Data conflation is a process that involves combining multiple datasets into a single, unified dataset. The goal of data conflation is to improve data accuracy and completeness by merging data from multiple sources. This can be particularly useful in situations where there are multiple sources of data on a particular topic, but no single dataset is complete or accurate enough to use on its own.
Data conflation can be a complex process that requires careful planning and analysis. Before conflation can occur, the datasets must be carefully evaluated to ensure that they are compatible with one another. This may involve cleaning the data, resolving inconsistencies, and standardising the data to ensure that it can be combined effectively.
Once the datasets have been evaluated and prepared, they can be combined using a variety of techniques. This may involve using statistical techniques to merge the data, or simply concatenating the datasets together. The resulting unified dataset can then be used for analysis, reporting, or other purposes.
One of the main benefits of data conflation is that it can improve the accuracy and completeness of the resulting dataset. By combining data from multiple sources, it is possible to create a more complete picture of a particular topic. This can be particularly useful in situations where there are gaps in the data or inconsistencies between different sources of information.
In the financial services industry, data conflation is an important consideration for companies that need to analyse large amounts of data. By combining data from multiple sources, companies can gain a better understanding of their customers, their operations, and the markets in which they operate.