Uncovering and leveraging that data that sits behind your business processes

Data and the ability to mine existing management data can drastically support organisations by quantifying the problem, formulating a change hypothesis, and testing and implementing change.

Successful organisations run, sometimes despite themselves. What we mean by this is that experienced leaders and founders leverage their experience and guts to make decisions. More often than not there are right but probably not as right as can be. Most organisations carry inefficiencies and waste in their business processes. They know it but they cannot fully quantify waste, understand factually the underlying factors linked to inefficiencies or identify the most effective corrective intervention.


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Akin to Sia Partner’s Time Out offering. We help our customers make the most of leadership guts and organisational data together by bringing together Process Diagnostic, Data Mining and Transformation & Change.

Once operational levers for improvement are identified with business leaders, Sia Partner’s Data & Analytics team runs practical efficient Data Deep Dive leveraging demand, output and quality data.

The analysis will provide insight into the levels of demand per process, its variability, seasonality and patterns (daily, weekly, monthly or as appropriate). Combined with output data, the analysis will provide insight on current lead times and will provide the baseline to asses any potential improvements. if the available base data were not sufficient to produce this insight, we will carry out a more limited exercise based on sampling. We will also gather capacity data (FTEs for all key roles in the process) and the data to assess operational and financial performance.

This enables the following outputs:


  • Analysis of customer demand and identification of segments of demand that explain at least 80% of current demand for resources (a proxy for cost).
  • Agreement on key performance metrics across the process.
  • Quantitative assessment of quality and productivity performance across key dimensions (study type, study size, geography, clinical site, customer).
  • Identification of levels and segments of operations showing normal performance, underperformance and over-performance across key dimensions and identification of specific cases sampling.
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