Augmented Analytics in Data Storytelling is Providing Opportunities

Augmented analytics in data storytelling is helping deliver data clearly and easily for employees

Data storytelling is the surprise technique that boosted the business performance of companies that engaged in early technology adoption. On a general scale, today, business growth solely depends on numbers that are driven by data and analytics. But most of them end up being in a dashboard or a spreadsheet, which is difficult for laymen to grab. Fortunately, data storytelling can help. As an advanced move, augmented analytics in data storytelling is helping deliver data clearly and easily to employees without the help of tech professionals.

Data storytelling takes data and automatically turns it into plain stories without knots. It paves way for anybody including non-tech employees to understand, read, and share insights and data stories. On the other hand, we have a disruptive trend called augmented analytics. Augmented analytics is overtaking the almighty 'business intelligence' in the digital world. It is designed to facilitate more growth and help generate more revenue. By promoting augmented analytics in data storytelling, business organizations are experiencing a new array of opportunities. The growth is further being accelerated by the existing skill gap in business intelligence. But we can't deny the fact that the skill gap is also costing a lot on data storytelling.

To begin with, there is no assurance that data analysts explore and get their hands on all data and use it for storytelling. Besides, developing data stories mostly rely on manual activities, so humans are bound to make mistakes and the results could sometimes even be biased. But automating the tasks could change the way data storytelling is done. Using augmented analytics in data storytelling could drive to a wider engagement with data assets, without falling victim to manual mistakes. According to Gartner, data stories will be the most widespread way of consuming analytics, and 75% of stories will be automatically generated using augmented analytics techniques by 2025.

How Augmented Analytics is Powering Data Storytelling?

Traditionally, data storytelling was very lengthy and involved many instances when humans have to intervene or carry out the whole process. Data professionals have to retrieve and review information from data terminals manually, make sure no data is left out, and drive data stories from it. But with augmented analytics, the lengthy routine of gathering the data and analyzing it to drive business decisions is drastically cut down. By using augmented analytics in data storytelling, organizations can get actionable insights in minutes.

Uncovering actionable insight involves critical aspects like data and analytics and somebody to unravel the potential of decision-making with data-driven insights. A routine data analysis is different from acquiring data from the bottom to deliver data stories. In order to streamline data storytelling, many companies have embraced business analytics to collect, aggregate, translate and present business information. But for many organizations, visualization alone cannot tell the data's full story, A complete data story would require the combination of interactive data visualization with spoken narratives. This is possible only with the help of augmented analytics.

Uses Complex Narrative Techniques to Deliver Insights

Data stories serve more than just the simple purpose of delivering insights and trends. They combine data visualization imagery, time-based narrative, and context to convey rational and emotional drivers in the outlook. While human analysts see data gathering as a time-consuming task, automation can easily do that in less time. They monitor a huge variety of data and find anomalies in the patterns. By taking note of structured and unstructured data, augmented analytics can deliver the information to decision-makers in the form of rich narratives. They grab the concept and understand the decision-maker's perspective before conveying data stories. This approach has the potential to analyze far more data than an analyst would possibly do and besides, it is also unbiased.