What does augmented analytics mean for business?
Four experts explain what augmented analytics means for business — it’s about getting more value out of the data available
As Gartner describes, augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyse data in analytics and BI platforms.
Applying augmented analytics in business means “they can get more value of their data, if they train their ML well,” commented Nico Fischbach, CTO at Forcepoint.
Elif Tutuk, AVP of Innovation and Design at Qlik, explained that analytics technology has moved on significantly since the first-generation passive systems, which restricted users to static data reports and quickly became obsolete.
He said: “The most effective augmented analytics combines the best aspects of machine intelligence and human creativity and experience to help users get faster responses and improved productivity, along with targeted insights to help make better decisions.”
Tutuk uses the example of a sales leader, who can use search-based analytics to evaluate performance for individual sales reps.
“The leader uses natural language processing to type a question into their analytics platform. Data science and AI immediately go to work analysing both structured and unstructured data against the search terms to display the most relevant results, including visual representations. The user can then explore interpretations of that data that were previously unavailable to help them to make the best business decisions.”
Augmented analytics makes data analytics accessible to everyone and “businesses who invest in [these] tools, in easy-to-consume visual representation and data narratives, are the ones who will propel data literacy at all levels and boost business success,” added Tutuk.
Alexis Fournier, director of AI Strategy at Dataiku, agrees that for modern business, the introduction of augmented analytics allows for greatly accelerated, and automated, AI modelling.
However, he suggests that with augmented analytics comes the changing role and definition of the data scientist.
“Implementing augmented analytics in a business will not make its data scientists obsolete, but it will democratise data, making it more accessible with the wider workforce.
