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Data Roadmap Capacity and Skill Coverage

Plan the data team's quarter against real capacity and the skills each epic needs, and see readiness before a date is promised.

These images are illustrations of the concept, not screenshots of the actual product.

Overview

Data Roadmap Capacity and Skill Coverage is a PlanMagnet concept for heads of data and analytics leads who plan a quarter of data work. It is designed for the point after requests have been captured and merged in PlanMagnet's feedback inbox and duplicate review, so it is not another intake list: each accepted request becomes an epic that names the decision it informs, who owns that decision and the date it is needed, and each epic is checked against the team's capacity and the skills it depends on.

In data and analytics teams, the roadmap usually fills up faster than anyone checks who can do the work. A new metric model is promised for a date, and only later does the team notice that one engineer holds the modeling skill it needs and is about to leave. Requesters see a missed date, and the team sees a dependency it never wrote down. Tying each epic to the decision it serves also makes it clearer which work matters most when capacity runs short.

The illustrated Q4 data roadmap sits under Products, with filter chips for all epics, at risk, planned and in progress, and a count of requests accepted from intake. Tiles show analytics engineering capacity, decisions due this quarter, skill gaps and engineers on a learning path. The epics table lists each epic with the decision it informs, the decision owner, the needed-by date, the skills needed, coverage and status. Coverage counts the people who hold each skill: the selected at-risk epic, moving store reports to a metric model, has a single holder who is leaving, and another at-risk epic has only two holders of query tuning. A detail panel for the selected epic gives CrewFoundry capacity for a six-week range, the skill risk, a TechnoSpam learning path with two engineers partway through, mentor sessions booked with the current holder, a note that the start date is an estimate that moves with path progress, and actions to plan the epic into Q4 or notify the decision owner.

The concept builds on PlanMagnet's feedback inbox and roadmaps and is designed to work with other Burdenoff products. Capacity for each analytics engineer is designed to come from CrewFoundry, and a skill gap is designed to link to a TechnoSpam learning path and mentor sessions, with path progress passing back so the epic's start date moves with readiness. Questions Botlit could not answer are designed to arrive as requests through a FluidGrids workflow; one planned epic in the illustration is marked as coming from Botlit. The epics, people and figures shown are sample data.

What this concept shows

  • Filter chips for all epics, at risk, planned and in progress, with a count of requests accepted from intake
  • Tiles for analytics engineering capacity, decisions due this quarter, skill gaps and engineers on a learning path
  • An epics table that names the decision each epic informs, its decision owner and the date it is needed
  • A Coverage column that counts the holders of each needed skill and flags a single holder who is leaving
  • A detail panel with CrewFoundry capacity for a six-week range and the skill risk behind an at-risk epic
  • TechnoSpam learning path progress and booked mentor sessions shown against the skill gap
  • An estimate-only start date that moves with learning path progress
  • Plan into Q4 and Notify decision owner actions

How it works

  1. Start from requests accepted in the feedback inbox and duplicate review, which the roadmap counts as accepted from intake.
  2. Open the Q4 data roadmap under Products and read the tiles for capacity, decisions due, skill gaps and engineers on a learning path.
  3. Filter to at-risk epics and compare each decision owner and needed-by date with the Coverage column.
  4. Select an epic to see CrewFoundry capacity, the skill risk, TechnoSpam learning path progress and mentor sessions.
  5. Plan the epic into Q4 with a start date that moves with readiness, or notify the decision owner.

Who it's for

  • Heads of data
  • Analytics leads and managers
  • Analytics engineering managers
  • Data product owners
  • Business stakeholders who own data-informed decisions

Illustrations

1 illustration of this concept. Select one to view it full size.

Q4 Data Roadmap with Capacity and Skill Coverage

Sample data epics tied to decisions, with skill holders, CrewFoundry capacity and a TechnoSpam learning path.

This illustration shows a Q4 data roadmap under Products in PlanMagnet, with a selector and a Last 6 sprints period selector at the top right. Filter chips cover all epics, at risk, planned and in progress, beside a count of requests accepted from intake. Tiles show analytics engineering capacity, decisions due this quarter, skill gaps highlighted in amber and engineers on a learning path. The epics table lists epic, decision it informs, decision owner, needed-by date, skills needed, coverage and status. The selected at-risk row, moving store reports to a metric model, shows one semantic modeling holder who is leaving; another at-risk epic has two query tuning holders, and a planned epic is marked as coming from Botlit. The detail panel gives CrewFoundry capacity for a six-week range, the skill risk, a TechnoSpam metric modeling lab path with two engineers partway through and mentor sessions booked, an estimate-only warning on the start date, and Plan into Q4 and Notify decision owner buttons.

Topics

  • data team roadmap planning
  • analytics capacity planning
  • skill coverage matrix
  • data roadmap tool
  • analytics engineering capacity
  • key person risk on a data team
  • decision-driven analytics roadmap
  • skills gap planning
  • data request prioritization
  • analytics team resource planning

Part of an industry solution

This concept appears in a cross-product solution on burdenoff.com — see how it works alongside other Burdenoff products to solve a problem in that industry.