New Looks for Intranets and Dashboards

“There could be a heatmap for project management to see how we’re doing; red, yellow, and green for completion timeframes around a project,” Crawford mentioned. “Or, what’s eligible for destruction [for retention purposes], or what’s coming up that’s eligible for destruction.” Such widgets are helpful for implementing facets of regulatory compliance, data privacy, and data governance. Other widgets are assistive for transactional purposes, such as one that Crawford characterized as “order status, which is another popular widget if you’re doing transactions as part of a workflow and people are requesting things, such as assets, to be delivered.” This type of widget could also be applied to various metrics for departments including accounting and other business units. 

Personalization

Language model implications of dashboards and intranets are particularly intriguing, even emblematic of the premier movement in this space. With the capacity to generate visualizations, colors, and shapes indicative of workflows, analytics, or enterprise data on demand, generative models facilitate a form of personalization that’s aligned with consumer expectations for dashboards. 

Such recommendations are a classic machine learning problem, which today is being addressed with a variety of traditional and advanced statistical models. A relatively recent new development for dashboards is their incorporation of measures to evaluate the content, visualizations, or other dimensions of what’s displayed as a feedback mechanism for the dynamic algorithms furnishing suggestions.

This form of AI is also improved depending on which dashboard characteristics a particular user favors. The result is a mutable dashboard able to learn personal preferences and “break the rules as well,” Crawford said. “A good dashboard is great at that. It will persist and be reliable for what it’s serving up to you, based on what it knows you need to see, the context, and you as a user. But it also knows when to say, ‘Here’s some other things that might be of interest to you, even though you haven’t asked for it before.’” With this approach, the same dashboard would yield different results to employees in the same department trying to accomplish the same task. Those differences would be predicated on their respective ways of working and personal preferences.

Data Quality

The inclusion of personalized, dynamic statistical algorithms for recommendations is an interesting facet of contemporary dashboards, because it brings attention to the notion of accuracy. Holistically, the underlying purpose of dashboards (whether or not they’re contained in intranets) has not, and likely will not, change. They’re designed “to serve as a true single pane of glass,” Crawford observed. “That’s what makes them so functional: the aggregation of multiple datapoints into a single, one-stop shop.” Consequently, dashboards place a premium on data quality so that the information they illustrate is timely, correct, and beneficial to end users. Poor data quality has the potential to undermine any dashboard endeavor, regardless of how sumptuous the visualizations, or how much information they show. As with most things related to data, the foundation for successful data quality requires prudent metadata management.