Do you feel that you don’t understand where you are in any given workflow? Does IT give you a daily report of job failures but you have no sense for what really happened overnight or during the day? Summarizing the logs is insufficient for gaining a true understanding of what’s important.
Separate the “Watcher” from the Task
The secret to getting meaningful information is to separate jobs or tasks from the monitoring process, “The Eyes”. Why?
The same job or task may have:
· Different audiences
· Different escalation points
· Different escalation times
RyanEyes allows you to have multiple independent and customizable views with distinct notification times. Imagine that you could design your own process for monitoring your data on the fly. What would you do? You might ask your system to tell you the following:
· Which milestone jobs finished more than 30 minutes late?
· Which jobs never started?
· Did someone perform their manual work when prompted by the completion of the automated jobs that preceded it?
· Did the crucial client deliverable complete when they required it (as opposed to finishing in your more rigorous schedule)?
Once the monitoring tool is divorced from the job chain it is easy to achieve all of these goals and many more. The key is that the tool needs to have the ability to pick any job at any point in a batch run and track all aspects of the job. In the event of an issue occurring (and the user gets to decide what counts as an issue), the tools can notify someone for handling. A staff member can be notified the minute that a pricing job fails. The accounting supervisor can receive an escalation notification if any of the pricing jobs starts more than 30 minutes late or finishes more than an hour late. The Head of Operations can see at a glance that all pricing, trade communication and reconciliations were completed by the end of the day. For each person the tool monitors the same data but reports it in ways that are important to each user.
So, don’t feel that you don’t understand your data. Make your data like you enough that it wants you to know when there is an issue that matters to you. Make sure that you and your data come to an understanding!