Storytelling is an ancient and precious human ability that has been rejuvenated in the digital age. Over the last decade, there has been a notable surge in the recognition and application of data storytelling, both in academia and industry. Recently, the rapid development of generative AI has brought new opportunities and challenges to this field, sparking numerous new questions. These questions may not necessarily be quickly transformed into papers, but we believe it is necessary to promptly discuss them to help the community better clarify important issues and research agendas for the future. We thus invite you to join our workshop (Gen4DS) to discuss questions such as: How can generative AI facilitate the creation of data stories? How might generative AI alter the workflow of data storytellers? What are the pitfalls and risks of incorporating AI in storytelling? We have designed both paper presentations and interactive activities (including hands-on creation, group discussion pods, and debates on controversial issues) for the workshop. We hope that participants will learn about the latest advances and pioneering work in data storytelling, engage in critical conversations with each other, and have an enjoyable, unforgettable, and meaningful experience at the event.

Data storytelling plays an important role in data workers' daily jobs since it boosts team collaboration and public communication. However, to make an appealing data story, data workers spend tremendous efforts on various tasks, including outlining and styling the story. Recently, a growing research trend has been exploring how to assist data storytelling with advanced artificial intelligence (AI). However, existing studies may focus on individual tasks in the workflow of data storytelling and do not reveal a complete picture of humans' preference for collaborating with AI. To better understand real-world needs, we interviewed eighteen data workers from both industry and academia to learn where and how they would like to collaborate with AI. Surprisingly, though the participants showed excitement about collaborating with AI, many of them also expressed reluctance and pointed out nuanced reasons. Based on their responses, we first characterize stages and tasks in the practical data storytelling workflows and the desired roles of AI. Then the preferred collaboration patterns in different tasks are identified. Next, we summarize the interviewees' reasons why and why not they would like to collaborate with AI. Finally, we provide suggestions for human-AI collaborative data storytelling to hopefully shed light on future related research.

Data science is a rapidly growing field that combines statistics, mathematics, and computer science to extract insights and knowledge from data. As a data scientist, you need to be proficient in a variety of tools, techniques, and concepts to effectively analyze and visualize data. To help streamline your work, we have created the ultimate data science cheat sheet. The cheat sheet covers all the essential topics in data science, from the basics of statistics and probability to advanced machine learning algorithms and deep learning techniques. It is designed to be a quick reference guide for data scientists, providing a comprehensive overview of the key concepts and tools used in the field.

Finding the right framing is key. As an educator, Alejandro Rodríguez knows that even the most complex concepts can become approachable if you choose your communication method wisely. This post is ostensibly an introduction to confusion matrices and classification metrics, but it's also a masterclass on the power of a simple, well-chosen example. Data visualization is about making massive amounts of information accessible and interpretable. Its success depends on a series of design decisions, both small and big; Weronika Gawarska-Tywonek's excellent primer will help you understand how color palettes work, and how to go about choosing the one that's most appropriate for the task at hand.

Automated data storytelling is the future of analytics. That's the argument put forth by James Richardson during a conference hosted by automated data storytelling vendor Narrative Science (as reported here). I've spoken to Mr. Richardson on a couple of occasions and deeply appreciate his understanding and enthusiasm for data storytelling. He's been a champion for data storytelling at Gartner for years. It is his modifier'automated' that worked me into a Stephen Few -style lather.

Automated data storytelling is the future of analytics. Its rise, meanwhile, could signal the demise of self-service analytics. That was the premise of a presentation by James Richardson, a research director at Gartner who spoke on Feb. 24 during a virtual conference hosted by data storytelling vendor Narrative Science. According to Gartner, data storytelling will be the most widespread means of consuming analytics by 2025. In addition, by then a full 75% of data stories will be automatically generated using augmented intelligence and machine learning rather than generated by data analysts.

There are a multitude of reasons why an organization might outsource the analysis of data they have already collected. Companies frequently partner with third-party providers to drive the speed and sophistication of their analytics insights and to connect these insights to action. Amaresh Tripathy, global business leader of analytics at Genpact, said his company has seen a significant uptick in demand for analytics outsourcing in the wake of COVID-19 challenges. "Increasingly, we see such relationships become strategic, where partners provide insights and take part in enabling the action as a result of those insights with digital tools and change management activities," Tripathy said. This often works as a center of expertise model, where the partner brings together a cross-functional team that combines business and technical skills with industry accelerators.

At first glance, data science always appears to be an intricate field -- or maybe I should say a collection of fields. It very broad vague, and one can argue complex. But, the truth is, data science can be defined very simply using one sentence. Data science is the field of interpreting data collected from different resources into useful information. Or in other words, it is all about listening and translating the story some data is trying to deliver.

In the previous articles I looked at how data is being considered as currency today, and that to start to realise value from data it first needs to be sorted. I discussed how challenging this was not only because of the large quantities of data but because of something called'data bias' which can skew our perception of the results. We are creating more data than ever before in our history, with most ending up as'data exhaust' (a waste by-product that is not used or stored). Of the data we do store most is archived, never used and its value never realised. Alongside this we have a scarcity and lack of diversity in the people that sort our data and extract the business value and insights we need.

Data is playing a larger role in day-to-day business conversations than ever before. The ability to communicate with data is now a necessity for business leaders, frontline employees, and everybody in between. People who may have easily avoided discussing data in the past are finding numbers being thrust upon them. When data is a foreign language to you, it can be frustrating to not understand what's being said or be able to use it effectively in communications with others. Not being conversant or fluent in data is quickly becoming a liability in today's fast-moving data economy.