Project to Product by Mik Kersten

Author:Mik Kersten [Kersten, Mik]
Language: eng
Format: epub
Publisher: IT Revolution Press
Published: 2018-11-20T05:00:00+00:00

Copyright © Tasktop Technologies, Inc. 2017–2018. All rights reserved.

Figure 5.2: Sample Value Stream Dashboard

This sample dashboard provides a glimpse into how the Flow Framework can be used to track and manage a software product portfolio, and make the trade-offs that the business and IT make visible. For example, in the two value streams visible in Figure 5.2, it is instantly clear how much net new-business value, in the form of features, has flowed through each value stream.

Rather than setting a target for each value stream, the business sets a value metric, such as a revenue target for a particular product. The team responsible for the value stream can then set the corresponding flow distribution to optimize for feature flow. If a large amount of new risk work arrives during a critical time window (e.g., to implement a new regulatory requirement), the corresponding feature delivery reduction is visible to the key stakeholders through this dashboard. Similarly, if the development team for the product predicts that technical debt needs to be reduced in order to sustain the rate for feature delivery, those debts can be planned for and scheduled; and the trade-off between short-term and long-term impacts on feature velocity can be discussed and decided explicitly. This means that the same kind of trade-offs that product and engineering managers make at a much finer level of detail can cascade up to the business stakeholders at a higher level of abstraction, in order to drive adjustments and decisions.

In addition, the business outcomes are accurate and visible to both the technical and the business stakeholders. Value Stream Networks are a complex dynamic system. Rather than blindly applying generic best practices, what Value Stream Metrics allow us to do is measure and then optimize the dynamic system specifically to our organization. For example, in the Hub story earlier, we witnessed that an accumulation of technical debt resulted in decreased flow velocity for features. For a different organization, that could have translated into increased flow time instead.

With the Flow Framework, we can determine these correlations using real data, and we can continually learn and adjust. If we see that too much flow distribution allocated to features is resulting in quality problems, we can determine whether that is likely to be a leading indicator for lost value in the form of revenue decline or user attrition. Common flow patterns will arise as well. For instance, an excessive flow load in a value stream will likely lead to lower flow velocity, but the point at which this happens will be particular to the value stream.

Finally, since all flow metrics are correlated to business results, we have a mechanism for spotting more fundamental problems. If the feature flow items intended to produce business value are being delivered at a high rate but that delivery is not translating into a revenue outcome, we may have a bottleneck outside of the value stream in sales and marketing; or the bottleneck may be external to the organization and a problem with product/market fit.

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