no one able to manage members or billing. If this happens, email contact@mydatawork.com and we can reassign admin access to another member. To avoid this situation, admins should transfer ownership before they leave or are offboarded.

Can MyDataWork push updates to Jira?

Yes. MyDataWork includes a comprehensive Jira integration that lets you create structured tickets and push progress updates directly from your use cases. The integration is available on all paid plans (Solo and Team). It includes professional templates for different project types: automation projects, reporting enhancements, and data quality initiatives. Each template creates structured tickets with sections for objectives, stakeholders, linked assets, acceptance criteria, and a direct link back to your MyDataWork workspace.

You can also push progress updates to existing Jira tickets with current measurements, value tracking, and latest progress notes. The system can read current ticket status, assignee, and priority directly in MyDataWork for complete workflow visibility.

To set it up, go to Setup → Integrations and enter your Jira instance URL and API token. For Jira Cloud use your email and an API token generated at id.atlassian.com/manage-profile/security/api-tokens. For Jira Data Center use a Personal Access Token.

The integration is one-way — MyDataWork sends to Jira, Jira does not write back. Your MyDataWork workspace remains the source of truth for your data work.

Can I use MyDataWork on my phone or tablet?

Yes, MyDataWork works fully in mobile web browsers (Safari, Chrome, etc.) for viewing and managing your analytical assets. You can view your analytical portfolio and executive dashboard, browse discovered assets and their details, check use case progress and metrics, export portfolio summaries, review asset lineage and connections, and purchase AI credits and manage account settings.

Desktop-only features: Initial connector setup (requires Windows Connector), asset discovery scans (automated scans run from desktop), and file system browsing and selection.

How do I set up connectors on mobile?

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

Can I discover new assets from my phone?

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

What’s the best mobile experience?

For optimal mobile experience, use landscape mode on tablets for viewing detailed asset information and lineage diagrams. Portrait mode works well for portfolio overviews and use case tracking.

How is MyDataWork priced?

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 connection, 3 AI credits/day with a 60-credit total cap across the trial. No credit card required. Default for new self-service signups.
• Solo Monthly: $20/month — 1 seat, 300 assets, 5 AI credits/day, individual workspace
• Solo Annual: $192/year — 1 seat, 300 assets, 5 AI credits/day, save vs monthly
• Team Starter: $900/year — 2-5 seats, 2,000 assets, 20 AI credits/day, shared workspace
• Team Growth: $1,440/year — 6-10 seats, 2,000 assets, 20 AI credits/day, advanced team features

All paid plans include a 14-day free trial with no charge until the trial ends. The Explorer plan is a separate free 90-day offering — see the next entry for the distinction. AI credits can be purchased separately as needed: 10 credits/$5, 25 credits/$10, 50 credits/$15.

What’s the difference between the Explorer plan and the 14-day free trial?

They’re two distinct offerings:

• Explorer plan is our 90-day free workspace for individuals. No credit card required. 1 seat, up to 30 user-added assets, 1 cloud source connection, 3 AI credits per day with a 60-credit total cap across the 90-day trial. Designed for an individual practitioner to build their working practice over time. New self-service signups are placed on Explorer by default.
• 14-day free trial applies to our paid Solo and Team plans. It’s the standard “try before you’re billed” trial for the paid tiers. After the 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.

How do I get access?

You can register directly at app.mydatawork.com and select a plan. All plans include a 14-day free trial so you can explore all features before your trial period ends.

What’s the difference between canceling my subscription and deleting my account?

These are two different actions with very different outcomes — it’s important to choose the right one.

Canceling your subscription stops future charges and ends your paid access. Your workspace data is retained for 30 days so you can resubscribe anytime during that period and pick up where you left off. Cancel via Account → View plans & billing → Manage billing to access your Stripe customer portal.

Deleting your account is immediate and permanent. Your account, workspace, and all associated data (assets, use cases, stakeholders, progress history) are removed right away. There is no retention period and no recovery option. Delete your account from Account → Delete my account.

If you simply want to stop paying, cancel your subscription. Delete your account only if you want to permanently remove yourself and your data from MyDataWork.

How do I delete my account?

You can delete your account yourself from the Account page. At the bottom of the Account section, click “Delete my account.” A confirmation dialog explains the consequences — deletion is immediate and permanent, your data is not retained, and your subscription (if any) is automatically canceled. Complete the confirmation checkbox and click Delete my account.

Important requirements:

• Super-admin accounts cannot be deleted via self-service.
• If you are a Team admin with other members in your workspace, you cannot delete your account until you transfer admin ownership to another member or remove all other members. This prevents workspaces from being orphaned.
• Active annual subscriptions are non-refundable — deleting mid-cycle forfeits the remaining paid time.

You are also invited to share optional feedback about why you’re leaving. This helps us improve the product.

Can I get a refund on my annual plan?

Annual plans are non-refundable. If you cancel an annual subscription, you retain access until the end of the current annual period, then lose access (with 30-day data retention). If you delete your account mid-annual-cycle, the subscription is immediately canceled and the remaining prepaid time is forfeited with no refund.

If you are on a monthly plan, you can cancel anytime; you’ll retain access through the end of the current billing period, and no future charges will be made.

What happens if I hit the AI credit limit?

AI credits reset daily — Solo plans get 5 credits per day, Team plans get 20 credits per day. If you run out, you can purchase additional credits (10 for $5, 25 for $10, or 50 for $15). Purchased credits are available immediately and carry over until used. You can also wait for your daily credits to reset the following day.

What happens if I hit the lineage rebuild or export limits?

Some features have daily or hourly rate limits to protect platform resources: lineage rebuilds are limited to 5 per day, Jira integration calls are limited to 20 per hour (shared across push and progress updates), and exports are limited to 10 per day. If you hit one of these limits, you’ll see a rate limit message and can retry after the limit resets. Normal workflows rarely hit these limits — they exist primarily to prevent accidental runaway usage.

What if I have more questions?

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