Real challenges. Measurable results.

The challenge

Learner data was collected by different people in different formats, with no single record per learner. The programme needed to show improvement in maths results by province, district and school, while making sure learners could never see each other’s information.

What we did

• Consolidated camper records from the programme’s data collection into one clean, validated data model
• Built a Power Apps canvas app on a secure data store, with access rules that isolate each learner’s record
• Created Maths Improvement Rating bands and both weighted and unweighted analysis in Power BI
• Mapped schools using GPS coordinates to show reach by province and district across KZN, the Western Cape, Mpumalanga, North West and Limpopo

The result

• A single, trusted record for every learner and camp
• Impact reporting that refreshes automatically instead of being rebuilt by hand
• Solution packaged and migrated from development into the client’s Azure-hosted production tenant, with environment variables, connection references and Azure SQL configuration
• Careful measure design so charts reflect true participation, avoiding misleading percentages

The challenge

Scores depended on who did the review and which spreadsheet version they used. There was no easy way to compare providers or show how a score was reached.

What we did

• Worked with stakeholders to define 18 criteria and agree their weightings
• Built a Power Apps capture tool so assessors score inside one controlled process
• Applied a 95% minimum threshold for quantitative measures, calculated automatically
• Delivered Power BI reporting that shows every score and how it was derived

The result

• Consistent, auditable scoring across assessors
• Provider comparisons and trends available on demand
• Clear evidence to support contract conversations

The challenge

User-access reviews were done by sampling and manual comparison of exports. Accounts belonging to people outside the organisation were hard to spot, and each audit cycle started from scratch.

What we did

• Combined Active Directory, HR and application user extracts into one model
• Matched identities across systems and flagged accounts with no owner
• Profiled last-logon activity to separate dormant from active risk
• Packaged the logic as a reusable proof of concept for ITGC automation

The result

• 24 external accounts identified that did not exist in AD or HR
• 37.5% of them had never been used, a clear case for removal
• A proof-of-concept approach later adapted into a wider automation proposal

The challenge

Profitability was estimated at deal level in spreadsheets, and residual values relied on internal tables with little market comparison.

What we did

• Rebuilt contract cash flows with PMT logic covering vehicle cost, term, residual value, admin fees, value-added products and maintenance
• Calculated a monthly IRR per contract and a spread against WACC
• Validated results in Excel before writing them back to SQL with anomaly flags and a data dictionary
• Designed a residual value and market pricing dashboard that maps listings from several online marketplaces to manufacturer models

The result

• Every contract ranked by its margin over the cost of capital
• Outliers flagged automatically for review
• A documented model finance can maintain and audit

The challenge

With tens of thousands of employees across many grades and regions, pay gaps were hard to see and harder to explain.

What we did

• Calculated compa-ratios and position-in-range against grade midpoints
• Measured pay spread with Palma-style ratios between top and bottom earners
• Fitted an ordinary least squares model on the log of guaranteed pay to isolate unexplained differences
• Presented results at a level leadership and reward teams could act on

The result

• Clear evidence of where pay is and is not aligned to grade
• Groups with unexplained gaps identified for review
• A repeatable method for future pay cycles

The challenge

Pay data lived in payroll exports. HR and leadership could not easily see how guaranteed pay, basic salary, retirement funding and medical aid were distributed, which made remuneration and employment equity discussions harder than they needed to be.

What we did

• Modelled payroll data in Power BI, separating guaranteed pay, basic salary, pension or provident fund and medical aid contributions
• Built side-by-side gender comparisons by grade, by race group and by department
• Grouped employees by tenure band to show where pay is concentrated
• Added simple filters so HR can focus on any group in a few clicks

The result

• One interactive view of the whole pay bill
• Grade-level gender gaps visible at a glance
• Evidence to support employment equity and remuneration reviews

The challenge

Payroll testing usually relies on small samples and manual recalculation in spreadsheets. Errors in tax, deductions or net pay can go unnoticed, especially for joiners and leavers.

What we did

• Rebuilt taxable income for each employee and month from earnings and deductions
• Recalculated PAYE and compared it with the tax actually deducted
• Calculated net pay and cost-to-company variances, flagged by size and direction
• Charted calculated against actual net pay by month, with the variance trend alongside
• Added views for joiners and terminations, payslip drill-down and audit sampling

The result

• Every PAYE and net pay variance identified at employee and month level
• Net pay variance tracked month by month to show where errors cluster
• Full-population testing instead of a small sample
• A clear evidence base for the review team

The challenge

Management could not easily see how volumes, net sales revenue and vehicle classifications were moving, or how reclassifications affected stock value.

What we did

• Consolidated stock and sales records into one reporting model
• Tracked units sold and net sales revenue (NSR) trends
• Analysed Code 2 and Code 3 reclassifications and their effect on stock
• Delivered monthly reporting over a five-month engagement

The result

• A single view of stock and sales performance
• Reclassification impact made visible to management
• Monthly reporting management could rely on