This week, we’ve collected articles that cover how to best visually present data to your audience. We share how to tell a story with data visualization and highlight six common data visualization mistakes and ways to avoid them. There’s also a new Marketing Performance Measurement report that we found super helpful and we take a look at the interdisciplinary relationship involved in data science, automation, validity, and intuition.
by Eva Murray, contributing writer for Forbes.com
The business world is data-driven. And it’s only effective if that data is presented in a way that the end-audience can consume, understand, and take action (if applicable). Murray walks through three clear strategies to tell a story with data visualization. She helpfully offers concrete examples along the way to illustrate each approach. Learn how to improve your data storytelling skills.
by Marie Fincher, contributing writer for KDNuggets.com
Data is a powerful tool that is used to inform important decisions. But not everyone is fluent in reading and understanding large amounts of data. Therefore, it is crucial that data is presented visually, in a simple, easy-to-digest way. This is done effectively in visual form—charts, graphs, infographics, etc. But there are ways you can get it wrong. Read about the six common pitfalls of data visualization and how to avoid them.
report developed by Ebiquity, featuring research from Forrester
Marketers are expected to demonstrate the ROI of their marketing investments. As a result, it is important for marketing leaders to deploy the best tools to take on a data-driven approach. As competition, financial markets, and digital disruption take a toll on brands, now is the time to understand how new data-driven approaches to marketing can improve bottom-line performance.
Ebiquity covers this and more in a new report, featuring research from Forrester. The report comprises a part of Forrester’s Marketing Measurement and Insights Playbook. It highlights new approaches to digital measurement. It also covers how to build a unified approach to attribution. Research on the growth of advanced marketing measurement is also featured. Access the full report below.
by Andrew Silver, contributing writer for KDNuggets.com
Silver offers up his own enhancements on the widely known and accepted Data Science Venn Diagram (originally developed by Drew Conway). He proposes two additions—the differentiating of statistical applications (multivariate vs. non-multivariate) and the addition of discipline essences (i.e., the main contribution or function of each skill set). Read about his examination of the interdisciplinary interplay involved in data science, focusing on automation, validity, and intuition.
Did you see an interesting article in the last week? Share it with us! Send it to astuttle [at] lityx.com.