Data troubleshooting

(Once upon a time) All IT projects used to be transformative – the starting point was tried and tested paper-based procedures. All that was needed was to automate them into a digital system.

The reality was that data usually needed redesign and remodelling, and paper-based or current digital systems were inadequate and failing. Troubleshooting was needed in any new system, which we called “framing” the problem. Where a client appreciated and understood this, then framing problems, constraints and specification and rectification became much easier.

All major digital projects include an element of risk identification and trouble-shooting, so Incorvus teams include experienced software developers and engineers as well as the data management specialists. We build upon established and known systems, but to do this best, we use a team that knows what problems to expect and has a vocabulary of suitable solutions.

Our stories cover more extreme troubleshooting issues, where the data is looked at in a wider context:

Speed of data analysis and data delivery: existing applications are often no longer up to the task. In the case of one global manufacturer we dealt with, their data volumes and complexity had outgrown the time constraints for delivery of analysed information. Matching an appropriate data ontology to a flexible calculation engine was done quickly in prototype, but to complete the system through all relevant and needed areas exposed unexpected problems that were quickly solved. The resulting fast flexible system was hugely successful and resilient for future expansion.

In another case, the core problem of managing detailed and complex payment records and their integration and reconciliation of with external payment system created a difficult environment. Data troubleshooting pointed to the need for a virtual platform to ensure accurate, confidential and secure data exchange.  In the environment of an international service company, this generated a competitive advantage that a larger and more powerful group was prepared to buy the company to acquire the technology.

What both case studies indicated was the importance of data in a complex environment and the need for appropriate datacentric solutions.