Resources

Data Science as a Service for Life Sciences

Data science as a service (DSaaS) is a form of outsourcing that involves the delivery of information gleaned from advanced analytics applications run by data scientists at an outside company to corporate clients for their business use.

For firms in biotech, medtech, and life sciences industries, this service can help you decipher large sets of structured and unstructured data from disparate sources. This translates that data into actionable insights and tools that can give you significant advantages over your competitors.

Examples of DSaaS in real cases performed by HexIQ:

ROI analysis for private healthcare practices based on payer mix for specific testing or disease state use cases.

Audience characteristics for patient and caregiver communities

Location analysis based on diagnoses, related procedures, or ongoing care related to any disease state

Anonymized neighborhood analysis based on demographics, psychographics, ethnographics, and social determinants

Transparency in pricing compliance tools for medical systems

What Value does DSaaS Offer?

More and more business applications have analytics and AI functionality built right in, but a data science team can build something more customized than these applications. Embedded tools (like those found in existing applications through Google or Salesforce for example) are convenient but limited to generalized use-cases. Outsourcing DSaaS can offer a customized solution, without burdening existing in-house teams dedicated to product development.

DSaaS as business intelligence platforms

General-purpose business intelligence suites delivered as a service increasingly have augmented analytics. But for the reasons above, companies may need a tailored solution. Companies can outsource individual steps of the analytics pipeline to external providers, he said, even if they have in-house teams for other parts (like storage).

DSaaS as AI development platforms

Vendors offer components and prebuilt AI modules that enterprises can stick together to build their own predictive applications. Using an external data science platform can make sense, even when a company has an internal data science team, because it allows flexibility to scale models up and down as needed and to spin up test environments quickly. It can also reduce capital expenses or licensing costs, and the provider is responsible for keeping the infrastructure maintained and updated.

DSaaS as consulting

Even companies that have internal data science teams may want to bring in a consulting firm to do specialized projects. A common situation is a company needing intelligent text extraction in order to deal with workflows that involve a lot of scanned documents. Ingesting those documents, pulling the data into a format the company can use and doing text analytics is not a set of capabilities most companies are likely to have in-house. This is specifically true for payer data recently released by insurance companies, which comprises many terabytes of data that is not readable by Excel as a default.

Enterprises are also likely to use consultants for one-off projects. Data science as a service can provide a quick hit to solve a quick business problem, and outsourcing can also make sense if a company doesn’t know whether the project will be a success or not. There’s no point in hiring an internal team of data scientists only to find out the data doesn’t give you the result you’re looking for. If a company decides to hire and train an internal team, the consulting firm can help bridge the gap until that process is completed.

Ready to get that data science and AI — as well as the integration into a scalable application? Let’s talk!