# 4. Leave room for judgment

Our caution is against using data reductively: as a replacement or substitute for managing. Management _must_ be about more than measuring. A good manager needs to be able to accommodate the immeasurable, since so much that is important to human thriving is in this category; dashboards et al certainly have their place, but if these were all that was needed then ‘managing’ could be conducted by machines. We all know from personal experience that the best managers and leaders are those that take a holistic interest in their staff, making the time and effort to understand the subtle, often intangible processes that connect their respective talents. As organizational management theorist Henry Mintzberg (2015) wisely puts it, Measuring as a complement to managing is a fine idea: measure what you can; take seriously what you can’t; and manage both thoughtfully. In other words: If you can’t measure it, you’ll have to manage it. If you can measure it, you’ll especially have to manage it. Have we not had enough of leadership by remote control: sitting in executive offices and running the numbers—all that deeming and downsizing?<sup>37</sup> Contrary to the “what can’t be measured can’t be managed” idea, we _can_ manage the less measurable if we embrace a wider set of tools and leave space for judgment. The key for practitioners is to begin with a recognition that measurability is not an indicator of significance and that professional management involves far more than simply “running the numbers”, as Mintzberg puts it. Perhaps the most compelling empirical case for the importance of ‘navigating by judgment’ in public administration has been made by Honig (2018), in which he shows – using a mix of quantitative data and case study analysis – that the more complex the policy intervention, the more necessary it becomes to grant discretionary space to front-line managers, and the more necessary such discretion is to achieving project success. Having ready access to relevant, high-quality quantitative data can aid in this ‘navigation’ but true navigation requires access to a broader suite of empirical inputs.

In a similar vein, Ladner (2015: 3) points out that “standard performance monitoring tools are not suitable for highly flexible, entrepreneurial programs as they assume that how a program will be implemented follows its original design”. In order to avoid ‘locking in’ a theory of change that prevents exploration or responsive adaptation, some practitioners have provided helpful suggestions for how to use various planning frameworks in ways that support program learning.<sup>3839</sup> The Building State Capability team highlights lighter touch methods, such as their PDIA “check ins”, which include a series of probing questions to assist teams in capturing

> 37 <u>https://mintzberg.org/blog/measure-it-manage-it. Says Mintzberg: “Someone I know once asked a most senior</u> British civil servant why his department had to do so much measuring. His reply: ‘What else can we do when we don’t know what’s going on?’ Did he ever try getting on the ground to find out what’s going on? And then using judgment to assess that?”

> 38 Teskey (2017) and Wild et al (2017) give examples of an adaptive logframe, drawn from DfID experiences, that sets out a set of clear objectives at the outcome level, and focuses monitoring of outputs on the quality of the agreed rapid-cycle learning process.

> 39 Strategy Testing (ST) is a monitoring system that The Asia Foundation developed specifically to track programs that are addressing complex development problems through a highly iterative, adaptive approach.

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