The challenge of preparing back-end data resources to support cognitive computing initiatives may be the single greatest factor that hinders progress, some observers warn. "Data scientists spend up to 70% of their time on data collection and preparation, rather than building and deploying predictive models," said Jeff Healey, senior director of product marketing, Vertica, Micro Focus. "While the need for data preparation for model-building would probably remain, having analytic platforms in place that support this critical step of data cleaning and preparation helps to shorten the predictive analytics lifecycle. Additionally, many data science and business analyst teams are being challenged to prove the value of their work in order to secure the time and resources needed to develop and operationalize ML-driven applications. Multiple data sources and different open source systems that require considerable maintenance and resources further complicates the time to value equation."