Feature bias: unbalanced concern for capability vs. sustainability

Last updated on July 7th, 2021 at 09:56 pm

Alaska crude oil production 1990-2015
Alaska crude oil production 1990-2015. This chart [Yen 2015] displays Alaska crude oil produced and shipped through the Trans Alaska Pipeline System (TAPS) from 1990 to 2015. Production had dropped by 75% in that period, and the decline is projected to continue. In January 2018, in response to pressure from Alaskan government officials and the energy industry, the U.S. Congress passed legislation that opened the Arctic National Wildlife Refuge to oil exploration, despite the threat to ecological sustainability that exploration poses. If we regard TAPS as a feature of the U.S. energy production system, we can view its excess capacity as a source of feature bias. It creates pressure on decision makers to add features to the U.S. energy system. Alternatively, they could act to enhance the sustainability of Alaskan and global environmental systems [Wight 2017].

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Enterprise decision makers affected by feature bias tend to harbor distorted views of the importance of new capability development compared to technical debt management. This tendency is likely due to the customer’s relative sensitivity to features, and relative lack of awareness of sustainability. Whatever the cause, customers tend to be more attracted to features than they are to indicators of sound technical debt management and other product sustainability practices. This tendency puts decision makers at risk of feature bias: unbalanced concern for capability vs. sustainability.

h4>Accounting changes can help

Changes in cost accounting could mitigate feature bias effects by projecting more accurately total MICs based on historical data and sound estimation. I explore possible accounting changes later in this post, and in future posts; meanwhile, let’s explore the causes and consequences of the distorted perspective I’m calling feature bias.

Causes and consequences of feature bias

For products or services offered outside the enterprise, the sales and marketing functions of the enterprise represent the voice of the customer [Gaskin 1991]. But customers are generally unaware of product or service attributes that determine maintainability, extensibility, or cybersecurity. These factors, the sustainability factors, affect the MICs for technical debt. But customers are acutely aware of capabilities—or missing or defective capabilities. Customer comments and requests are therefore unbalanced in favor of capability over sustainability. The sales and marketing functions tend to accurately transmit this unbalanced perspective to decision makers and technologists.

An analogous mechanism prevails with respect to infrastructure and its internal customers. Internal customers tend to be more concerned with capabilities than they are with sustainability of the processes and systems that deliver those capabilities. Thus, pressure from internal customers tends to emphasize capability at the expense of sustainability. The result of this imbalance is pressure to allocate excessive resources to capability enhancement, compared to activities that improve sustainability. And therefore controlling or reducing technical debt and its MICs gets less attention.

Nor is this the only consequence of feature bias. It provides unrelenting pressure for increasing numbers of features, despite the threats to architectural coherence and overall usability that such “featuritis” or “featurism” present. Featurism leads, ultimately, to feature bloat, and to difficulties for users, who can’t find what they need among the clutter of features that are often too numerous to document. For example, in Microsoft Word, many users are unaware that Shift+F5 moves the insertion point and cursor to the point in the active document that was last edited, even if the document has just been freshly loaded into Word. Useful, but obscure.

Feature bias bias

Feature bias, it must be noted, is subject to biases itself. The existing array of features appeals to a certain subset of all potential customers. Because it is that subset that’s most likely to request repair of existing features. And they’re also the most likely to suggest additional features. The pressure for features tends to be biased in favor of the needs of the most vociferous users. That is, there is pressure to evolve to better meet the needs of existing users. That pressure can force to lower priority any efforts toward meeting the needs of other stakeholders or potential stakeholders. These other stakeholders might be even more important to the enterprise than are the existing users. This bias in feature bias presents another risk that can affect decision makers.

Organizations can take steps to mitigate the risks of feature bias. An example of such a measure might be using focus groups to study how educating customers in sustainability issues affects their perspectives relative to feature bias. Educating decision makers about feature bias can also reduce this risk.

At the enterprise scale, awareness of feature bias would be helpful. But awareness alone is unlikely to counter its detrimental effects. These effects include underfunding technical debt management efforts. Eliminating the source of feature bias is extraordinarily difficult, because customers and potential customers aren’t subject to enterprise policy. Feature bias and feature bias bias are therefore givens. To mitigate the effects of feature bias, we must adopt policies that compel decision makers to consider the need to deal with technical debt.

A possible corrective action

One possible corrective action might be improving accounting practices for MICs, based on historical data. For example, there’s a high probability that any project might produce new technical debt. It might be prudent to fund the retirement of that debt in the form of reserves when we fund projects. And if we know that a project has encountered some newly recognized form of technical debt, it might be prudent to reserve resources to retire that debt as soon as possible. Ideas such as these can rationalize resource allocations with respect to technical debt.

These two examples illustrate what’s necessary if we want to mitigate the effects of feature bias. They also illustrate just how difficult such a task will be.

References

[Gaskin 1991] Steven P. Gaskin, Abbie Griffin, John R. Hauser, Gerald M. Katz, and Robert L. Klein. “Voice of the Customer,” Marketing Science 12:1, 1-27, 1991.

Cited in:

[Wight 2017] Philip Wight. “How the Alaska Pipeline Is Fueling the Push to Drill in the Arctic Refuge,” YaleE360, Yale School of Forestry & Environmental Studies, November 16, 2017.

Available: here; Retrieved: February 8, 2018

Cited in:

[Yen 2015] Terry Yen, Laura Singer. “Oil exploration in the U.S. Arctic continues despite current price environment,” Today in Energy blog, U.S. Energy Information Administration, June 12, 2015.

Available: here; Retrieved: February 8, 2018.

Cited in:

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