As a transplanted New Yorker, I was sorely disappointed to learn that the New York Knicks were going to let national sensation Jeremy Lin go to the Houston Rockets. It’s true that there were solid reasons against keeping him: his contract would have cost the Knicks a huge sum and Lin is still a relatively green player.
But while there may be more seasoned point guards out there for cheaper, Lin had important qualities that the Knicks were lacking. The Knicks had talent and a star player in Carmelo Anthony, but somehow Lin became their one truly marketable figure: likeable and relatable to an incredibly broad fan base and able to create excitement around a lagging team.
Every team needs a balance of skills in order to succeed. A few weeks ago, I wrote about how Research should structure their analytics teams to ensure that the function drives decisions rather than merely reports. Analytics teams, when left out of the strategic decision making process, are unable to provide business relevant insights. At the same time, without the right people, even well-integrated analytics teams will not succeed.
Three important skills are required: leadership, quantitative, and technical skills. It’s both difficult and unnecessary to find someone highly capable of all three. The trick is finding an appropriate balance and ensuring that skills are well-matched to team roles.
- Analytics team leaders: These professionals play an integrative role working with different stakeholders to deliver business relevant, quant-supported insights.
- Quantitative analysts: Generally equipped with a statistical background, these professionals should be skilled in data analysis and modeling.
- Technical analysts: Technical roles usually require IT/software engineers with data warehousing/data mining/database administrator capabilities.
MREB members, visit our new insight page to learn more about how to balance leadership, quant, and technical skills on the analytics team.
- Analytics as Competitive Advantage
- The Shiny Object Syndrome in Analytics
- Using Data Analytics for Insight