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Feature Risks: Analysis

Fashion​

Fashion plays a big part in IT. By being fashionable, web-sites are communicating: this is a new thing, this is relevant, this is not terrible. All of which is mitigating a Communication Risk. Users are all-too-aware that the Internet is awash with terrible, abandon-ware sites that are going to waste their time.

How can you communicate that you're not one of them to your users?

Delight​

If this breakdown of Feature Risk seems reductive, then try not to think of it that way: the aim of course should be to delight users, and turn them into fans.

Consider Feature Risk from both the down-side and the up-side:

  • What are we missing?
  • How can we be even better?

Analysis​

So far in this section, we've simply seen a bunch of different types of Feature Risk. But we're going to be relying heavily on Feature Risk as we go on in order to build our understanding of other risks, so it's probably worth spending a bit of time up front to classify what we've found.

The Feature Risks identified here basically exist in a space with at least 3 dimensions:

  • Fit: how well the features fit for a particular client.
  • Audience: the range of clients (the market) that may be able to use this feature.
  • Change: the way the fit and the audience changes and evolves as time goes by.

Let's examine each in turn.

Fit​

"This preservation, during the battle for life, of varieties which possess any advantage in structure, constitution, or instinct, I have called Natural Selection; and Mr. Herbert Spencer has well expressed the same idea by the Survival of the Fittest" - Charles Darwin (Survival of the Fittest), Wikipedia.

Darwin's conception of fitness was not one of athletic prowess, but how well an organism worked within the landscape, with the goal of reproducing itself.

Feature Fit Risk, Conceptual Integrity Risk and Implementation Risk all hint at different aspects of this "fitness". We can conceive of them as the gaps between the following entities:

  • Perceived Need, what the developers think the users want.
  • Expectation, what the user expects.
  • Reality, what they actually get.

Feature Risks Assembled. Fit Risks, shown as gaps, as in the Service Quality Model

For further reading, you can check out The Service Quality Model which the diagram above is derived from. This model analyses the types of quality gaps in services and how consumer expectations and perceptions of a service arise.

In the Staging And Classifying section we'll come back and build on this model further.

Fit and Audience​

Two risks, Feature Access Risk and Market Risk, consider fit for a whole audience of users. They are different: just as it's possible to have a small audience, but a large revenue, it's possible to have a product which has low Feature Access Risk (i.e lots of users can access it without difficulty) but high Market Risk (i.e. the market is highly volatile or capricious in it's demands). Online services often suffer from this Market Risk roller-coaster, being one moment highly valued and the next irrelevant.

  • Market Risk is therefore risk to income from the market changing.
  • Feature Access Risk is risk to audience changing.

Fit, Audience and Change​

Risks of Change either of the product or the expectations of clients.(/img/generated/risks/feature/all-feature-risk2.svg)

Two risks further consider how the fit and audience change: Regression Risk and Feature Drift Risk. We call this Change in the sense that:

  • Our product's features evolve with time and the changes made by the development team.
  • Our audience changes and evolves as it is exposed to our product and competing products.
  • The world as a whole is an evolving system within which our product exists.