Good news, your data scientist just got a personal assistant

personal asstIf you are or have a data scientist in house you’re in for good news.

Today at Hadoop Summit in San Jose, Pentaho unveiled a toolkit built specifically for data scientists to simplify the messy, time-consuming data preparation, cleansing and orchestration of analytic data sets. Don’t just take it from us…

The Ventana Research Big Data Analytics Benchmark Research estimates the top two time-consuming big data tasks are solving data quality and consistency issues (46%) and preparing data for integration (52%). That’s a whopping amount of time just spent getting data prepped and cleansed, not to mention the time spent in post processing results.  Imagine if time spent preparing, managing and orchestrating these processes could be handed off to a personal assistant leaving more time to focus on analyzing and applying advanced and predictive algorithms to data (i.e. doing what a data scientist is paid to do).

Enter the Pentaho Data Science Pack, the personal assistant to the data scientist.  Built to help operationalize advanced analytic models as part of a big data flow, the data science pack leverages familiar tools like R, the most-used tool for data scientists and Weka, a widely used and popular open source collection of machine learning algorithms. No new tools to learn. In the words of our own customer, Ken Krooner, President at ESRG “There was a gap in the market until now and people like myself were piecing together solutions to help with the data preparation, cleansing and orchestration of analytic data sets. The Pentaho Data Science Pack fills that gap to operationalize the data integration process for advanced and predictive analytics.”

Pentaho is at the forefront of solving big data integration challenges, and we know advanced and predictive analytics are core ingredients for success. Find out how close at hand your data science personal assistant is and take a closer look at the Data Science Pack.

Chuck Yarbrough
Director, Big Data Product Marketing

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