One of the holy grails of big data is “the portal” (also known as “the dashboard”): there is transparency, timeliness, and nifty graphics that expose never-before-seen insights. For those with the swankiest implementations, data is even personalized: depending on your level and your functional responsibilities: what you see is precisely what you need. (We’re assuming that most organizations have at least something online that tracks OKRs/KPIs/other data/etc.)

So two tough questions for those who think that the job is done once it is set up: Is the implementation of a dashboard also the end of thoughtful analytics? Is it the beginning of the worst kind of management complacency?

Consider the arguments:

1. When the dashboard is developed, decisions are made about what is tracked, as well as the priority of data placement. But as time goes on, are these still the precise markers that should be tracked? And are the priorities still unchanged? Mitigation: Each year, as the business changes and new objectives are set – or whenever there is a management re-organization – the dashboards should be reviewed at the same time.
2. What’s not on the dashboard often gets ignored. As the dashboard becomes the singular point of truth, smaller “micro-signals” that do not appear within it get ignored. Mitigation: Allow individuals to customize their version of the portal with key data required in their roles. And during the launch (or re-launch) training, remind the users about all of the other ways to get the data they need.
3. Data focus vs Intelligence focus: Pretty charts and graphics are one thing, but understanding what it all means is something else entirely. With the data available so readily, managers need to understand their value is in two areas: understanding the implications of the data, and then acting on it. Mitigation: Leaders need to coach their team to stop being conveyors of data, but analyzers of it. It can be as simple as asking “Why”, “What does this mean”, or “What should we do about this”. We have found, when we deliver critical thinking training with our clients, there is a substantive change in this area.
4. Common vs Custom analysis: Just because the organization has automated the production of basic data on a dashboard, doesn’t mean that there aren’t more strategic or tactical insights that are available in the larger data set. The frontiers of data science (and AI-based analysis) have moved significantly over the last few years, pretty much in step with the profusion of data that is now available. Mitigation: resources should be allocated to plumb for these insights: machine learning and AI-based tools are the payoff.
5. AI-based “analytics”: Just because generative AI can surface insights on the data and present it in a dashboard, it doesn’t mean that the insights are actually correct, or that important insights are missed. There is a risk that the users of an AI-driven portal become even more complacent. Mitigation: label AI-based analysis within the dashboard as requiring confirmation; train users to always look for the insights that AI has missed.

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