Increase the Value of Generative AI-Powered Analytics with Domain Expertise

"Very manual." It's how Gartner® describes the current state of enterprises' data analytics processes. The Gartner Market Guide™ for Augmented Analytics points out that activities, from data preparation, pattern identification, and model development to insight sharing, are slow and labor-intensive. As a result, businesses struggle to facilitate data-driven decision-making across their organizations and see improved business outcomes due to their investment in data analytics solutions. However, a generative AI enterprise tool with augmented analytics capabilities solves those pain points. 

The Benefits of a Generative AI Enterprise Tool with Augmented Analytics Capabilities 

Gartner's market guide explains that augmented analytics platforms enhanced with generative AI allow business users in various roles the opportunity to discover insights that would go unnoticed with current manual processes. 

These platforms use advanced artificial intelligence and linguistic techniques to allow users to interact with them conversationally. They also enable analytics consumers to explore a wider range of data sources than they can with traditional business intelligence dashboard solutions while accelerating time to insights. These platforms also dynamically generate data stories, automatically generating visualizations and narratives to make patterns, relationships, and insights easy to consume. The ease of use fuels higher user adoption and data-based decisions as a part of daily workflows. It also results in users requesting less assistance from the company's IT and data teams, lowering the total data analysis cost of ownership and allowing those teams to focus on higher-value tasks for the organization. 

Companies Need More than Generative AI Alone

While generative AI brings new efficiencies to data analytics and insight delivery, all platforms don't provide the same value to an organization, mainly if they weren't developed and trained for the domain. Gartner states, "In the augmented analytics market, simply being able to generate an automated insight for a user is already no longer enough to win over customers. The contextualization and relevance of such insights within a domain-specific workflow have become the battleground for augmented analytics capabilities."

Tools that provide the most value will deliver insights relevant to the user's role, brand focus, geographic region, and other metrics. They will also learn as the users interact more and provide proactive alerts based on anomalies, changing trends, and consumer preferences and behaviors.  

In life sciences, domain-specificity is incredibly crucial. Life sciences professionals have a language all their own, and the platform must understand the intent and the literal meaning of queries. A generative AI enterprise tool pre trained for life sciences will be able to deliver accurate contextual insights out of the box, which will help build trust in the platform and lead to higher user adoption. Pretraining with domain-specific data also shortens implementation from months to weeks. 

Dramatic Changes on the Horizon for Analytics 

Gartner research indicates that analytics platforms and processes will transition quickly in the next three years: 

• By 2025, augmented analytics platforms will drive analytics and business intelligence (ABI) solution adoption to more than 50% for the first time, and context-specific models will replace 60% of current solutions. 
• Also, by 2025, augmented analytics platforms will generate 75% of data stories. 
• By 2026, 30% of companies will implement metadata to enable automation, accelerated time to insights, and augmented decision-making. 

Gartner predicts the augmented analytics market, valued at $6-$10 billion, will increase 10x in the next decade. 

Augmented Analytics Platform Selection 

Gartner's Market Guide offers guidance for selecting an augmented analytics platform enhanced with generative AI and maximizing the value it provides: 

• Evaluate vendor capabilities as they mature. Assess their current capabilities and roadmaps concerning the setup, data preparation required, the data types and algorithms they support, integration with the systems and business applications currently in use, explainability of the models, and insight accuracy.
• Provide users with training and peer learning to increase adoption and data literacy across lines of business. 
• Stay open to using multiple tools. The optimal stack for a life sciences company may be a combination of traditional data analytics platforms and augmented analytics tools for different types of analyses. Augmented analytics tools may even provide an easier path to accessing insights from the models that the data team develops. 
• Conduct a pilot to confirm the platform's viability for the organization's use case and the degree to which it addresses business problems. 

Learn More About a Generative AI Enterprise Tool for Life Sciences

Gartner's Market Guide for Augmented Analytics includes a list of Representative Vendors that illustrates the breadth of choices in the market. These solutions can address a wide range of use cases in various industries – but there's only one specifically developed and pre-trained for life sciences organizations: WhizAI.

WhizAI's generative AI enterprise tool is designed to easily access insights for any life sciences business user, from commercial teams to the C-suite. It analyzes petabytes of data from all data sources necessary to deliver the deepest contextual insights and allow users to drill down to understand patterns or customer behaviors, even at the physician level. WhizAI also offers anomaly detection, proactive alerts, and root cause analysis that users can configure in a zero-code environment. 

Augmented analytics with generative AI capabilities is the direction the industry is heading. Contact WhizAI to get on the path to faster, contextual insights, data-based decision-making, and greater competitiveness in life sciences.