How Generative AI-Powered Analytics Lowers Total Cost of Ownership (TCO)

Successful life sciences companies have always been cost-conscious. However, macroeconomic and regulatory pressures make taking a hard look at operating costs more critical now than ever. Granted, some costs are beyond life sciences companies' control. However, others are manageable. It's time to move data analytics from the first category to the latter with self-service analytics tools. 

The Historically High Data Analytics TCO

Traditional data analytics processes have come with a big price tag. Factors contributing to the total cost of ownership (TCO) fall into three general categories:

• People: Life sciences companies need a skilled data team to build business intelligence (BI) dashboards capable of providing the insights that business leadership and various departments need. The data team must be knowledgeable in analytics and life sciences nuances, which translates to higher staffing costs. 
• Processes: Traditional data analytics take time, and they're often multilayered and inefficient. A business user must communicate a request for insights to an IT team that, in turn, explains it to the data team. Additionally, business users spend time writing emails to clarify requests and following up to see where the analysis stands. And unfortunately, the process from request to insights can take weeks or months, and when the team making the request receives the information, it could be too late to be of any value.
• Infrastructure: IDC estimates that healthcare and life science data grew by 270 GB for each of the more than 7.8 billion people in the world in 2020—and data volume growth is accelerating. Analyzing skyrocketing data volumes requires substantial investments in infrastructure and cloud services. Additionally, traditional BI dashboards have a limit on the number of data sources and data volumes they can use. As a result, life sciences companies must resort to investing in additional dashboards when they need new or broader insights. 

‍How Self-Service Analytics Tools Lower Data Analytics TCO

Life sciences organizations basing data analytics on BI dashboards won't see much opportunity to lower data analytics costs. Therefore, reducing data analytics TCO necessitates a new approach. Fortunately, artificial intelligence (AI) provides the foundation for a viable alternative to traditional analytics processes. 

Self-service analytics tools leveraging large language models (LLMs) and generative AI give business users direct access to analytics just by asking a question. A model pre-trained with life sciences data will understand the industry language and the intent behind the user's question; for example, the platform will know that a sales rep asking about "sales" refers to the number of prescriptions. 

Note that when the analytics platform understands users, it removes layers from analytics processes. Users receive the answers they need immediately rather than waiting for one department to pass the request to another and add the project to a priority list. Additionally, deploying self-service analytics tools changes how life sciences organizations staff their data teams. They don't need to find data scientists with life sciences knowledge. The platform itself understands the domain, so companies have more options when expanding their data teams.

Self-Service Analytics Tools Increase Speed and Efficiency

Self-service analytics tools also have features that streamline processes. No-code dashboards allow each user to configure screens to display and monitor the most critical insights. Additionally, the solution that makes it easy to share insights or visualizations with colleagues and collaborators will provide additional value and encourage adoption. Furthermore, an analytics platform that integrates with the company's business applications allows users to get answers with just a few clicks—and replaces a cascade of dashboards on a user's desktop that adds complexity to accessing data insights. 

The speed and simplicity of leading self-service analytics tools have several cost benefits for life sciences companies. When users access data analytics insights on demand, there are no delays and wasted effort with analyses that users receive too late to be helpful. Moreover, data-driven decision-making becomes routine. Users ask questions and receive answers in a split second, increasing productivity and efficiency. Additionally, dependence on the IT team decreases—WhizAI data shows by up to 40%--further lowering TCO. 

Finally, a self-service analytics tool designed with modern architecture lowers infrastructure costs by up to 50% compared to traditional data analytics processes. 

How Much Could Self-Service Analytics Tools Save?

Although each life sciences company has different data analytics strategies, staffing, time, infrastructure, and complex processes add up to significant costs. For global life sciences organizations, it can total millions of dollars. By decreasing dependence on the IT and data teams, lowering infrastructure costs, and eliminating wasted time and effort, self-service analytics tools can reduce TCO by 50%. At a time when controlling costs is essential to competing in an ever-increasing regulatory climate, economic uncertainty, and heightened demands, those savings could determine market leaders and companies in decline. 

To learn more about self-service analytics tools helping life sciences and healthcare companies lower data analytics TCO and enhance overall business operations, contact WhizAI.