global secure repository
The repository provides a global and secure file store that allows access for all users within an organization. Every file and folder that exists in the repository is versioned and security controlled. The repository also keeps track of the so-called “parental relationships” of the files and folders – we know how the different data relate to each other.
Additonally we create an activity log that keeps track of the “who, what, when, where, why and how” of your changes – you can view this “audit trail” directly from the workbench.
the improve workbench
The workbench is the cockpit for your modeling activities. Here you define your modeling steps and start your simulation activitites. You have access you your favorite tools like R, SAS, NONMEM or others. The workbench automatically connects to the repository where all your data is stored and to the run servers that are responsible for executing your programs.
We have also included a full text search capability that allows you to search all files and folders you have access to. See our tutorial videos to see the improve workbench in action. Tom Rott shows how to create an analysis tree and runs some NONMEM steps.
going visual: analysis trees
An analysis tree contains a set of steps for your analysis. It graphically shows your decision making on a specific analysis in a tree structure.
The visual representation of each activity shows the input and output parameters of each step and how each step is linked to the others. The information highlighted in the tree is highly customizable. The data of all steps performed during your analysis is made available by the run records. They show the “who what when where and why” of your analysis and decisions.
audit trail for regulatory compliance
improve automatically makes your modeling compliant to strong regulation standards. Without the need for the user to explicitly check something in or out we document every step of the process and any changes. You can view the audit trail using the workbench.
improve on premises or in the cloud improve can be used in different operational contexts. You can run improve locally or on local servers, but we recommend running it in the cloud. This is the fastest way to explore modeling with improve. Additonally you can dynamically allocate ressources as needed. And: you always are using the most current version.
improve on premises or in the cloud
improve can be used in different operational contexts. You can run improve locally or on local servers, but we recommend running it in the cloud. This is the fastest way to explore modeling with improve. Additonally you can dynamically allocate ressources as needed. And: you always are using the most current version.