Asset Data Insight (ADI) looks to be a very promising analysis tool. I realize it is in beta, but I want to share a couple of my impressions from using it over the last couple of days. Perhaps these suggestions can make it to a list of features to be implemented for the coming releases.
I am a bit confused about the future development of ADI and Cognite Charts. There is clear overlap in functionality, and yet key features in ADI are not present in Charts, and vice versa. Will one take over for the other? Will they coexist? If one will be deprecated in the future, my feedback still is relevant for the one that survives.
#1 Make all Chart functionality available in ADI.
The calculation node editor is especially useful, but the interface for customizing how toe visualization looks is better in Charts than in ADI.
#2 Improve the Events layer
Overlaying events, such as maintenance history, on key time series can be very useful. However, the current implementation is lacking. The filtering of events is not useable. I would like to be able to select only events from within a certain data set in CDF, such as SAP workorders or SAP notifications. I also want to be able to customize how these are overlayed, by selecting a color and transparency. There is also an issue with overlapping events, where I need to zoom in a lot for certain events to become invisible. I would like events to be separated from the main time series coordinate system, and instead be shown on its own axis stacked underneath the time series area. Events from different data sets should also be separated again, and by default be given clearly distinguishable colors, so that one can actually get a quick overview of the situation.
# 3 User-defined templates
It is tedious creating a dashboard of 20 similar charts. I would like to be able to define a template that can easily be reused to generate the same Chart but with different assets. Currently I duplicate a Chart, but when removing and adding new assets I need to reformat the time filtering and the lines.
#4 More efficient interface when working with Dashboards
I would like options for applying the same settings (filtering, graph colors) to multiple Charts in a Dashboard at once. I would also like a faster way of moving between Charts present in a Dashboard. Currently I need to click on “Back” in the browser to get to the Dashboard view, and then scroll and click on the Chart I want. Why not have a sidebar that lists all Charts in the Dashboard by name and a small preview?
More than once have I lost my work because the authorization protocol needed to reload my credentials.
#6 Add more Dashboard widgets than Charts
I feel like ADI should take advantage of all the contextualization in CDF. Possible widgets:
- A File lister connected to one or more assets
- Event lister connected to the graphical display of events in a Chart
- Add hyperlinks to other Charts/Dashboards
- The possibility to add custom elements to Charts, such as horizontal and vertical lines. Some of this can be done in Cognite Chart, but is not possible in ADI.
- In root cause analyses, it would be helpful to annotate time series in Charts, for example by adding text annotations, or possible by highlighting certain regions of the time series by adding coloring aka `ax.axvspan(x1, x2)` in matplotlib.
#7 The possibility of structuring Charts/Dashboards in folders
For structure, it would help if I could organize Charts and Dashboards in folders. That would remove the time thief who forces me to spend time looking for a specific Chart/Dashboard.
#8 If ADI and Cognite Charts with coexist, then
I would like to be able to import from Cognite Chart, and vice versa. Some features may be lost, but if two tools as similar as these built by the same company will coexist, then some form of import/export between them needs to be developed.
Again, I want to emphasize that there is a lot I like with ADI and Cognite Charts. I think this could become brilliant tools if their functionality gets developed further, with the main focus on 1) taking full advantage of having CDF as the underlying data engine, 2) adding tools and features that help the users visualize contextualized, industrial data in a better way, and 3) making it more user friendly and the interface more streamlined.