Portfolio Review Queue with Power BI: A 2027 Project Guide

The starting point for this Power BI exercise is a peer-review organizer working with review records. Their question is specific: What is the average time spent on completed portfolio reviews? Building a filter-aware dashboard around that question gives the project a manageable scope and makes the result possible to verify.

This is a 2027 study-edition project brief. All example records and operational situations are fictional. Start with basic Power BI knowledge, the ability to inspect structured data and a local practice environment. The complete first version should answer the stated question before adding accounts, real integrations or additional screens.

Define the project rule

Average completed review duration is an operations metric, not a judgement of the learner. A withdrawn review must not count as a completed assessment. For this exercise, one row represents one review. The stable key is record_id; project_category is the grouping or filtering label. The numeric review_minutes field uses minutes. Accepted states are queued, completed, withdrawn.

The summary selects status completed and applies mean to the selected numeric values. Keep this definition visible in the interface, response or report so another learner can reproduce it. Use positive finite values in the starter fixture and define any future zero or missing-value policy explicitly.

Choose the Power BI approach

State the grain and relationship direction before designing the visual. Distinguish a row-level calculation from a measure evaluated under filters. Build a small reconciliation table beside the chart while developing. Explain which records a slicer includes and how an empty selection behaves. Use synthetic data and check the documentation for the installed product version.

The three design decisions to explain in your project notes are:

• Model grain and relationships. Apply this choice to review records and explain its effect on review_minutes.
• Measure filter context. Apply this choice to review records and explain its effect on review_minutes.
• Slicer and total reconciliation. Apply this choice to review records and explain its effect on review_minutes.

Work through a small sample

The three records below are deliberately small enough to inspect by hand. They are a compact specification for the data shape and expected result, and can later be expanded into a larger synthetic fixture.

[
  {
    "record_id": "portfolio-reviews-01",
    "project_category": "Web Application",
    "review_minutes": 25,
    "status": "completed",
    "recorded_on": "2027-01-04"
  },
  {
    "record_id": "portfolio-reviews-02",
    "project_category": "Data Report",
    "review_minutes": 40,
    "status": "queued",
    "recorded_on": "2027-01-05"
  },
  {
    "record_id": "portfolio-reviews-03",
    "project_category": "Mobile Prototype",
    "review_minutes": 15,
    "status": "completed",
    "recorded_on": "2027-01-06"
  }
]

Only the first and third records match completed. Their review_minutes values are 25 and 15; the full-fixture result is 20 minutes, with 2 matching records. Selecting project_category = Web Application leaves the first record, so the result becomes 25 minutes, with 1 match. This provides two independent checks for the filter and aggregation.

The full-fixture output contract is:

{
  "matching_count": 2,
  "operation": "mean",
  "value": 20,
  "unit": "minutes"
}

Check the result by project_category

The overall figure answers the all-records version of the question. Grouping by project_category produces the following separate expectations, using the same completed rule. The middle group has a record in the source fixture but no qualifying observation.

Use this breakdown when answering the group-level question. Compare like-for-like units and keep the matching count visible, especially for averages. A different filter changes which records enter the calculation; it does not change the meaning of the metric.

The code blocks describe sample data and an expected result. Implement the surrounding feature using the installed version of your chosen tools, then save the actual run evidence separately from these expectations.

Build the feature in three passes

1. Inspect the data before charting

Check record_id uniqueness, project_category labels, review_minutes types and status values. Keep the supplied data as a raw input and create a separate cleaned representation. Record exclusions so the peer-review organizer can understand what entered the report.

2. Calculate a transparent metric

Restrict the analysis to completed entries and calculate the mean of review_minutes. Show both the result in minutes and the matching-record count. Reconcile the small fixture by hand before applying a chart, pivot or dashboard measure.

3. Present a reproducible answer

Use a project_category filter and label every unit. Show the empty-selection behaviour and explain the difference between this metric and a wider operational claim. Include the input version and transformation notes so another learner can rebuild the result.

Find the mistake before polishing the result

A useful defect to introduce deliberately is: Withdrawn reviews are assigned a fabricated zero duration. First reproduce it against the fixture, then identify the smallest rule or state transition that explains the mismatch. Fix that boundary and rerun the full-fixture and selected-label examples. This creates a stronger explanation than changing several unrelated parts until the screen looks right.

The scenario-specific acceptance check is: Leave withdrawn duration outside the completed-review average. Preserve before-and-after evidence. Add a missing project_category, a negative review_minutes, an unrecognised status and a duplicate record_id as separate invalid cases. Define the rejection or duplicate-handling policy before testing, and verify that invalid input does not silently change valid existing records.

Check the Power BI concerns as well:

• A slicer must filter the intended records.
• Relationships must not multiply the metric.
• A displayed total must reconcile with the definition.

For an empty filter match, the expected mean value is undefined (no matching observations). Keep matching_count at zero. This distinction prevents an empty dataset from being interpreted as a measured observation.

Explain the finished work in a portfolio

Show the peer-review organizer question, the record definition and the expected calculation first. Then demonstrate a project_category filter and the acceptance check. Include the fixture, setup instructions, chosen tool versions, actual test output and one remaining limitation in the repository. If you show a screenshot, pair it with the input that produced it. Another learner should be able to repeat the same result from a clean checkout.

For a focused extension, add a second project_category selection and compare the two groups using the same rule. Specify how missing groups appear and keep the original fixture as a regression case. Expand scope only after the current calculation, state handling and recovery path remain understandable.

Questions learners often ask

Which review records belong in the result?

Select records whose status is exactly completed and that satisfy the chosen project_category filter. Other states remain in the source data and can have separate summaries. Follow the domain rule above when a record changes state.

What should happen when no records match?

Treat the empty result as undefined and show a clear no-observations message. Keep the applied filter visible so the user can change it. Avoid using a loading indicator for a completed request that simply has no matching data.

How can I prove the review_minutes result is correct?

Reconcile the first and third sample records, then repeat the calculation for Web Application alone. Save both expected and actual outputs, and rerun the scenario-specific acceptance check after making a change.

Continue learning with Groot Academy

Use this exercise as a concrete discussion point when choosing a learning path. Browse the current Groot Academy course directory and ask about the current syllabus, learning mode and project practice. Course availability and batch details should be confirmed directly with the academy.

For implementation details, consult the official Power BI documentation matching the tools you install. This project defines an original practice brief; it does not depend on a forecast about 2027 software releases.

Continue with Peer Feedback Tracker using Power BI, or compare Portfolio Review Queue using Manual Software Testing. These companion practice guides are part of the 2027 learning collection.