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BrandPost: How BPO Machine Learning Can Boost Revenue and Simplify Recruiting

By Christine Barr

One of the most overlooked truths about AI’s Machine Learning (ML) is that the ML is only as powerful as its training. And the more datasets from just as many diverse environments it is trained with, the more powerful the results. That kind of ML delivers better answers and the ability to spot patterns and pattern deviations that few enterprises can find on their own. 

But some enterprises opt to hold onto their core datasets and only outsource datasets they consider to be non-core. This can undermine any Business Process Outsourcing (BPO) strategy for two reasons. First, core datasets are presumably where the enterprise’s most critical data resides. Why choose to not subject that data to the most extensive analysis possible? Second, by deliberately withholding those critical datasets from the ML analysis, the results are, at best, skewed, and at worst, misleading. Without knowing that other core dataset information, the analysis will only draw conclusions from what it is given, depriving the enterprise of critical but undiscovered patterns.

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