There was a question on the Slack about dashboards using SMA data and I wanted to share a couple of examples of dashboards we have created.
This is a page from our Replacements dashboard that shows the devices that are up for replacement in FY26. The numbers across the top are normally the names of the liaison that supports the computers, based on the computer location. The table also normally includes system names. This allows each liaison to easily view their systems to review the devices up for replacement in a given FY.
This dashboard relies on some specific data points we have added to our assets:
- Locations - All devices in inventory are assigned a location. The location includes the liaison that supports that building.
- PO Data - All assets (and therefore devices) have a PO number associated with them that translates to a purchase date.
- Replacement cycle - All assets also have a replacement cycle value that indicates how many years it will be in service. This, combined with the PO data allows us to calculate when a given device is up for replacement.
This is the overview from our replacements dashboard. Again, machine and liaison names have been removed.
This last example is from our classroom equipment dashboard. It shows the equipment in each classroom broken down by equipment type and the replacement cost based on the purchase price of the equipment. As with our computer replacement dashboard, this example relies on some data points we have added to our appliance.
- Classroom custom asset type
- Name of the classroom
- Number of seats
- Instructor computer type
- Classroom Equipment custom asset type
- Serial number
- Equipment type
- Purchase price
- Date received
We have plans to add replacement cycle information to these assets, which will allow for better forecasting of equipment replacements. This is more complicated than our computer replacement cycle which has much more uniform timeframes for equipment (desktops are six years, laptops are four years).
These are just a few examples of ways we have created dashboards using the SMA database. Both of these examples are based on PowerBI dataflows that connect to the database using the reporting user and load the data into Dataverse tables. This allows the visuals to be updated on a regular basis using scheduled updates.