Dashboards, as previously mentioned, should be divided into some types. For simplicity, we will single out three types, as it seems to me, the most contrasting: strategic, operational, analytical:
1 - Strategic - a quick state and triggers overview for decision making; focus on high-level indicators; ease of use; interactivity tends to zero; no or hardly any filters; audience - tops;
2 - Operational - metrics monitoring in various sections, comparisons, dynamics; provide more information, more complex and require deeper insight; easy-to-understand proven user scenarios; a wider audience of middle-, low-level management;
3 - Analytical - require additional context; difficult for perception and insights, more comprehensive analytic facilities; non-obvious user scenarios; highly interactive, lots of filters.
So, the first two types can now be combined - both are about in-process monitoring, just at a different level of generalization, there are differences in design approaches, but the goals are the same - to display information, speeding up the decision-making process as much as possible. The third type is a total mistake and our BI pridefulness - to build dashboards for everything. We all seem to have already understood that the purpose of data exploration is inherently vicious for dashboard-focused BI tools (such as the holy trinity of scoreboard, powerBI and click). We feign that this is not the case, create "Frankensteins" from reports with a bunch of filters and switches in order to maximize its opportunities for unpredictable user research scenarios. Vendors are promoting research features for casual users (like web edit) and wrangling tools (like tableau prep) for similar reasons.
But the truth is - this is too little for analyst, and too much for manager. Well, no, some of your colleagues using it ad hoc do not disprove this fact.
Also, we can notice rapid spread of
notebook-tools, which combine convenient code-based (sometimes drag-and-drop) querying in sql / python and ad-hoc data visualization into charts / graphs. Such a "BI notebook" contains up-to-date data and context - everything you need to adapt and change analysis, visualize, describe, share and check someone else's at different stages. These conditional "BI notebooks" have put points on the board, entrenching upon a good p
art of analysts' time spent on BI tools. This idea is specified in the articles here and here, including, however, Count tool ad - probably a really good one (an attempt to humanize the old school thing like jupiter and zeppelin). Will data wrangling notebook kill the scoreboard and click functionality? - nfi, but there’s more vim in it!
So, BI notebooks assist analysts in their work and help to examine data by extracting fast responses to questions, but this is not an interface for a decision-maker. And what is ultimately going to be the dominant analytical workflow for decision makers?
In fact, the
new BI is aimed at eliminating a temporal and conceptual block between the decision maker and the insight-decision-action and:
- seeking a new form of self-service, eliminating not only coding, but also data search, drag-drop development, slicing and part of data analysis, and possibly even reducing the self-serve component itself to zero as a source of unnecessary losses,
- combining the advantages of an agile and variable BI notebooks and pre-configured, personalized dashboard.