Augmented analytics refers to the use of no to very less code involvement, often leveraging tools associated with Artificial intelligence and Machine learning (AI/ML) to automate various analytics. This spans all critical areas of data and analytics, right from data ingestion to insights generation. Augmented analytics capabilities allow business users to discover insights that could have otherwise gone unnoticed in the existing data and explore new data while minimizing human biases and the time to insight.

As the digitalization of life sciences organizations is growing, commercial business users are oversupplied with data, risk, and complexity, which obviously leads to more confusion. Many data-related activities remain highly manual, including preparation, pattern identification, transformations, model development, and insight sharing. Automating these routine tasks is the need of the hour to support the data and deliver business value.

Augmented analytics is transforming how and where business users interact with analytics content as it has become a core component of most analytics, Business intelligence, and data science platforms. With the advent of Customer Data Platforms (CDP) and others in the league, the insights from advanced analytics that were once available only to the data science specialists are now in the hands of sales and marketing teams. These augmented consumers are driving new sources of business value. Life science business users often struggle to identify anomalies, correlations, underlying trends, and change drivers from traditional static dashboards or Excel reports that provide limited insights and interactivity with data. In contrast, augmented analytics tools enable the discovery, visualization, and narration of important data findings via visualizations, conversational interfaces, and technologies.

Life science organizations increasingly need to analyze large, complex, and varied datasets combining data from commercial operations and purchased data from external sources. With an increasing number of variables to explore in healthcare data, it is practically impossible for business users to test their hypotheses through a conventional, interactive analytics workflow and to determine whether their findings are relevant, significant, and actionable.

In the above scenario, there is a need to evaluate and experiment with augmented analytics capabilities of Analytics and Business Intelligence platforms in life sciences. Examine how their growing portfolio of technologies can automate insights generation for your specific use cases. The need is to engage various business users, citizen data scientists, and data scientists in evaluating augmented analytics tools to align desired expectations/user experiences to the capabilities offered. Further, there is also a need to focus on explainability as a key feature to build trust in the autogenerated models.