Next Practices For The Intelligent Enterprise Life Sciences Company

Michelle Schooff

Data analytics is a winner in the life sciences industry, providing tremendous value. In 2018, several factors are expanding its scope to touch every corner of life sciences companies. Data volumes are growing while storage costs have declined. CPU power has increased. The cloud, mobile, automation, machine learning, AI, blockchain, and the Internet of Things are adding new and varied data sources. And data analytics platforms have rapidly evolved to handle diverse data types and massive data volumes with powerful and user-friendly tools.

Until recently, the opportunity to effectively integrate data from across and outside of an enterprise life sciences company for analytics on a grand scale was easier said than done. Now it’s a reality.

From our global experience working with the leading, most innovative life sciences companies, here are three SAP “next practices”— capabilities and outcomes to help your company utilize data and analytics on a grand scale.

Integrate diverse data sources. Data is scattered. It’s in multiple applications, files, data warehouses, data lakes, and public and private clouds. Each silo walls off the data with proprietary rules and complexity. You need visibility into that data. Without it, you have a disjointed picture of the business. With it, you can improve the speed and quality of learning between the patient and the drug discovery process. You can tap into lab instrumentation data from multiple sources for R&D and fine tune the delivery and pricing of drugs – and much more.

Next practice #1: Integrate your data by combining data sets — including big data, process data, product data, analytical data, etc. — as needed, into a single data universe for much greater visibility.

Make data more useful. Your data comes to you structured, semi-structured, and unstructured. It may be spatial, chart, numeric, geographic, time-series, relational, JavaScript object Notation (JSON), etc. Integrating all of these different types of data is extremely complex. But without it your company is at a competitive disadvantage, squandering available resources.

Next practice #2: Integrate your data sources, using orchestration and governance solutions. Go from raw feed to intelligence with real-time analysis of vast data sets. How? With solutions to understand, integrate, cleanse, manage, associate, and archive data to optimize business processes and analytical insights.

Simplify your data landscape. Centralized. Easy-to-use. Automated. That’s what you want from your data analytics platform. And those features have been a challenge because of all the different databases, apps, and clouds in your IT and business environment. But now a centralized data management solution is available that manages all facets of an enterprise life sciences company’s data universe. Represented visually, the architecture is easy to share and understand. Stakeholders assigned to an architecture team within your company can collaborate through a user-friendly Web application in the planning, design, and governance of the architecture.

Next practice #3: Create and maintain a complete landscape architecture that is easy to share and understand. Open up this landscape to an array of company employees and managers to jointly manage your data environment as an agile, strategic tool.

A growing number of data analytics use cases for life sciences companies

Data analytics is being recognized as a vital tool for life sciences companies that need to innovate ever faster, create new markets and products quickly, attract and retain customers, and enhance the quality of care for medical patients and practitioners. The need for speed has grown — along with the diverse types and quantity of data. Becoming a truly intelligent enterprise life sciences company requires a reliable, easy-to-use platform to capture, ingest, process, orchestrate, compute, and consume data at tremendous scale.

SAP customers in the life sciences industry that are intelligent enterprises are using data analytics fed by an increasing array of data sets for use cases that include:

  • Connected healthcare, with remote monitoring and communication between doctors and patients to provide better treatment outcomes
  • Internet of Things strategy, including multiple use cases to improve the customer experience, manufacturing efficiency, and productivity
  • Clinical data warehouse, a validated reporting platform across diverse data sources
  • Supply chain temperature monitoring for environmentally sensitive shipments
  • Quality complaints and feedback analysis, to explore the relationships between quality outcomes and internal factors to support improvements

These are just some of the many quickly evolving, creative ways that larger and diverse data sets are being put to work to guide life sciences companies today. Some use cases are relevant to every type of organization within the industry. Others are more suited to different types of businesses, geographies, markets, and other unique characteristics.

For more on how life sciences companies around the world are transforming into intelligent enterprises, read the new SAP white paper “The Data-Driven Life Sciences CompanyData Management for the Intelligent Enterprise.”

And please listen to the replay of our “Pathways to the Intelligent Enterprise” Webinar, featuring Phil Carter, chief analyst at IDC, and SAP’s Dan Kearnan and Ginger Gatling.

Michelle Schooff

About Michelle Schooff

Michelle Schooff is a global marketing director in the life sciences and wholesale distribution industries for SAP. She is responsible for the marketing strategy, messaging and positioning for SAP solutions in the global marketplace. With over 20 years experience in technology and marketing, Michelle builds strategic marketing plans that drive growth, innovation and revenue.