Data to Action: 4 Characteristics of Actionable Information

These days, “actionability” is one of the most over used terms in information design. Many digital products including decision support tools, personal health apps, and financial dashboards boast claims of actionability to their customers. Countless clients and executives task designers with “making information actionable”. As a result, the term has become so commonplace that it risks becoming a meaningless buzzword.  Nothing could be farther from the truth; however, in a world characterized by increasing volumes of data, actionability is only becoming a more meaningful concept. Without a level of actionability even beautifully designed information will fail to engage users and deliver its full value.

The problem is that many designers don’t have  clear guidelines for how to make information actionable. Actionability, as it turns out, is neither a mystery nor a simple unitary concept. In fact, there are four easily identifiable heuristics to test whether information will be actionable to users. Actionable information must have all of the following characteristics: relevance, specificity, insightfulness, and affordance for action. When the actionability of information is called into question, cross check with the principles outlined in the rest of this article.

Relevance.

The first and most foundational characteristic actionable information must have is that it must clearly pertain to the decision goals of your target users. Actionability exists and only exists within the context of your users making a decision or, more generally, seeking guidance. Yes, its true that some users only seek the self-understanding and or sense of control that comes from information. But, with the possibilities for apps to deliver information about nearly every part of our lives, it is more likely that your users will be interested in how this information affects a particular decision that they face. This sounds obvious, but it means we need to reverse our design process in many cases. Rather than beginning our designs with the questions of which information we can provide users and then identifying a use case for it, first we need to understand the decisions users face and identify which data can help them become more well informed. In order for us to do this we need to understand as much about users’ decisions as possible. Though it is simple to say this, it is the fundamental challenge of a product team and requires insightful user research, iterative design, and a rich understanding of subject matter.

This Epilepsy symptom tracker from PatientsLikeMe provides an example of information that is relevant for decisions. Though this information could be used in a number of ways, it is probably most useful for people who want to understand the situations that are most likely to lead to an episode of seizures so that they can avoid situations that cause it, or at least prepare for it. By stacking triggers and seizure count on top of each other, users can identify the triggers that most affect them.

Specificity.

In addition to irrelevant information, vague and general information is rarely very helpful. General imperatives or binary statements that simply imply “good” or “bad” don’t provide as much value as information that clarifies the “why” behind a message.  More specific information is more versatile because it allows users to build a deeper understanding that is relevant to a variety of situations they may encounter. Providing analysis and recommendations that are specific empowers users to have a clear course of action and derive insight into complex situations.  A personal training app that tell users to simply “work out more” will provide little help to users. It would be much more compelling to tell users how often they should exercise and which workouts might be best for them. Without specifics, information displays offer little implication for action and are unlikely to inspire the type of action we intend.

It is worth noting that relevance and specificity must be balanced. A focus on relevance will result in streamlining information while highly specific information may lead to providing more data in an interface. Again, the key to finding this balance is understanding the precise demands of the decision your high priority users face.

Insight.

Though specific and relevant information is a great starting point for actionability, many apps or software products that offer this still fail to engage users. Why? Because it doesn’t offer users new insight about themselves or their situation. When we receive information that allows us to understand our situation deeper or in a different way we pay closer attention to it. As a result we behave differently and are more motivated towards action. For example, most working adults already know they should save more money for retirement; however, a dynamic visualization like the one found on Betterment.com enables you to understand the real impact saving extra money each month will have on your retirement. For example, I can see the surprisingly large effect that saving an extra $1000 per month will provide me when I retire.

Implication for action.

Finally, in order for information to be clearly actionable, its implications for how to be used should be clear and prominent. In other words, it should afford action. For many new and intermediate users, relevant and specific data is not enough to deliver on the value of information. Without providing an answer to “What do I do with this?” or “What should I do next?” many users will not be helped by your app and it will fail to generate repeated use.

Take for example, Apple Health. Upon opening Health, many users will be pleasantly surprised to see that their smart phone also doubles as a wearable fitness tracking devices. Indeed, it presents a beautiful visualization outlining very specific activity. On further investigation, one has to wonder how useful this dashboard actually is. I can see that I have 1794 steps so far (at 3:20pm) today and that this amount is little more than half my daily average. But this data fails to provide me with clarity on what to do next. I am left wondering if I should I walk more and how increasing (or decreasing) my activity will affect my health? Visualizations like Apple Health’s are silent on these questions and, therefore, engagement can fall flat.

Transforming data into actionable insights is complex. This complexity often gets glossed over due to relying on a single overused term. However, you can begin to understand how to increase the actionability of your app’s information by asking yourself if it is relevant to your users’ goals, specific enough to provide guidance, contains novel insights, and clearly points them towards a desired action. If you can answer yes to all these questions you’re on your way to helping users get the most out of the data you offer them.