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» Handling increased data size: The shared data set is often much larger than originally scoped, which creates problems with the data extraction process. You’ll likely need a scripting language to automate the breakdown and extraction process, which may require additional IT assistance. The reverse process must also occur for data consumers.
» Decrypting sensitive data: If the data set includes sensitive information, the output files will likely need to be encrypted, masked, or redacted, which may require additional IT assistance. If the data set is encrypted, encryption keys must be securely shared between the parties via a separate process, and the data consumer must decrypt the shared data.
» Changing file formats and schema: It may be necessary to change the file format multiple times if additional database attributes must be shared. When table attributes change on the data provider’s end, a corresponding change must also occur on the data consumer’s end.
The accumulation of all these steps results in slow and painful processes for both data providers and consumers. All of this must happen before any attempt to analyze and develop insights from the data, which creates time-to-value delays.
Usually,­the­delays­and­difficulty­don’t­end­with­just­the­transfer­ effort.­For­example:
» Sharing data in real time: More IT assistance is needed if the data set could be shared in a more real-time fashion, rather than being sent only once per night.
» Cleaning data: The import process has problems and the data isn’t as clean as anticipated. For example, the data extraction may contain special characters that should have been disregarded. This means the data provider must build more sophisticated data extraction processes, resulting in more IT assistance, costs, and delays.
To­ protect­ against­ failures­ during­ the­ file­ transfer­ process,­ on­ either the extraction and/or import side, both the data provider and data consumer must incorporate special software code or scripts to monitor the transfer and automatically restart the pro- cess­in­the­event­of­failure.­This­means­greater­effort­and­longer­ delays to develop insights and derive value from data.
CHAPTER 2 Understanding Traditional Data Sharing Challenges 13 These materials are © 2018 John Wiley & Sons, Inc. Any dissemination, distribution, or unauthorized use is strictly prohibited.