Ten Mistakes to Avoid in Building Out a Data Science Program
TDWI Member Exclusive
January 31, 2017
By Fern Halper and Martin Pacino
The data and analytics landscape is changing. Organizations are moving past traditional structured data and are embracing text data from emails, machine-generated data, and streaming data to enhance their analyses. Data science—an interdisciplinary field that extracts insights from data—is an outgrowth of this evolution. A data scientist is a fairly new name for a well-established group of professionals who engage in statistical analysis, data mining, and exploration. Many companies are still in the early stages of their data science efforts. In this Ten Mistakes to Avoid, TDWI’s Fern Halper and Martin Pacino identify the mistakes most detrimental to building out a successful data science program.