You evaluate individual performance before entrusting a new hire with a big task. Science suggests you should apply the same principle to teams.
Teams, as we've all experienced, aren't simply the sum of their parts. Through good mojo, friendly competition or complimentary talents, some groups wildly outperform what their individual members could accomplish working alone. But sadly, the converse is also true. Some teams produce less than the same people could crank out alone in a cubicle.
What separates the one sort from the other?
The ability of a team to gel together seems magical sometimes, something akin to the inexplicable chemistry that you either do or do not have with a date, but science, it turns out, actually has useful lessons to offer on how you can better ensure the teams at your company have chemistry.
A post by Dr. Christian Jarrett, a psychologist and author of the Rough Guide to Psychology, on 99u recently laid out five evidence-based techniques for getting more out of your teams, including one idea that applies a fundamental principle of car shopping to team selection. In short, writes Jarrett, take it for a test drive:
Individual assessment is such a fundamental part of working life, yet we often take it for granted. If you want the best person for a job, you put the candidates through their paces to see who comes out on top. The basic assumption is that if they do well in the test context, they'll also excel on future projects. It turns out the same principle applies to groups – U.S. researchers showed in 2010 that a team that does well in one situation will tend to do well on other challenges too.
He concludes that, "it's a mistake to think that putting together a bunch of skilled individuals will automatically create a gifted team." Social sensitivity matters as much as smarts, according to the same research (which, as a side note, means teams of mixed gender generally outperform all males groups because of women's tendency to be more socially sensitive). So before you hand over a huge pile of work to a newly formed team, consider giving them something smaller to gauge how well they'll perform together.
Interested in the other four research-validated ideas to improve your teams' performance? Check out Jarrett's uniformly interesting post. Or, if you're looking for other expert tips to encourage teamwork, architects and designers suggest that office design really matters, while Phil Geldart, author of In Your Hands, the Behaviors of a World Class Leader, has outlined seven fundamental building blocks of successful teams.
What's your top tip to optimize teamwork?
UMBC CSEE Colloquium
Integration of HBase and Lucene for real-time big data analysis
Yin Huang
CSEE Department, UMBC
1:00 pm Friday, 22 February 22 2013, ITE 227, UMBC
The increasing size of data sets have posed several challenges on real-time big data analysis, Business Intelligence for example, in terms of system scalability and data availability. Business Intelligence focuses on mining the big data, providing multidimensional visualization and thus supporting business decision making, ideally in a real-time fashion. Traditional relational database management systems fail to provide a flexible and stable solution. Several NoSQL database systems have been proposed to tackle these challenges, such as Cassandra and HBase. HBase, however, does not support full-text searching; current implementation of HBase only offers the row-key based indexing. In this talk, we introduce building Lucene index on top of HBase to support multidimensional queries for data mart under MapReduce framework, serving as the corner stone for future data analysis and business report.
Yin Huang obtained his B.S. in Computer Science from Nanchang University in 2009, and studied in Chongqing University for two years for his M.S. He started his Ph.D. program in Computer Science at the University of Maryland, Baltimore County in 2011. In 2012, he interned in IBM Ottawa lab for four months with the focus of using Multicore-Enhanced Hadoop System for Buisness Intelligence. His current research area is database, data mining, and parallel computing.
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