The team follows a set methodology and principles to ensure each use case has a measurable impact. From the onset, we apply Design Thinking to define the business problem or hypothesis with the relevant subject matter experts and business analysts to ensure alignment on unit of analysis, performance criteria and risks. Our data engineers than ensure we can effectively source, ingest and store master and raw data in a manner that complies with governance (Data Privacy Policy). 

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Data is then acquired, prepared, cleaned and normalised by our developers and data quality analysts to ensure usability (example of ELT tools: Alteryx and Python). Remove any target leakage and perform all feature engineering.

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All data analysis is conducted with relevant SMEs to ensure metrics, calculations and analysis meet the objective defined at the start. The outcome is then visualised and presented in reports (examples of visualization tools: Power Bi, SAP Analytics Cloud). 

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Predictive models are designed by our data scientist using DataRobot using computer algorithms to

• uncover insights,

• determine relationships, and

• make predictions about future trends.

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All insights and models are interpreted, validated and deployed. Examples of use cases include: 

• Merge data from SAP & Invoicing Management System to update the Power BI report

• Measures work capacity for a Mining operation

• Show all the earnings and deduction a reporting period

• Show all the Statutory and Non-Statutory Leave Balances 

• Report flags employees receiving double benefits

• Payroll fraud detection 

• Variable pay on benefits compliance – managing double-dipping for employees

• Garnishees – admin outsource, legal action, comms, and financial wellness advice and assistance – alignment with labour Unions

• Workforce analytics including headcount tracking and diversity analytics

• Performance tracking and analysis

• Cashflow predictions 

• Capacity management