Presented By O'Reilly and Cloudera
Make Data Work
September 26–27, 2016: Training
September 27–29, 2016: Tutorials & Conference
New York, NY

Top data wrangling use cases in enterprise analytics

Connor Carreras (Trifacta)
2:05pm–2:45pm Wednesday, 09/28/2016
Sponsored
Location: 1B 03/04
Average rating: ***..
(3.67, 3 ratings)

What you'll learn

  • Explore how leading organizations, such as PepsiCo, Royal Bank of Scotland, and Kaiser Permanente, are leveraging data wrangling to accelerate analysis processes and uncover new sources of business value by incorporating new data sources that were previously too difficult to work with
  • Description

    Data wrangling has quickly become a hot topic and technology category within the big data analytics industry. Stakeholders across business and IT are hungry to learn the right way to think about applying these new wrangling solutions to the data and analytics efforts of their organization. As with any emerging technology, the leading question from organizations still learning about data wrangling is, “How are other organizations wrangling data and what are the benefits they are realizing?” If this question sounds familiar, then this is the session for you.

    Connor Carreras offers an in-depth review of the most popular use cases for data wrangling solutions among enterprise organizations, explaining how leading organizations, such as PepsiCo, Royal Bank of Scotland, and Kaiser Permanente, are leveraging data wrangling to accelerate analysis processes and uncover new sources of business value by incorporating new data sources that were previously too difficult to work with. Connor also addresses common questions for data wrangling solutions, including: Where do data wrangling tools fit? Who are the ideal users? How are security and data governance managed?

    This session is sponsored by Trifacta.

    Photo of Connor Carreras

    Connor Carreras

    Trifacta

    Connor Carreras is Trifacta’s manager for customer success in the Americas, where she helps customers use cutting-edge data wrangling techniques in support of their big data initiatives. Connor brings her prior experience in the data integration space to help customers understand how to adopt self-service data preparation as part of an analytics process. She is a coauthor of the O’Reilly book Principles of Data Wrangling.