![]() Misstep 2: Not treating agile as a strategic priority that goes beyond pilots Further, the lack of alignment on the value of the transformation meant that teams spent little time thinking through and tracking the value their efforts would deliver. The transformation ended up having limited impact, as teams in different parts of the organization applied agile principles to varying degrees and in multiple flavors, which led to a significant increase in the overhead of managing across teams. Not doing so can constrain the impact the transformation might have.įor example, a large global company initiated a bottom-up agile transformation without first aligning on the end-state aspiration and the value the transformation would create. The identified value drivers are then used throughout the transformation, from guiding the design of the operating model to ensure value delivery, to designing metrics to monitor value capture during rollout. While we don’t encourage attempts to design an end state in granular detail, the depth and breadth of an agile transformation requires aligning at a high level on the aspiration, the value it would deliver, and a plausible plan for achieving it. Further, even when there is such alignment, we often see companies that, in the spirit of adopting some agile principles-such as experimentation and empowered teams-end up creating a burning platform, as different leaders across the organization choose different approaches to implementing agile, while others dig in their heels to maintain the status quo. We have often seen organizations embark on such transformations without first ensuring alignment among the leaders of the organization on the aspiration and value of the transformation. Misstep 1: Not having alignment on the aspiration and value of an agile transformationĪgile, fundamentally, is a redesign of the operating model of (parts of) the enterprise. Based on our experience across numerous transformations, we see the following as common missteps on an agile journey. While many traditional heavyweights have embarked on agile transformations, most have faced real challenges in achieving their desired objectives. Beyond solution development, we are designing and implementing enterprise-wide operating models based on these principles. The term “agile” has now expanded to many facets of solution development with the same underlying principles-develop iteratively, release frequently, focus on the customer, and collaborate through a cross-functional team-always prioritizing test-and-learn methods over detailed planning. Agile started as a set of principles for software development to write and release code iteratively without waiting for months (or years) to release functionality. Is an ongoing longitudinal survey that began in 1968 and with aįocus on the core topics of income, employment, and health.Agile principles have been one of the key drivers of Silicon Valley’s ability to innovate, learn, and adapt rapidly. The goal of this research is to examine these issues.ĭata for this research comes from the Panel Survey of Incomeĭynamics (PSID) conducted by the University of Michigan. As these two source of nonresponse have been shown to have different determinants, they can also be expected to have different impacts on data quality. If the dynamic behaviors that panel surveys are designed to examine are also prompting attrition, estimates of those behaviors and correlates of those behaviors may be biased.Īlso, current research on panel attrition generally does not differentiate between attrition through non-contacts and attrition through refusals. However, current research on attrition in panel surveys focuses on the characteristics of respondents at wave 1 to explain attrition in later waves, essentially ignoring the role of life events as determinants of panel attrition. The fundamental purpose of most panel surveys is to allow analysts to estimate dynamic behavior. Also, the higher the attrition, the greater the concern that error (bias) will arise in the survey estimates. ![]() ![]() Reduced sample size increases the variance of the estimates and reduces the possibility for subgroup analysis. Cumulative nonresponse over several waves can substantially reduce the proportion of the original sample that remains in the panel. Nonresponse is of particular concern in longitudinal surveys (panels) for several reasons.
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