11 dashboards and reporting layers

The projects 11

Open a row for what was built, the details that mattered and the stack.

• Automated Power BI financial dashboardFinance. Data pipelines.

  A monthly finance pack that stopped being assembled by hand. The source data is pulled, transformed and refreshed on a schedule, and the report renders the current position without anyone opening a workbook.

  • Scheduled refresh against the accounting source, no manual export
  • Profit and loss, balance sheet and cash views from one model
  • Period comparison and variance computed in the model rather than per visual
  • Row-level security so each viewer sees their own entity

  Stack: Power BIDAXPower QuerySQL

• Reporting layer across a businessBusiness intelligence. Data pipelines.

  A reporting layer rather than a single report: a shared data model with consistent definitions, and the individual dashboards built on top of it, so two reports asking the same question return the same number.

  • One semantic model with metric definitions held centrally
  • Report set built on the shared model rather than per-report queries
  • Scheduled distribution to the people who will not open a portal
  • Definitions documented alongside the model

  Stack: Power BILooker StudioSQLBigQuery

• Business insights dashboard in Power BIBusiness intelligence. Data pipelines.

  An operating view for a management team: the measures that matter, at the grain decisions are taken, with drill-through from the summary to the transactions behind it.

  • Summary tiles that drill through to the underlying rows
  • Filters that persist across pages so a question survives navigation
  • Measures written once in the model and reused across pages

  Stack: Power BIDAXPower Query

• KPI dashboardBusiness intelligence. Data pipelines.

  A dashboard built around a defined set of indicators with targets attached, so a reading is against a bar rather than against nothing.

  • Each indicator carries its target and its trend
  • Period-on-period comparison in the model
  • Alert thresholds surfaced visually rather than buried in a table

  Stack: Power BIDAXSQL

• Customized KPI dashboard in Power BIBusiness intelligence. Data pipelines.

  A KPI dashboard fitted to one organization's own definitions rather than to a template, including the awkward measures that no standard report carries.

  • Metric definitions taken from the client's own policy documents
  • Non-standard measures implemented in DAX rather than approximated
  • Layout organized by team, so each audience finds its own section

  Stack: Power BIDAXPower Query

• Vendor performance dashboardProcurement. Data pipelines.

  A view of suppliers against the terms they agreed to: delivery timeliness, quality returns, price variance and responsiveness, ranked so a procurement conversation starts from the record.

  • On-time delivery measured against the agreed date, not the revised one
  • Quality returns and price variance attributed per vendor
  • Vendor ranking with the components of the score exposed

  Stack: Power BIDAXSQL

• Customer segmentation dashboardRetail. Data pipelines.

  A segmentation built from behavior rather than assumption: transaction history clustered into groups, then rendered as a dashboard where a marketer can see who is in each group and what changed since last period.

  • Clustering over recency, frequency and value from transaction history
  • Segment membership refreshed on schedule, not fixed at build
  • Migration between segments shown period on period

  Stack: Pythonscikit-learnPower BISQL

• Key metrics report in Looker StudioBusiness intelligence. Data pipelines.

  A shared report for a team that lives in Google Workspace: connected directly to the sources they already use, scheduled to their inboxes, with no new tool to log into.

  • Connected to Google Analytics, Sheets and BigQuery sources directly
  • Scheduled email delivery to stakeholders
  • Blended sources so one chart can span two systems

  Stack: Looker StudioBigQueryGoogle AnalyticsGoogle Sheets

• Power BI dashboard developmentBusiness intelligence. Data pipelines.

  Dashboard development as a delivery rather than a demo: the data model, the transformations, the measures and the report, handed over with the model documented so the client's own analyst can extend it.

  • Star schema modeled before any visual was placed
  • Transformations in Power Query rather than in the source
  • Measures documented so the model can be extended without the author

  Stack: Power BIDAXPower QuerySQL

• Analytics assistant over a business data setBusiness intelligence. Data pipelines.

  A question-answering layer over a company's own metrics: a plain-language question becomes a query against the warehouse, and the answer comes back with the numbers and the chart rather than as prose.

  • Natural-language question translated to SQL against a governed schema
  • Generated query shown alongside the answer so it can be checked
  • Result rendered as a table and a chart, not a paragraph
  • Schema scoped so the assistant cannot reach tables it should not

  Stack: PythonOpenAISQLBigQueryPlotly

• Bicycle chain performance visualization in RManufacturing. Data pipelines.

  Test data from a component testing program turned into a set of statistical graphics: distributions, wear curves and comparisons across product variants, produced as a reproducible script rather than a spreadsheet chart.

  • Reproducible plots generated from raw test output
  • Distribution and variance shown rather than averages alone
  • Variant comparison on shared axes

  Stack: RRStudioggplot2

More of the work

• 5 business systems, from ERP to point of sale

  Logistics, real estate, property, HR and hospitality. We start from the data model, because the joins between records are where these systems break.

• 10 AI assistants, from retrieval to agents

  Retrieval chatbots, a healthcare compliance assistant, a meeting copilot and agents that work inside the tools people already use.

• Every case study and project

  The full list, filterable by practice, industry and stack.