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Every day, more and more organizations are deciding to make the move to the cloud for data management. The cloud is no longer considered just a place to store data, but it is now seen as essential in supporting critical innovations in advanced analytics, data science, and AI. — While cloud-based workloads grow in number and size, organizations are dealing with extreme challenges in regards to data preparation. Cloud data lakes could become extremely difficult to navigate due to the raw, diverse and very often unstructured data. With the lack of good data preparation processes and strategies, users can become overwhelmed. Productivity can decrease extremely because users find it difficult to get accurate data that has been properly structure for their usage.

In this webinar, David Stodder and Will Davis will teach you how to overcome your data preparation challenges while adopting the cloud for data management. They will also go over the six data preparation steps, and how to accelerate the conversion of raw, cloud-based data into well-prepared outputs for data warehousing, analytics, and Machine Learning. Additionally, they will go over how to leverage data prep to get the most from cloud data platforms like AWS, some of the best practices for increase self-service data prep for data engineers, data scientist and data analyst. Lastly, they will cover how to improve data governance while using the six data preparation steps, and Trifacta’s data preparation process.

Featured Speakers:
David Stodder, Senior Research Director Business Intelligence, TDWI
Will Davis, Senior Director of Product Marketing, Trifacta

""Trifacta brought an entirely new level of productivity to the way our analyst and IT teams explore diverse data and define analytic requirements. Our users can intuitively and collaboratively prepare the growing variety of data that makes up PepsiCo’s analytic initiatives.""

""We were actually able to shave the amount of time it took to do the analysis by [a factor of] six. Rather than having to do a tremendous amount of analysis, we’re actually readily able to start getting incremental data products out quickly.""