There is a tendency nowadays to focus on data-driven performance management techniques mostly in modern public sector administration. This approach takes into consideration the results (numbers) and cannot evaluate governmental processes and activities.


An example of this change has been studied through the system of governance by targets in the English NHS adopoted by the Blair administration : the important aspect is to see if reaching a target because of data-driven performance management leads to an actual improve of performance.

In practice, the actual performance evaluation has proved not to be meaningful because the performance of employees was made while being focused on “making the numbers” rather than the larger objectives of the program which can ultimately lead to the erosion of confidence in government.


Taking into consideration that data-driven performance management, which has a similar logic as the reactive algorithmic systems, is more and more likely to be used especially in the governmental sector, how to balance data-driven management vs quality and processes?

edited question

I reworded the question title. I like the question : full speed quantitative and machine-friendly vs qualitative and human-friendly ? Quite central.

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