PA Data Analytics
Dashboards & Reporting

Power BI dashboards and business reporting your team will actually use

When reporting means copying numbers between spreadsheets every week, decisions wait and figures disagree. PA Data Analytics designs and builds Power BI dashboards, Excel reporting systems and automated reporting pipelines that bring the right KPIs together in one view your team can trust.

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Data → Insights → Decisions

The questions dashboards & reporting answers

  1. What happened?

    Where do you stand today against targets, by channel, product, region or programme?

  2. Why did it happen?

    Which drivers explain the change? Drill down from the headline number to the detail behind it.

  3. What should happen next?

    Which KPIs need attention this week, and who is responsible for the next action?

What we deliver

Dashboards & Reporting services

Power BI dashboard development

Executive and operational dashboards with a clean data model, DAX measures and drill-through from the KPI overview to campaign, product or customer level.

  • Power BI
  • DAX
  • Power Query

Executive & KPI dashboards

A focused set of agreed KPIs, such as revenue, orders, ROAS, customer metrics or programme indicators, shown against targets and trends.

  • Power BI
  • Excel

Excel dashboards & reporting systems

Structured Excel or Google Sheets trackers with validated formulas, designed for non-technical teams to maintain.

  • Excel
  • Google Sheets

Data cleaning & transformation

Cleaning, deduplicating and restructuring messy source data with SQL, Power Query and Python, so reports start from reliable numbers.

  • SQL
  • Python
  • Pandas

Reporting automation

Python and SQL pipelines that produce weekly and monthly reports from raw data and flag KPIs that cross agreed thresholds.

  • Python
  • SQL

Looker Studio & GA4 reporting

GA4-connected dashboards for web and marketing performance, combined with ad and sales data where available.

  • Looker Studio
  • GA4
  • BigQuery

For NGOs and impact organisations

Impact reporting and donor dashboards, monitoring and evaluation (M&E) indicator tracking, programme performance trackers and cleaning of beneficiary and donor data, so funder reports are consistent and the numbers can be traced back to their source. Discuss an NGO reporting project.

Why it matters

What it helps your business do

  • One agreed set of KPIs instead of competing spreadsheets
  • Hours of manual reporting replaced with scheduled refreshes where possible
  • Faster answers to follow-up questions through drill-down
  • Reporting your team can maintain after handover
Typical deliverables

What you receive

  • KPI definitions agreed with your team
  • Cleaned and modelled data sources
  • Power BI, Excel or Looker Studio dashboard
  • Refresh and maintenance documentation
  • Handover session for the people who will use it

Scope depends on your data and question. It is agreed after the discovery call and data audit.

Proof of work

Related case studies

Portfolio projects built on demonstration datasets. They are not client engagements, and their figures describe the datasets and models, not real business results.

USERS AT EACH FUNNEL STEPProduct viewProduct view: 61,252 users61,252Add to cartAdd to cart: 12,545 users12,545Begin checkoutBegin checkout: 9,715 users9,715Shipping infoShipping info: 9,714 users9,714Payment infoPayment info: 5,751 users5,751PurchasePurchase: 4,419 users4,41920.5% of viewersadded to cart40.8% drop-offat payment stepPortfolio project · Public GA4 sample dataset
  • Portfolio project
  • Public dataset
  • Marketing Analytics

E-commerce funnel and commercial performance analysis (GA4)

A portfolio analysis of Google's public GA4 sample e-commerce data that locates funnel drop-offs and compares performance by device and traffic source.

Public dataset: 270,154 users · 354,970 sessions · $362,165 revenue

Read the e-commerce funnel case study
RETENTION BY COHORTOct 232023-10 cohort, month 1: 39% retained2023-10 cohort, month 2: 22.9% retained2023-10 cohort, month 3: 18.5% retained2023-10 cohort, month 4: 17.1% retained2023-10 cohort, month 5: 15.1% retained2023-10 cohort, month 6: 10.2% retained2023-10 cohort, month 7: 14.6% retained2023-10 cohort, month 8: 10.7% retained2023-10 cohort, month 9: 12.2% retained2023-10 cohort, month 10: 10.2% retained2023-10 cohort, month 11: 7.3% retained2023-10 cohort, month 12: 8.3% retained2023-10 cohort, month 13: 7.8% retained2023-10 cohort, month 14: 5.4% retained2023-11 cohort, month 1: 40.5% retained2023-11 cohort, month 2: 20.3% retained2023-11 cohort, month 3: 21.2% retained2023-11 cohort, month 4: 16.2% retained2023-11 cohort, month 5: 12.6% retained2023-11 cohort, month 6: 12.2% retained2023-11 cohort, month 7: 11.3% retained2023-11 cohort, month 8: 9.9% retained2023-11 cohort, month 9: 10.4% retained2023-11 cohort, month 10: 10.4% retained2023-11 cohort, month 11: 6.3% retained2023-11 cohort, month 12: 9% retained2023-11 cohort, month 13: 8.6% retained2023-12 cohort, month 1: 43.1% retained2023-12 cohort, month 2: 23.9% retained2023-12 cohort, month 3: 22.3% retained2023-12 cohort, month 4: 12.2% retained2023-12 cohort, month 5: 16% retained2023-12 cohort, month 6: 9% retained2023-12 cohort, month 7: 13.8% retained2023-12 cohort, month 8: 9.6% retained2023-12 cohort, month 9: 9.6% retained2023-12 cohort, month 10: 7.4% retained2023-12 cohort, month 11: 9% retained2023-12 cohort, month 12: 9.6% retainedJan 242024-01 cohort, month 1: 35.2% retained2024-01 cohort, month 2: 20% retained2024-01 cohort, month 3: 20.5% retained2024-01 cohort, month 4: 15.2% retained2024-01 cohort, month 5: 14.8% retained2024-01 cohort, month 6: 9% retained2024-01 cohort, month 7: 9.5% retained2024-01 cohort, month 8: 9% retained2024-01 cohort, month 9: 12.4% retained2024-01 cohort, month 10: 10.5% retained2024-01 cohort, month 11: 10.5% retained2024-02 cohort, month 1: 39.1% retained2024-02 cohort, month 2: 25.5% retained2024-02 cohort, month 3: 21.1% retained2024-02 cohort, month 4: 17.4% retained2024-02 cohort, month 5: 15.5% retained2024-02 cohort, month 6: 9.9% retained2024-02 cohort, month 7: 12.4% retained2024-02 cohort, month 8: 8.1% retained2024-02 cohort, month 9: 6.2% retained2024-02 cohort, month 10: 7.5% retained2024-03 cohort, month 1: 33.9% retained2024-03 cohort, month 2: 22.4% retained2024-03 cohort, month 3: 18.8% retained2024-03 cohort, month 4: 10.4% retained2024-03 cohort, month 5: 9.4% retained2024-03 cohort, month 6: 13.5% retained2024-03 cohort, month 7: 10.9% retained2024-03 cohort, month 8: 8.3% retained2024-03 cohort, month 9: 10.4% retainedApr 242024-04 cohort, month 1: 34.1% retained2024-04 cohort, month 2: 22.5% retained2024-04 cohort, month 3: 17.6% retained2024-04 cohort, month 4: 13.7% retained2024-04 cohort, month 5: 15.4% retained2024-04 cohort, month 6: 9.9% retained2024-04 cohort, month 7: 12.1% retained2024-04 cohort, month 8: 10.4% retained2024-05 cohort, month 1: 36.8% retained2024-05 cohort, month 2: 20.3% retained2024-05 cohort, month 3: 13.4% retained2024-05 cohort, month 4: 11.7% retained2024-05 cohort, month 5: 13% retained2024-05 cohort, month 6: 10.4% retained2024-05 cohort, month 7: 6.5% retained2024-06 cohort, month 1: 37.8% retained2024-06 cohort, month 2: 27.9% retained2024-06 cohort, month 3: 16.9% retained2024-06 cohort, month 4: 12.9% retained2024-06 cohort, month 5: 9.5% retained2024-06 cohort, month 6: 16.9% retainedJul 242024-07 cohort, month 1: 38.8% retained2024-07 cohort, month 2: 27% retained2024-07 cohort, month 3: 19.4% retained2024-07 cohort, month 4: 15.8% retained2024-07 cohort, month 5: 13.8% retained2024-08 cohort, month 1: 39.5% retained2024-08 cohort, month 2: 20.6% retained2024-08 cohort, month 3: 14.9% retained2024-08 cohort, month 4: 15.8% retained2024-09 cohort, month 1: 38% retained2024-09 cohort, month 2: 25.9% retained2024-09 cohort, month 3: 20% retainedOct 242024-10 cohort, month 1: 38.2% retained2024-10 cohort, month 2: 21.3% retained2024-11 cohort, month 1: 36.9% retainedM1M3M6M9M125%45%+Portfolio project · Synthetic dataset · Avg M1 37.9%
  • Portfolio project
  • Synthetic dataset
  • Customer Intelligence

Cohort retention analysis: when do customers stop coming back?

A portfolio cohort analysis of 15 months of e-commerce orders, with a reusable SQL pipeline and retention heatmaps showing when customers stop returning.

Synthetic dataset: 2,810 customers · 6,404 orders · 15 cohorts

Read the cohort retention case study
Questions

Common questions about dashboards & reporting

Do you work in Power BI, Excel or Looker Studio?

All three. The right choice depends on the tools your team already uses, where your data is stored and who needs access. We recommend a tool during discovery rather than defaulting to one.

Can the dashboard update automatically?

Usually, yes, when the data source supports scheduled refresh (for example a database, GA4 or a cloud-hosted spreadsheet). Where it does not, we document a simple manual refresh process.

Do you work with NGOs on M&E and donor reporting?

Yes. We build indicator tracking, monitoring and evaluation reports and donor dashboards from programme and survey data.

ProductsExcel dashboard templates for sales, marketing ROI and business performanceRelated serviceMarketing AnalyticsRelated serviceCustomer Intelligence

Discuss a dashboards & reporting project

Share the question you are trying to answer and the data you have. We will reply with practical next steps.

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