Data Preparation in the Big Data Era

Best Practices for Data Integration

Data Preparation in the Big Data Era

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Preparing and cleaning data is notoriously expensive, prone to error, and time consuming: the process accounts for roughly 80% of the total time spent on analysis. As this O’Reilly report points out, enterprises have already invested billions of dollars in big data analytics, so there’s great incentive to modernize methods for cleaning, combining, and transforming data.

Author Federico Castanedo, Chief Data Scientist at, details best practices for reducing the time it takes to convert raw data into actionable insights. With these tools and techniques in mind, your organization will be well positioned to translate big data into big decisions.

  • Explore the problems organizations face today with traditional prep and integration
  • Define the business questions you want to address before selecting, prepping, and analyzing data
  • Learn new methods for preparing raw data, including date-time and string data
  • Understand how some cleaning actions (like replacing missing values) affect your analysis
  • Examine data curation products: modern approaches that scale
  • Consider your business audience when choosing ways to deliver your analysis

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Federico Castanedo

Federico Castanedo is the Chief Data Scientist at where he analyzes massive amounts of data using machine learning techniques. For more than a decade, he has been involved in projects related to data analysis in academia and industry. He has published several scientific papers about data fusion techniques, visual sensor networks and machine learning. He holds a Ph.D. on Artificial Intelligence from the University Carlos III of Madrid and has also been a visiting researcher at Stanford University.