Dashboards are at the forefront of today’s episode, and so I will be responding to some reader questions who wrote in to one of my weekly mailing list missives about this topic. I’ve not talked much about dashboards despite their frequent appearance in data product UIs, and in this episode, I’ll explain why. Here are some of the key points and the original questions asked in this episode:
Quotes from Today’s Episode * “The term dashboards is almost meaningless today, it seems to mean almost any home default screen in a data product. It also can just mean a report. For others, it means an entire monitoring tool, for some, it means the summary of a bunch of data that lives in some other reports. The terms are all over the place.”- Brian (@rhythmspice) (01:36) * “The big idea here that I really want leaders to be thinking about here is you need to get your teams focused on workflows—sometimes called jobs to be done—and the downstream decisions that users want to make with machine-learning or analytical insights. ” - Brian (@rhythmspice) (06:12) * “This idea of human-centered design and user experience is really about trying to fit the technology into their world, from their perspective as opposed to building something in isolation where we then try to get them to adopt our thing. This may be out of phase with the way people like to do their work and may lead to a much higher barrier to adoption.” - Brian (@rhythmspice) (14:30) * “Leaders who want their data science and analytics efforts to show value really need to understand that value is not intrinsically in the dashboard or the model or the engineering or the analysis.” - Brian (@rhythmspice) (18:45) * “There's a whole bunch of plumbing that needs to be done, and it’s really difficult. The tool that we end up generating in those situations tends to be a tool that’s modeled around the data and not modeled around [the customers] mental model of this space, the customer purchase space, the marketing spend space, the sales conversion, or propensity-to-buy space.” - Brian (@rhythmspice) (27:48) * “Data product managers should be these problem owners, if there has to be a single entity for this. When we’re talking about different initiatives in the enterprise or for a commercial software company, it’s really sits at this product management function.” - Brian (@rhythmspice) (34:42) * “It’s really important that [data product managers] are not just focused on deliverables; they need to really be the ones that summarize the problem space for the entire team, and help define a strategy with the entire team that clarifies the direction the team is going in. They are not a project manager; they are someone responsible for delivering value.” - Brian (@rhythmspice) (42:23)
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