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Augmented Analytics: Providing a Narrative and Context via Data Stories
The global augmented analytics market size was $5 billion in 2019 and is growing at a rate of 25 percent annually, according to a report by TMR Research. The industries where augmented analytics is being deployed include financial services, healthcare, government, and telecommunications.
Augmented analytics is the application of AI, machine learning and automation to analytics to help improve data ingestion, preparation and analysis and to make tools easier to use for generation of insights to support decision making.
Bradley Shimmin, distinguished analyst at Omdia, said that “augmented intelligence has emerged as a practice that involves people, process and platform that aims to utilize mostly machine learning mechanisms to do a couple things. One is to augment human decision making and the other is to automate processes that may be too difficult for people to make on their own if they didn’t understand the context or the history, or if it’s just too complex for any human to deal with.”
Rita Sallam, distinguished VP analyst at Gartner, said that “the predefined dashboard with predefined KPIs, predefined relationships, is likely to be displaced.” She predicts that by 2025 the preferred alternative will be the use of data stories. A data story is a presentation that lets a user explore how data has changed over time. It provides visualization, a narrative and also context.
James Richardson, senior analyst at Gartner, said that “a data story starts out like any other story, with a beginning and a middle. However, the end should never be a fixed event, but rather a set of options or questions to trigger an action from the audience. Never forget that the goal of data storytelling is to encourage and energize critical thinking for business decisions.”
Creating ‘data stories’ and using easy augmented analytics offers the business user with average technical skills the opportunity to ask a simple question and get an answer in natural language and to present data in a story format to support recommendations and suggestions and ensure that data analytics are presented in a way that is meaningful to the target audience. When the organization uses these tools to enhance its Citizen Data Scientist initiative and improve data literacy, it must also ensure that cultural changes are made and that the new roles and methods of collaboration, augmented analytics tools and techniques are embraced and not seen as too complex or mind-numbing. Simple data stories are the way to go!