This section is meant to help those with solid SQL knowledge sharpen their analytical thinking ability. We’ve interviewed Analytics managers and re-created some of the problems they shared with us using fake data. These problems will force you to think critically about not just SQL syntax, but about the meaning behind what you’re measuring.
You should have a strong handle on SQL before attempting these problems. If you need a refresher, start here.
Analytics cases: Yammer
Yammer is a social network for communicating with coworkers. Individuals share documents, updates, and ideas by posting them in groups. Yammer is free to use indefinitely, but companies must pay license fees if they want access to administrative controls, including integration with user management systems like ActiveDirectory.
Yammer has a centralized Analytics team, which sits in the Engineering organization. Their primary goal is to drive better product and business decisions using data. They do this partially by providing tools and education that make other teams within Yammer more effective at using data to make better decisions. They also perform ad-hoc analysis to support specific decisions.
The Yammer analytics philosophy
Yammer analysts are trained to constantly consider the value of each individual project; they seek to maximize the return on their time. Analysts typically opt for less precise solutions to problems if it means investing substantially less time as well.
They are also taught to consider the impact of everything on the company at large. This includes high-level decision making like choosing which projects to prioritize. It also influences the way analysts think about metrics. Product decisions are always evaluated against core engagement, retention, and growth metrics in addition to product-specific usage metrics (like, for example, the number of times someone views another user’s profile).
Engagement dips—you figure out the source of the problem.
The product team is thinking about revamping search. Your job is to figure out whether they should change it at all, and if so, what should be changed.
A new feature tests off the charts. Your job is to determine the validity of the experiment.