Structuring System Data into Useful Information

Enterprise software systems create and store massive amounts of data created by the processes carried out within them. But, that data isn’t automatically useful information for assessing those processes. System data represents the translation of the reality of the processes as understood and enacted by people into the structure of the system. What data is captured and how it’s structured represents what happened in the system, but the relationship of that data to reality outside the system is not always clear. To be useful for answering questions about what’s happening in the real world, system data has to be translated back into an information architecture that gives it coherent meaning in terms of the actual workflows, intentions, and concerns of the people managing and doing the work.

This talk is a case study of doing this work to structure analytic data from a new Library Services Platform to be meaningful information about the physical collections and fulfillment processes at a large academic library. I’ll talk about what makes data be information, and then show how created an architecture to make our data meaningful information for assessing our processes and to support “”informed”” decision making.

I’ll talk about:

• Working with those who manage and perform the work to create diagrams that model how the parts of the real world workflows create system data
• Exploring data with pivot tables and Tableau to test assumptions and find edge cases that need clarification
• Documenting the differences that ‘make a difference’ and sharing information back to those doing and managing the processes”