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 WiseAthena.com, 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
Federico Castanedo is the Chief Data Scientist at WiseAthena.com. Involved in projects related to data analysis in academia and industry for more than a decade, he’s published several scientific papers about data fusion techniques, visual sensor networks, and machine learning
