Video Library
Structure for the freedom that defines data work
Follow one use case from scattered assets to a legible, defensible piece of the portfolio: The whole estate in one home base — every asset grouped by the tool it lives in. Cataloged by metadata alone — never the file’s contents, and it never changes how you work. A use case the analyst defines: the business problem, the value targeted versus realized, and the assets and stakeholder behind it. Measurable progress toward the goal.
What you will learn
- What "structured freedom" actually means — adding structure to scattered analytical work without giving up the tools or flexibility that make it valuable.
- Why cataloging by metadata alone (never file contents) keeps work visible and portable while leaving how you work untouched.
- How framing work as a use case — business problem, stakeholders, value targeted vs. realized — turns a folder of files into a defensible piece of the portfolio.
- How lineage surfaces fragility, like uncataloged upstream sources, so you can see what breaks before it becomes an incident.
Meet MyDataWork: A Guided Tour
MyDataWork is a workspace that catalogs your data work and tracks the value it delivers. This tour loads a sample workspace — nineteen assets across seven tools, tied to six use cases — and walks through the pieces that make it work: the Asset Health Dashboard, where portfolio value, staleness, and stakeholder coverage sit in one view; an individual asset, catalogued by its metadata alone; a use case that connects assets to a measurable outcome; and the lineage map that shows how everything fits together. By the end you'll understand the shape of the product — what it holds, how it's organized, and where the value shows up — and how to start with the demo before connecting your own files and sources.
What you will learn
- What MyDataWork is and the problem it solves — one catalog of your data work, with the value it delivers tracked right alongside it.
- How the Asset Health Dashboard summarizes an entire estate: portfolio value (estimated vs. realized), staleness, and stakeholder coverage.
- How assets, use cases, and lineage connect — from a single metadata-only asset to the outcome it drives and the dependencies behind it.
- How to get started — load the demo to explore, then connect your own files and cloud sources.
Inside the Catalog: Every Asset, One Place
Every analytics estate is scattered across tools — SQL, Excel, Power BI, Tableau, Python, and more. MyDataWork pulls them into a single catalog, indexed by metadata alone and never by file contents. This video opens that catalog and shows what it captures: an asset's tool, path, and topics; the related assets it shares data with; and a lineage preview that, with one toggle, reveals the upstream warehouse tables a dashboard reads from — even when those sources live outside your files. It closes on how assets get there in the first place — the Windows Connector, which indexes local files by structure alone, and direct cloud connections made with read-only credentials — so that whatever tools you use, it all lands in one place.
What you will learn
- How MyDataWork catalogs assets across seven-plus tools by metadata only — never touching file contents.
- What an asset record actually holds — tool, path, topics, and the related assets it shares data with.
- How the lineage preview surfaces external dependencies — the warehouse tables behind a dashboard, even outside your files.
- The two ways assets get indexed — the Windows Connector for local files, and read-only cloud connections.
Use Cases: The Business Record Around Your Data Work
In MyDataWork, use cases are where data work connects to the outcomes it serves — the business record around the files, not just the files themselves. This video opens a sample use case and walks through the pieces that make it manageable: an Overview that captures status, estimated and realized value, and who owns it; Objectives & Progress, where a baseline, a current value, and a target turn an intention into a measurable outcome with progress calculated automatically; Assets & People, which ties the SQL, Tableau, and Python behind the work to the stakeholder who depends on it; and an Action Plan where next steps live — added directly or generated from the AI's recommendations, then checked off as work moves forward. Together they turn a folder of files into a portfolio of outcomes you can actually manage.
What you will learn
- What a use case is in MyDataWork — the business record that connects assets to a measurable outcome, with estimated and realized value tracked alongside it.
- How Objectives & Progress makes an outcome measurable — a baseline, a current value, and a target, with progress calculated automatically.
- How Assets & People connects the work to what powers it and who depends on it — the linked assets and the accountable stakeholder.
- How the Action Plan keeps work moving — next steps added directly or generated from the AI's recommendations, then checked off.
Making FP&A Modernization Visible, Measurable, and Expandable
MyDataWork follows a headcount and OPEX planning use case across Excel workbooks, CSV extracts, SQL queries, workflow logic, a variance model, and a Power BI leadership dashboard. The scenario shows how scattered planning assets can become a visible, measurable, and defensible piece of the analytics portfolio. The video highlights how MyDataWork helps teams connect planning work to business objectives, stakeholders, value targets, fragile dependencies, and future modernization opportunities — without replacing the tools analysts already use.
What you will learn
- How MyDataWork sits around FP&A modernization as the work-context layer — complementing a planning platform and a data platform, not replacing them.
- Why starting with the use case, not the spreadsheet, gives finance teams and consultants one shared, defensible frame for the whole effort.
- How lineage exposes the real risk: the most visible artifact (the leadership dashboard) depending on the least visible work (uncataloged GL tables).
- How targeted-vs-realized value turns one modernization into a roadmap of adjacent processes — CAPEX, revenue forecasting, project cost planning.
MyDataWork AI: Intelligence Built into the Work
MyDataWork's AI is shown across the places it works — turning a catalog of data work into a prioritized action plan. It is opt-in and admin-controlled, and it works from metadata and context alone: use-case descriptions, asset names, progress notes, lineage, value figures, and stakeholder roles — never file contents, with stakeholder names stripped before anything reaches the model. The video covers Propose Use Cases (what to track), the Asset Estate Assessment (what's happening across the workspace), the Workspace Agent (what changed), Leverage (where effort compounds), Use Case Recommendations (what to do next), the Assistant (help on any screen), and Agent Access over MCP (read-only context for the agents you choose) — a set of focused functions built into the workflow, not one chat box.
What you will learn
- Where MyDataWork's AI actually lives — focused functions, each answering a real question: what to track, what's going on, what changed, where effort compounds, and what to do next.
- How the AI stays trustworthy — opt-in, admin-controlled, metadata-and-context only, file contents never read, stakeholder names stripped before the model.
- How deterministic checks (the Workspace Agent) differ from generative analysis (Assessment, Leverage, Recommendations) — and why grounding matters.
- How Agent Access exposes read-only workspace structure to an AI agent you choose, over MCP — metadata only, nothing else.
The Workspace Agent: A Worklist That Builds Itself
Most of what needs attention in a data estate is easy to miss — a high-value use case drifting below its target, a new file that belongs to existing work, an asset quietly shared across three teams. The Workspace Agent watches for all of it. It runs automated checks across your estate and builds a worklist you didn't anticipate, sorting findings three ways: Insight, a strategic observation; Activity, something that changed and is worth a look; and Cleanup, the housekeeping that keeps the estate honest. Because its checks are rules rather than a model, every finding is grounded, never guessed — and each one is a single click to act on, not a report to read. The result is a proactive, always current list that turns "what am I missing?" into work you can just move through.
What you will learn
- How the Workspace Agent proactively monitors your estate and builds a worklist without being asked.
- How findings are organized — Insight, Activity, and Cleanup — so you know what kind of attention each needs.
- Why the checks are rule-based and grounded — findings reference your real assets and use cases, never guessed.
- How each finding is a one-click action — link a stakeholder, tie an asset to a use case, mark it critical, or dismiss.
Stronger as a Team: How Shared Assets Fuel Everyone's Work
A MyDataWork Team is one shared workspace where an analytics lead and their analysts each keep their own work — but stop duplicating each other's. This walkthrough steps into three separate accounts to show how that plays out. Maya, a sales and marketing analyst, shares her weekly sales actuals with the team in a single click. Across the aisle, Devin — a supply chain analyst — finds that dataset already in his own workspace, without asking or hunting for the latest copy, and links it to his forecasting use case. With that sales signal connected, his AI recommendations sharpen, proposing he rebuild his forecast baseline on Maya's actuals. Finally, back in the lead's account, Alex sees it come together in Team metrics — how much of the estate is shared and where — alongside a members view reserved for the admin. Use cases stay private to each person; the asset is what travels. That's shared inputs with independent ownership — how a Team compounds value, day to day.
What you will learn
- How asset sharing works in a Team — one analyst makes a dataset visible in a single click, and it appears in colleagues' own workspaces without copies or handoffs.
- How shared visibility fuels other people's work — a shared asset links straight into another analyst's own use case, and the AI gets sharper because of it.
- Why use cases stay private while assets travel — shared inputs, independent ownership (MyDataWork doesn't share use cases across people, by design).
- What the admin sees that members don't — Team metrics (how much of the estate is shared, plus the sharing trend) and the full members roster, reserved for the account lead.
Global CPG Demand Forecasting: From Disarray to an Ongoing System
A global Consumer Packaged Goods manufacturer's demand forecasting is scattered across spreadsheets, notebooks, and workflows — the kind of disarray that used to take weeks to untangle. This walkthrough shows how MyDataWork turns it into a plan you can act on. Reading metadata only, it catalogs the estate; its Propose function frames trackable use cases with estimated value; its AI generates concrete recommendations and flags a bounded automation candidate; the Workspace Agent surfaces ownership gaps; and — with Azure configured as your cloud — Discover marketplace suggests external demand signals from the Azure data marketplace, parked as a next step. The Estate Assessment pulls it all into one shareable report. Whether you're a consulting team scoping an engagement or an analyst inside the business, the result isn't a one-time deliverable — it's a living system that stays with the work and keeps surfacing what's next.
What you will learn
- How MyDataWork makes a scattered forecasting estate legible from metadata alone — no data or file contents ever read.
- How its AI turns files into action: Propose frames valued use cases, then it generates recommendations and flags a bounded automation candidate.
- How Discover marketplace, tailored to your configured cloud (e.g., Azure), suggests external demand signals to sharpen the forecast — parked as a future step.
- How the Estate Assessment becomes a shareable SOW/roadmap artifact, and how the whole thing stays live as an ongoing, self-serve system — for a consultant or an in-house analyst alike.
The Number You Take to the CFO: Robust Value Governance in MyDataWork
Every analytics team has to defend one number to leadership: what did all this work actually deliver? But roll a portfolio of use cases together and a single over-claimed value — a maintenance benefit logged gross when everything else is net — can inflate the total and make the whole report indefensible. This video shows how MyDataWork guards that number with robust statistics. Comparing the median realized value to the mean, it flags when one use case is skewing the portfolio total; on the use case itself, a median-based check (MAD) names the culprit and asks you to confirm the figure. The analyst reconciles it to a consistent basis, the flags clear, and the total becomes one you can defend line by line. MyDataWork doesn't measure the machine — your dashboards do that — it governs what the work was worth, on a consistent basis, using the median over the mean so a single over-claim can't distort the story you present.
What you will learn
- Why averages fail on value rollups — one outlier inflates both the mean and the standard deviation, hiding inside its own band — and why the median doesn't.
- How MyDataWork surfaces it: the Portfolio mean-vs-median divergence check, and a MAD-based outlier flag on the individual use case.
- Why the fix is about a consistent basis, not a typo — reconciling gross/net, attribution, and annualization so values compare apples-to-apples.
- How robust value governance produces a defensible number for the CFO, a steering committee, or a board — without touching the operational dashboards that measure the work itself.
Solution Packs: From a Blank Page to a Ready-Made Plan (FP&A Walkthrough)
Every analytics project starts with a blank page — Solution Packs give you a running start. In this short walkthrough, watch how activating the Financial Planning & Analysis pack instantly creates a set of best-practice starter use cases, already grouped under an Initiative so your workspace is organized from the first click. See how to tailor each starter — edit its objective and value, attach a notes link for context, or send it straight to Jira — then filter your Portfolio to the initiative and export a stakeholder-ready deck covering exactly that program.
What you will learn
- Where to start — find Solution Packs in Setup and pick the pack that matches your work
- Tailor, don't accept a dump — choose only the topic areas you need (nothing is pre-selected)
- Organized automatically — starters land grouped under an Initiative named for the pack
- Make it yours — edit the pre-filled objective and value, link your notes or supporting docs, and push any use case to Jira
- Report in one click — filter your Portfolio to the initiative and export a scoped PowerPoint or PDF of just those use cases
The Decision Record: Supply Network Optimization
A strategic analytical decision — like a facility-location or supply network optimization study — is rarely one model; it's the data, the assumptions, the people, and the proof around it. In this short walkthrough, watch how MyDataWork captures that whole study as a single use case — linking the Alteryx workflows, spreadsheets, SQL extracts, and dashboards behind the recommendation, mapping their lineage from inputs to model to result, and naming the stakeholders who supplied each input. See how to track estimated versus realized value, surface AI recommendations grounded in your own assets, and discover external datasets — freight, geospatial, demand — that could sharpen the model and change the answer. Then hand the whole context to an AI agent over MCP, group related studies into an Initiative, and export a decision-ready summary. The example is supply network optimization, but the pattern fits any complex analytical decision.
What you will learn
- Capture a whole analytical study — models, data, assumptions, and people — as one reusable decision record, not scattered files.
- Link the assets and stakeholders behind a recommendation, and trace the lineage from inputs → model → result.
- Get AI recommendations grounded in your own assets — and discover external datasets that could sharpen the model.
- Hand the full context to an AI agent over MCP, then export a decision-ready summary.