Slate's clean, functional design ensures that users do not need technical expertise to maximize its powerful capabilities. The intuitive interface and straightforward systems simplify training needs and empower users to be up and running quickly with their varied business processes. It is helpful to understand a few important elements of the Slate implementation process before attending the Fundamentals of Slate training.
Best Practice - The best way to learn about Slate is to use it. A test environment may be provisioned to try out tools and build and test processes with actual data in a playground. While it may feel daunting to “go live” before every part of the process is built, it is strongly recommended that certain tools be utilized in the production environment when initial work on a particular project is completed.
The sooner staff access and use the Slate administrator screens, the sooner their comfort level with Slate will grow.
When to "Go Live"
One of the biggest differences between Slate and other systems is that there does not need to be one system-wide “go live” date. Slate is designed to “go live” gradually as tasks and projects are accomplished. For example, when communications and person records are set, these may “go live” before the event management and appointment scheduling process is ready to launch.
A sample Slate “go live” schedule may look like this:
|Tool / Process||"Go Live" Date|
|Fields and Prompts||January 1|
|Entities, Forms, Tabs||January 15|
|User Permissions||February 15|
|Upload Dataset||March 1|
|Event Management||March 15|
|Deliver Communications||April 1|
|Case Management Structure||April 15|
|Reader, Checklists, Materials||May 1|
|Data Integration||June 1|
While Slate does offer a fully-featured test environment, it is a best practice to develop all business processes in the production database. Rest assured that these processes will not be available to the general public until they are ready. Furthermore, it is relatively easy and safe to adjust and manage data and procedures in the production database.