A recent New York Times article highlighted the data “janitor work” that’s a key hurdle for data scientists using predictive analytics to find insights in data. Whether you call it data wrangling, data blending or data munging, they’re all terms intended to describe the difficulty data can present. One statistic often quoted in articles and surveys says the data work is somewhere between 50-80 percent of the data scientist’s total time spent on a single project.

While distributed processing advancements and in-memory analytics have brought both tremendous speed and accessibility to predictive analytics, it’s still the data that drives the decision design and decision engineering processes. And it’s the data that can still bog down the analytics life cycle.

In this session, you’ll learn why doing all the so-called data dirty work is a solid investment in your future. You’ll also learn how smart planning, careful choices and using the right technology can help break up data logjams, speed up data analysis and let valuable insights flow freely.