As an analyst, you can perform the most sophisticated analysis and draw the most compelling conclusions, but without a way to share these with others, your hard work stays with you. So how to best communicate when it comes to quantitative data? In a nutshell: words and pictures.
Tweet: How to harness the power of pictures
In this blog, we offer practical suggestions on how to tell compelling stories through data visualization. (more…)
When we rely on data to make decisions, how do we tell what is a meaningful signal and what is merely noise? Data is neither, in and of itself, as Stephen Few reminds us in his latest book: “Signal: Understanding What Matters in a World of Noise.”
Tweet: How to Separate Noise from Meaning in Big Data
Few has written a series of books about harnessing visualization to aid in analysis. In “Signal,” he takes a broader look at analysis, focusing on the idea of “sensemaking” – that is, deriving meaning from data that can be used to empower decision makers. This is especially relevant for data sets that are large and unfamiliar. Here’s how you can apply some of these techniques to help you understand a set of data and what it might be telling you.
November 1, 2017, may someday be remembered as the date that analytics irrevocably took over baseball. That’s when the Houston Astros won the World Series. Theirs is the latest championship clinched by a team relying on modern analytics in a sport filled with revered old traditions.
Tweet: In Baseball and in Business: “Adapt or Die”
Baseball has changed significantly in the past 15 years, with analytics upending 150 years of conventional wisdom. So far in this Practical Analysis series, we have focused on statistical tools and concepts. Baseball provides a compelling example of how these can be applied, sometimes with astounding results. The rise of analytics in baseball also offers a cautionary tale about embracing – or ignoring – empirical analysis. In the words of one baseball insider, organizations must “adapt or die.” (more…)
In Part I of my Practical Analysis series on analysis and statistics, I talked about statistical concepts. Understanding the concepts is essential, but we also need to know the tools to put them to work. Here we discuss tools for applying statistical concepts and the importance of using the right tool at the right time.
There are two sides to what we do with these tools. As analysts, we can think of ourselves as members of a team competing to prove the truth. We’ll call this “the game.” The defensive side is working to defend our claims, using our tools and data. On offense, we work to refute the opposing team’s claims – at least when they differ from ours. The theories of Statistics provide the rules and boundaries of the game.
Tweet: Practical Analysis: Our Statistical Toolbox
Using Deborah Rumsey’s books “Statistics for Dummies I & II” for background, let’s look at a few particularly useful tools in an analyst’s toolbox.
Where do statistics fit into the world of analysis? Well, analysis allows us to make objective arguments to discover the truth based on actual observations – that would be data – as opposed to subjective claims based purely on intuition or potentially biased points of view.
Tweet: Practical Analysis: How Statistics Help Us See the “Gray”
But observed data still comes with an inherent degree of uncertainty. Where math tends to be “black and white” precise, statistics deals in the realm of “gray.” Statistics allow us to factor in uncertainty so we can draw meaningful conclusions with confidence. In this blog post, we will take a look at statistics’ fundamental concepts and tools. (more…)
In the first post of the “Practical Analysis” blog series, I suggested that numeracy will become an increasingly essential skill set in the 21st century, and that everyone, in one way or another, will become an analyst. But what does it actually mean to be analyst? There is no one answer. The possibilities range from someone who uses a working knowledge of analysis in their day-to-day work to hotshot data scientists at companies like Amazon and Google.
Tweet: Practical Analysis: Learning from the Experts
If you’re involved in any way with analyzing information, you undoubtedly are acquainted with the term “big data” by now. There is certainly a proliferation of electronic data becoming available in increasing abundance. But just because there’s more of it, is it really that much different from the data we’ve had right along? I’d argue that the basics are still very much the same but with the addition of both rich possibilities for finding meaning and daunting challenges for filtering out the noise.
Tweet: Numeracy: Analytical Skills for the 21st Century
In this blog series, which I’m calling Practical Analysis, I’ll explore the emerging role of the analyst in helping to answer some questions that I’ll discuss below and in building a body of knowledge to take advantage of the data deluge. Hopefully by the end, you’ll be completely jazzed about your future as an analyst!