As the volume and complexity of data multiplies, strong competencies and disciplined processes for data and information management have become critical. Research also shows that companies that actively maintain their data get greater leverage from their technology investments, have easier systems upgrades and realize tremendous cost and cycle-time advantages over their competitors. With this in mind, we approach data conversion as the key to system implementations, not as an afterthought. At MIDIOR, we apply systems engineering concepts to all of our data conversion efforts, breaking down the data to the most granular level and “bagging and tagging” it in a structured and logical way. We define data models that are modular and easy to maintain so that data integrity and reliability can be sustained into the future. We document inputs and outputs, identify bottlenecks, evaluate risks and opportunities and establish tools for extracting data and turning it into business information. Finally, we establish success measures, and define data quality scorecards. The net result is that our clients become “masters of their own data domain.” If you have a data conversion effort, data forensics initiative or data remediation challenge, give us a call - our data team will give your data the respect it deserves.
MIDIOR will take responsibility for managing the conversion of fund office data through the life cycle of the implementation project. We apply systems engineering concepts to all of our data conversion efforts, “bagging and tagging” it in a structured and logical way. We will extract your data, stage it (so that it can be reviewed and remediated if needed), map it to the target system, and convert it when your system is ready to go live.
Many times data needs to be cleaned up before it is loaded into a new system. We audit legacy data and identify areas where clean-up is practical or important (or both). We then support business and IT teams with the tedious forensics, remediation, and transformation work needed to make the data as clean and complete as possible prior to loading into the new system.