A knowledge-based approach to government efficiency (part 2): Lessons learned – slow or fast?

The first part of this two-part article series asked the question: what constitutes a knowledge-based approach to government efficiency? It then explored evidence to support the need for rigorous scope and requirements definition, a systems thinking approach, and enhanced accountability and oversight with lessons learned capture to drive continuous improvement.

However, a significant caution that must be raised is that traditional lessons learned approaches are too slow to be effective in something as fast paced as the efficiency initiatives being implemented by the US Department of Government Efficiency (DOGE), and too linear to be workable with systems thinking.

While traditional lessons learned approaches could still have a place in learning across different efficiency initiatives over a number of years, what would be needed now if DOGE was to take a systems thinking approach is iterative lessons learning that occurs in real time or as close as possible to real time.

Iterative impact-oriented monitoring

As discussed in the Overseas Development Institute (ODI) Working Paper¹, Taking responsibility for
complexity: How implementation can achieve results in the face of complex problems, this fast lessons learning approach is called “iterative impact-oriented monitoring.”

Author Harry Jones advises that:

Continual monitoring of the effects an intervention is having will be critical to its success – and this should be done in order to check and revise understandings of how change can be achieved, rather than simply recording progress. It is therefore imperative to make any evaluation as utilisation-focused as possible, to ensure the requisite feedback is received to allow for timely adaptation.

Case study 1 – A government river recovery program

A case study of the successful application of iterative impact-oriented monitoring is the Australian Government funded $77.4 million Hawkesbury-Nepean River Recovery Program² that I also referenced in part 1 of this series. This program had the aim of improving the health of an iconic river system, and comprised seven projects carried out by six different state government agencies.

As HNRRP Program Manager, I implemented an agile approach to program management, as discussed in part 8³ of the RealKM Magazine series “KM in project-based and temporary organisations.” To do this, I included the following additional requirements in the three-monthly project reports:

• An explanation of any delays that occurred in the reporting period and the actions to be taken to address the delays.
• Risk assessment review, involving the review of the project schedule of the comprehensive risk management report prepared at the beginning of the program, as discussed in part 7⁴ of the “KM in project-based and temporary organisations” series.
• A detailed explanation of the work to be undertaken in the next reporting period,
• Any potential difficulties, issues or risks anticipated in the next reporting period and the actions that would be taken to mitigate these potential difficulties.

These additional requirements had the effect of turning each three-monthly reporting period into an incremental and iterative agile stage. As a result of this approach, the HNRRP was completed on time and under budget in two and a half years, having exceeded its intended outcomes despite significant challenges, and subsequently won two major awards.

Case study 2 – Xiaomi

A further case study of the successful application of iterative impact-oriented monitoring is Chinese electronics maker Xiaomi. As reported in a RealKM Magazine research summary⁵ and shown in the following video, Xiaomi engages directly with its customer support base to listen to and directly respond to the issues this large and growing community⁶ identifies. Because the iterative cycles are rapid and direct, problems and potential solutions are quickly identified and addressed, and the community helps to drive innovation.

Header image source: fauxels on Pexels.

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