![]() Semmelweis was an obstetrician who set out to investigate why one of the maternity clinics in which he worked witnessed a high rate of deaths in admitted mothers. One unfortunate example of failed data storytelling can be found in the story of Ignaz Semmelweis. And the cost of failed data storytelling can be tragic. ![]() The last point is important, as neuroscience proves to us that most decisions are driven by emotion rather than logic.Ī failure to present data as a story threatens one’s ability to persuade their audience to take action. ![]() Stories can connect to the emotions of the reader, while numbers can’t.Narratives are more memorable than numbers.Stories are essential to driving data-driven action for many reasons, two of which being: Instead, data experts can effectively communicate their findings by presenting it in the form of a data story – a story told to communicate the findings and actionable insights found in the data. In fact, the technical summary of their findings may be completely lost in translation. However, to effectively convey their findings to their audience, they have to communicate them in a way that their audience can understand.ĭata experts must assume that their audience won’t share their technical background in data science. To extract insights from complex data sets, data professionals leverage a wealth of technical tools, such as code, statistical analysis, and machine learning algorithms. In most cases, it’s the role of the data scientist to communicate these key insights to stakeholders. Not every role in data science requires storytelling skills, but those that convey key insights from data analysis to decision makers do. Breaking into data science as a storyteller.Today, we’ll discuss why data science needs storytellers to help data make a strong impact: If you’re a data scientist, data storytelling skills will be crucial to help you drive change and communicate business intelligence to your audience. If you’re a storyteller, you already possess one of the most important non-technical skills in data science. Storytelling has been identified as one of the most important non-technical skills for data scientists, and companies have been upskilling their data professionals to teach them the skill of data storytelling. Here is Forbes on the importance of storytelling in data science: “Many of the heavily-recruited individuals with advanced degrees in economics, mathematics, or statistics struggle with communicating their insights to others effectively-essentially, telling the story of their numbers.” The reason why might surprise you: poor storytelling. Problems can often arise at this stage of translation. Then, the data science professionals must convey their findings back to leadership to inform data-driven decision-making. To infer and extract actionable insights from all this data, business leaders and decision makers look to data science professionals, who leverage advanced technologies and methods to analyze and interpret that data. From Fitbits to satellites, we have access to a vast mass of complex data sets, broadly referred to as big data. And today, we’re equipped with more data than ever before. Each day, business decisions big and small are driven by data.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |