Work data management in Control Room
Work data management for Control Room is a foundation and set of functionality that allows you to scale, manage and analyze your processes in a flexible manner.
The vision for work data management
Our vision for work data management, in short, is _ "Built-in robustness, connectivity, scalability, analytics and reporting to ANY automation use case "_.
Multiple aspects need to be taken into account when building business-critical RPA processes. Just having the robot to run is only a part of the bigger picture, and typical questions that arise during development and delivery are, for example:
- How do I input data to the robot or get out results?
- How do I scale the workload efficiently over multiple execution environments?
- How do I send the failed items to be processed manually?
- How do I automatically rerun only the failed data items? I don't want to rerun everything.
- How do I integrate system X with process Y?
- How can I be sure that the robot has processed the right information?
- What happens when the execution environment crashes mid-run before the log file gets generated?
- How can I be sure I don't lose any data if the robot crashes?
- How do I measure the value generated by the process?
- My process executes 500000 transactions in a month. How can I stay on top of what is happening?
- I would like to know how many X, Y, and Z the process has gone through today. How do I do that?
Typically finding solutions to all these questions would take a lot of time and resources. Instead of having to develop custom libraries, setting up databases, monitoring systems, analytics software, and figuring out how to use and maintain all that in the future, Control Room work data management in conjunction with RPA Framework provides a standard and best practice answer to all of these questions with a minimal effort from the user.
A core concept of work data management is the work item. Work items are the entities used in Control Room to store any data meant to be processed by robots. By using work data management to handle every data item processed in the process by work item basis, the state of processing can be kept always up-to-date. Even if the robot crashes unexpectedly, the state of each work item stays known and can be re-processed or handled manually.
Work items can be individual pieces of data that your process handles — invoices, URLs, or customer support tickets. Each work item can contain both input metadata for robots processing them as well as output data and output files.
In the Control Room, Work Data Explorer can be used to manage work items in processes from the UI. Available user actions include creating, running, modifying, retrying, and marking work items as done according to different use cases.
Process run maps the data handled in single process execution, even when the execution contains multiple work items or is processed with multiple parallel robots. Process run ends up in an Unresolved state if there are failures in processing the work items and is considered complete when all the failed work items are retried successfully or resolved by the user. A common pattern is to set automatic notifications to track processing failures. The user can then look up and solve the issues in the unresolved process runs from the Work Data Explorer.
In a process consisting of multiple steps, e.g., implemented with the "producer-consumer -pattern", the status of work items can be tracked on step level. For example, when the first step reads an Excel file of customers and splits it into work items, each containing information of one customer, and the second step processes them, the status of the input Excel files can be tracked separately from the customer information.
Scaling over multiple execution environments
The work data management also allows splitting the work to multiple execution environments in a one-to-many processing style. It can be used to process huge volumes of data efficiently. For example, some parts of a complex Enterprise process can be parallelized to hundreds of Control Room containers, making it possible to take advantage of the Cloud scale and efficiency without hosting your own infrastructure.
Business user friendly UIs
Business user friendly UIs make inputting data like Excel files to the robots to process a breeze, while extensive APIs help developers implement and debug all kinds of complex data flows. Data integrations can be done in a standard way while also allowing different trigger methods for the processing.
Measuring and monitoring
Measuring value generated and monitoring that everything works correctly are also important aspects of RPA. Work data management provides standard metrics like error rates, run times, and value generated as time-series graphs but also allows digging deeper into the process when the content of work items is mapped. Process mining built-in!
To add or read work item data in your robots, use the RPA.Robocorp.WorkItems library.
- Development guide for using work items
- Keyword documentation for RPA.Robocorp.WorkItems
- Example Robot — A robot showing the producer-consumer work item model on the Robocorp Portal