Scaling Data Connectivity While Handling Real-World Complexity As the need for more data from more partners rises, so does the need for methods of data exchange that simplify the process. Finding and vetting data partners can take months, and it’s hard to trust data partners not to mishandle or misuse your data. Before you can even evaluate the benefits of connecting data sources, preparing to connect data takes a great deal of manual effort. Many of the steps are lengthy, costly, and cumbersome. Once the data is approved, there’s still concern about how to make regular updates, and if the data doesn’t pass the evaluation, you must begin the time consuming, lengthy process over from the beginning. According to IT leaders, the top complexities of data sharing and integration projects are: Alignment on security protocol Alignment on file formats Alignment of data element normalization/standardization Downtime associated with data refreshes Custom development/coding required for each integration When we look toward the future of data collaboration, we see a major push toward interoperability and a focus on digital transformation. With this also comes the need for more precise and accurate data from more (and more varied) sources, but real-world complexity has historically been a roadblock to achieving this. 5