"data teams" entries
Smart data scientists can make big problems small.
Having worked in academia, government and industry, I’ve had a unique opportunity to build products in each sector. Much of this product development has been around building data products. Just as methods for general product development have steadily improved, so have the ideas for developing data products. Thanks to large investments in the general area of data science, many major innovations (e.g., Hadoop, Voldemort, Cassandra, HBase, Pig, Hive, etc.) have made data products easier to build. Nonetheless, data products are unique in that they are often extremely difficult, and seemingly intractable for small teams with limited funds. Yet, they get solved every day.
How? Are the people who solve them superhuman data scientists who can come up with better ideas in five minutes than most people can in a lifetime? Are they magicians of applied math who can cobble together millions of lines of code for high-performance machine learning in a few hours? No. Many of them are incredibly smart, but meeting big problems head-on usually isn’t the winning approach. There’s a method to solving data problems that avoids the big, heavyweight solution, and instead, concentrates building something quickly and iterating. Smart data scientists don’t just solve big, hard problems; they also have an instinct for making big problems small.
We call this Data Jujitsu: the art of using multiple data elements in clever ways to solve iterative problems that, when combined, solve a data problem that might otherwise be intractable. It’s related to Wikipedia’s definition of the ancient martial art of jujitsu: “the art or technique of manipulating the opponent’s force against himself rather than confronting it with one’s own force.”
How do we apply this idea to data? What is a data problem’s “weight,” and how do we use that weight against itself? These are the questions that we’ll work through in the subsequent sections.
Edd Dumbill looks at the hot topics in data for the coming year.
The coming year of big data will bring developments in streaming data frameworks and data marketplaces, along with a maturation in the roles and processes of data science.
Building data science teams, the evolution of data products, and the grunt work of data journalism.
This week on O'Reilly: DJ Patil revealed the skills and qualities of great data science teams, we learned that new data products put emphasis on experiences rather than on the data itself, and Simon Rogers discussed the considerable effort that goes into The Guardian's data journalism.