To report on a mountain of data so your clients can quickly glean valuable insights from their marketing campaigns, agencies rely more on data storytelling than ever before. Using data tools and technology to collect, store, analyze, and report data is essential to growing your business-savvy clients’ brands. You don't have to be a data scientist to tell a good data story leveraging data to craft a narrative helps your agency effectively communicate insights based on the data collected by your automated reporting tool. It requires balance among three core components – data analysis, visualization, and narrative.Data storytelling is the art of turning numbers into narratives, unlocking a world of insights and possibilities. However, data storytelling requires a structured approach for organizing and communicating the insights from data. Often times, data storytelling is equated to building compelling visualizations. If an insight isn’t properly understood, is not compelling or actionable, no one will act on it. Unless businesses improve the communication and collaboration frameworks around these insights, they will experience a poorer insight-to-value conversion rate. Correspondingly, we will witness an unprecedented growth in the number and significance of insights being generated than ever before. With more self-service business intelligence tools, such as Tableau and Microsoft Power BI, the pool of business users generating insights will grow beyond just data analysts and data scientists. In other words, the need for data storytellers is only going to increase in the future.
Organizations need specialists, or “translators,” who can analyze, distill, and clearly communicate information of the greatest potential value. From my experience, many data scientists with advanced degrees in economics, mathematics, or statistics find effectively communicating their analyses and insights with other business stakeholders and interpreting the significance of their sophisticated analyses within the business context particularly challenging. data engineering, rather than on the skills that convert analysis to insights and actions. However, much of the current hiring emphasis is centered on the data infrastructure and preparation a.k.a. Recently, LinkedIn reported that data analysis is one of the hottest job categories and it is the only category that consistently ranked in the top 4 across all of the countries they analyzed. The ability to take data-to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it-that’s going to be a hugely important skill in the next decades.Ĭompanies are desperately searching and recruiting data science talents. Your data may hold tremendous potential, but not an ounce of value can be created unless the data is analyzed, actionable insights extracted and translated into concrete business process action items.ĭuring a 2009 interview, Google’s Chief Economist Dr. The next elusive step is to extract value from your investments in data projects. The chances are, your business has already launched Big Data projects and has started collecting all kinds of data.
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