Data is everywhere: whether you choose a new location for your business or decide on the color to use in an ad, data is an invisible advisor that helps make impactful decisions. With quite a number of resources to choose from, data is becoming more accessible, day by day. But as soon as it has been collected, one inevitable question arises: how do I turn this data into insights that can be acted upon? Why data alone is not enough? In the wake of the big data bang, many mistakenly thought raw data to be essential for their business development, which is only partially true. Such a misbelief often leads to frantic investments in data science, which are, however, unlikely to bear any results. Analyzing data and turning it into something comprehensible is only half the battle, while actual insights depend on the way data is treated after being processed. That’s when data storytelling comes in. Whether you are in a leading position, a data engineer, an analyst, or a marketing specialist, making the right data-driven decisions is only possible when your insights are logical and relevant. What is data storytelling? Storytelling in general is one of the most ancient ways in which people communicate and share information. The oldest written story in the world, which is The Epic of Gilgamesh, was written in 2000 BC. Since then, the art of storytelling was developing and evolving, becoming as natural as breathing. Today, sharing information is hardly possible without converting data into a story. Data analysts can take their cue from writers, who never serve plain facts but spice them up with characters’ reasoning, motivations, conflicts and resolutions, and apply this approach to raw data. Visualize with BI tools, which are becoming more accessible with the rise of specialized providers of data visualization consulting, and you can create a complete story. So data storytelling is not only about getting to know what’s going on, but about understanding why this is going on and what can be done about it. That’s when it becomes clear that not all data is equally valuable, and some of it can even be completely useless. That’s why choosing data sources wisely and being able to extract meaning from them is a challenging yet indispensable step in any data-related journey. How to make data meaningful? No one is interested in pointless stories, and there’s no use in collecting all available data either. Instead, the emphasis should be made on the most important, advantageous and sometimes unobvious things. How to achieve that? Think about your target audience When performing data mining and analytics, think of those at whom your data is targeted—investigate their field of expertise and interests, strengths and weaknesses. In this way, you will be able to make decisions equally beneficial for both the company and its audience. Team up with subject-matter experts Collect insights company-wide. A barista working in a coffeeshop chain most likely knows nothing about marketing but can be proficient in the history of coffee culture, species and processing technologies. At the same time, a marketer working there may be a brilliant professional who knows everything about a marketing strategy, but little to nothing about coffee. So, why not let these people exchange valuable information? In this way, the former can improve their communicational and upselling skills, and the latter can learn the peculiarities of this or that coffee and thus generate more relevant advertising campaigns. Make an impact now Tireless search for enormous facts and statistics will decrease the efficiency of your work. To avoid this, focus on correlations, trends, and changes that may be effective for your particular business in its current state. The market today is changing faster than our abilities to digest these changes, so don’t wait for a better moment or more information to act upon. Instead, look for ways to uncover more insights with the data you already have. How to create a story with data? As suggested by Forbes contributor Brent Dykes, a story based on data comprises three vital components: data extraction, data visualization, and the narrative. All together, they can guide you in creating a good story. Data extraction Most modern technologies are driven by data science. Spanning a number of disciplines, it is used to extract sense from data, preparing it to become a basis for decisions. At this stage, though, data is still just a set of letters and numbers that need to be adapted for non-scientists. To become comprehensible for any team member, data needs to be visualized. Data visualization Rows and columns are difficult to perceive and analyze, and thus great amounts of information may remain hidden behind them. The best way to deal with this problem is to transform data into charts and graphs. The narrative The narrative augments our comprehension of information, explaining the data. With comments and context making insights clearer, and visualizations being effective proof points, narrative is a key to convey data to anyone involved in the process of decision making. So, why storytelling? Difficult to ignore or forget, stories unite and inspire us, and while raw data may not be informative enough, storytelling helps to convey the meaning behind it. In particular, it adds clarity and logic to comprehending business processes, making insights easier to be defined and acted upon. Thus, storytelling saves time on explaining data to team members and making decisions. Moreover, it influences the way you treat your business on the whole, letting you learn more valuable information, prioritize your actions within the company, and focus on the most important things.

The Art of Data Storytelling: How to Make Your Data Impactful

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Darya Efimova

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