Data Integration Challenges
While data integration is an essential component of business decision making, it also comes with some major challenges. The data sources are unique and so are their formats unless they belong to the same organization or possibly the same department in a company. In practice, this is never the case and the data sources present significant variations in types, formats, velocities, and volumes. Further, the quality of data can also differ with sources. Problems like incomplete data, wrong formatting, missing entries, and incorrect entries, are difficult to handle.
The volume of data is on an exponential rise making it really difficult to accommodate in warehouses without incurring significant costs for integration and maintenance. The large volume of data that is both structured and unstructured is termed as Big Data. This data is so huge and complex that traditional technologies cannot store or process it sufficiently.
Another big challenge is that data keeps changing every second and there cannot be a point where one can be sure of having all the required data at hand for decision making. Data integration can happen at one point of time but there is quite a possibility of this data changing the very next second. Most data warehouses are manually operated so how do we keep track of the data that is changing in the real time?