Matt Yglesias and Johnny Harris over at Vox recently posted a great video about a rather contentious issue when it comes to storytelling and data. That is, whether or not you should always set Y-axis baselines in bar or line charts to zero. I’ve written about this a few times in the past and I wanted to take a moment to add a few points to the debate.

So there was great news from Google recently as they announced the roll-out of a new feature in Google Analytics (GA) which they call calculated metrics. In short, calculated metrics allow you to go beyond the off-the-shelf measures offered by GA by allowing you to create completely new metrics based on your needs.

A metric is simply a unit of measure which helps you quantify something. In order to ensure you choose the right KPIs to measure success you need to understand the different types of metrics you can use and when it's appropriate to choose one metric type over another. In this article I will highlight the different types of metrics available, their pros and cons, and when it's best to use each type.

There are 5 marketing metric categories that can help your organize your KPIs and ensure you have a comprehensive view of performance. This includes Awareness, Engagement, Perception, Experience and Conversion metrics

So there’s a chart making the rounds in the media right now that was used by U.S. Rep. Jason Chaffetz (R-UT), the chair of the House Oversight Committee, during a senate hearing with Planned Parenthood president Cecile Richards. The chart Chaffetz presented offers some valuable lesson in how data can be visually manipulated to deceive, and how we can all be better communicators with data.

I'm a total sucker for geographical data viz and lately I've found myself losing countless hours exploring CartoDB's growing community gallery of maps. Seriously, if you need some visual inspiration today head on over to their site and marvel at the beautiful intersection of geography, data and creativity.

As someone who works in the private sector, or more specifically, in marketing, I’ve seen data applied in some pretty manipulative ways to make a point or validate an untested hypothesis. if you're working in the field of marketing analytics and you care about being truthful you'll always struggle to find the right balance between time, cost and rigour. But not everyone understands the importance of rigour when it comes to truthful interpretation of data, and worse, some don’t care.

Whether you're starting from scratch or looking to evaluate an existing analytics practice, it's important that you review the resources and investment in and output of analytics on a routine basis. I've come across many cases where a client is investing in data and analytics but they end up being heavy on reporting while being light on actual insight.

What’s the ROI of data analytics? Increasing your speed to insight or speed to action is one way to look at the returns on your investments in data