As We May See: The World after Dashboards
In 1945, influential scientist and government advisor Vannevar Bush published As We May Think, an article of remarkable foresight. In less than a dozen pages, he outlined many futuristic technologies that have come to pass, including hypertext, the personal computer and speech recognition. His memex, a kind of World Wide Web for the analog age, presaged today’s connected informational environment.
Eighty-five years later, we are at the cusp of equally groundbreaking possibilities. Natural language processing (NLP), natural language generation (NLG) and machine learning (ML) have merged to create a new paradigm for the representation of information. Instead of asking questions from data, we are moving towards self-curating data experiences that tell decision-makers what they need to know.
Dashboards are over (if you want it)
Enterprises have spent the last two decades meticulously curating dashboards. Tools have improved and what once was the preserve of trained professionals has largely been democratized across the enterprise. However, dashboards are quintessentially static. A dashboard shows what its developer has directed it to show. That ignores the vast realm of the ‘unknown unknown’ that is, what we don’t know that we don’t know.
Dashboards, in that sense, answer narrow questions while giving the illusion of comprehensiveness and a 30,000ft perspective. In reality, dashboards are limited tools to convey limited information, while providing a psychologically fulfilling yet false illusion of comprehensiveness.
This may be true, but until now, there have been rather few alternatives. In the modern enterprise, a dashboard is created to provide a consumer with information on a particular subject. This involves certain judgments about what is, and what is not, included. This, in turn, biases the observer to what they are presented: they are, in a sense, trapped in an involuntary version of McNamara’s fallacy. What is not quantified and dashboarded does not exist. Few modern executives have the capability to reach beyond these summaries of data (if they did, they wouldn’t need dashboards in the first place).
However, machine learning may spell a fundamental change in this. We now have the tools and techniques to sift through vast volumes of data and evaluate the relative saliency of each data item.
Saliency Based Representation of Evidence (SABRE) is a new paradigm for creating data experiences. SABRE considers an initial dashboard design little more than an informative prior that is subject to updating. As the volume of data increases, SABRE updates these priors with indicators of relative saliency of the incoming data.
Saliency refers to the relative cognitive importance of a feature. In other words, salient features are those slices of a wider reality that most efficiently convey a situational picture to an observer. A driver on a highway may pay particular attention to the road, the vehicle in front and the speedometer gauge. These are salient because from the perspective of decision-making by the driver, the situational picture is almost exhaustively described by these facts, while the row of trees adjoining the road contributes rather little to the driver’s decision-making.
Consider now one of the trees from the row of trees starting to lean over the highway. Suddenly, this becomes acutely relevant to the driver’s conduct if he expects to survive. This is an example of an anomaly: an otherwise rather less noticeable object behaving in an unusual manner. On the other hand, consider the coolant temperature gauge rapidly climbing towards boiling point. What is otherwise a rather much-neglected indicator suddenly becomes relevant due to its rapid change. These two scenarios describe the two typical sources of saliency: anomalies and trends. What is salient what we ought to pay attention to is what is out of the ordinary, or what is changing rapidly.
SABRE creates secondary variables (likelihood of anomaly and trend of change) to discern the overall saliency of an indicator. Where hundreds of KPIs and gauges compete for the viewer’s attention, SABRE prioritizes these, and determines a relative rank.
SABRE is quintessentially a Bayesian metric. As such, it is amenable to updating its priors according to evidence. These can be gradual changes: for instance, as the likelihood of anomaly increases for a metric, it would typically slide upwards, whereas if an indicator were to regress towards normal, non-anomalous behavior, it would eventually slide lower. However, as an open Bayesian metric, it is possible to include other forms of evidence. For instance, users may provide feedback on the metrics they provide, focusing on metrics they deem important, which would be prioritized in future representations.
Creating data experiences
Beyond dashboards, data visualization will focus on data experiences. The experiential approach to data means that dashboards are going to be only one of many ways to interact with data. Clear-text queries, voice recognition and even immersive experiences such as those leveraging AR/VR, will emerge as other ways to interact with information. No longer constrained by the fundamentally format-dependent genre of dashboards, data experiences can take any shape perhaps beyond data visualization as we may think today.
Creating data experiences is fundamentally going to be an emergent art of turning saliency-ranked data items, such as KPIs, into an explorable, interactive experience. As such, saliency-based data experiences do not replace dashboards with another form of data visualization. Rather, it challenges the very paradigm on which the philosophy of dashboards rests, and by which dashboards are constrained (e.g. two-dimensionality, pre-definition). Beyond dashboards lies a world in which data is not something that is provided in a pre-configured structure, but rather something that is conveyed as an informed response to a system’s inferences about what a decision-maker might need, or want, to know.
And the world is ready for it.
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