Mastering Data Overload: How Life Sciences Companies Can Unlock Business Value with the right Data Management Approach
Life sciences companies are evolving into data-driven, patient-centric tech companies. Data is becoming the lifeblood of these organizations, with volumes continuously growing and unlocking opportunities—from driving innovation in drug development to optimizing processes and enhancing patient care. In this article, we’ll explore how Life Sciences companies can future-proof their data management using technologies like edge computing, data mesh, and data fabric, maximizing the value of their data.
The Global Data Surge: From 33ZB to 175ZB
The world is generating more data each year. In 2018, the total amount of data created, processed, and used globally was 33 zettabytes—that’s 33 trillion gigabytes. By 2020, it had grown to 59ZB, and it’s projected to reach 175ZB by 2025.
The Life Sciences industry stands out with even more diverse sources of data compared to other sectors. Data is stored in three key locations. First, at the endpoints—these include IoT devices, personal computers, smartphones, and health-monitoring devices like smartwatches, fitness trackers, blood pressure monitors, and pacemakers.
Second is the Edge, a crucial area for data processing. It handles data closer to the source, optimizing future data management from IoT devices and providing real-time insights to users.
Finally, the majority of data is stored on data servers and within cloud data centers, where large volumes of information are processed and secured.
Edge Computing: Bringing Data Management Closer to Patients and Users
Edge computing is transforming how data is handled. It focuses on capturing and processing data as close to the source—or end user—as possible. This includes IoT devices like heart monitors and smartwatches. For example, a smartwatch processes data within the device, showing real-time results on the display while uploading aggregated data to the cloud.
In manufacturing, the Industrial Internet of Things (IIoT) is another powerful application of Edge computing. It plays a key role in digitizing factories, where IIoT sensors enhance production processes. Data is processed locally, then further refined in data centers. The term “Edge” refers to this local processing, which reduces latency, cuts data transfer costs, and enables real-time decision-making with instant feedback.
For Life Sciences companies, Edge technology offers significant value. Patient data, collected from diagnostic devices and telemedicine applications, can be processed in real time. For instance, a device monitoring a patient’s vitals can send alerts in emergencies, with summarized data uploaded to medical records. This allows healthcare providers to make faster, more informed treatment decisions.
Data Mesh: Decentralizing Data for Faster Innovation
Introduced by Zhamak Dehghani, Director of Emerging Technologies at ThoughtWorks, Data Mesh is a revolutionary approach to data architecture. Instead of relying on a centralized structure, Data Mesh promotes a decentralized model that allows data to be connected from various locations and sources, all while ensuring proper governance. This decentralization not only solves issues with data availability but also makes scaling easier when needed.
While traditional data warehouses, the first generation, focused on storing centralized structured data, and data lakes, the second generation, handled both structured and unstructured data, Data Mesh represents the third generation. It enables decentralized data storage and governance, allowing data to remain in its original form with domain-specific teams taking ownership.
Data Mesh architecture is built on four key principles:
- Domain-oriented decentralized data ownership and architecture.
- Data treated as a product.
- A self-serve data infrastructure platform.
- Federated computational governance.
A powerful example of Data Mesh in action is at Memorial Sloan Kettering (MSK) Cancer Center, where this architecture has significantly accelerated research efforts. The time required for data projects has been reduced from months to days, helping scientists speed up their search for cures.
Data Fabric: Weaving Together Seamless Data Integration for Life Sciences
IBM defines Data Fabric as an architecture that intelligently automates the connection of multiple environments and data pipelines. It can easily scale to accommodate growing data needs, streamlining analytics and supporting faster, more informed decision-making.
Life Sciences organizations can greatly benefit from adopting Data Fabric to gather patient and provider information from various sources, such as smart devices, sales and marketing data, and real-world evidence. By connecting clinical trial data with real-world evidence, Data Fabric helps meet regulatory requirements and enhances customer confidence.
Pharmaceutical companies also gain from improved integration of patient data with sales data, enabling more targeted sales efforts and more effective marketing strategies.
Navigating the Data Surge: Embracing Innovative Strategies for Effective Data Management in the AI Era
The rapid advancements in cloud technology, the Internet of Things (IoT), artificial intelligence, and edge computing are driving an exponential increase in available data. As a result, data processing and management are becoming increasingly complex. In this evolving landscape, new approaches are emerging to address the challenges of governance, security, and process optimization, ultimately enhancing decision-making capabilities.
From Edge Computing to Data Fabric, these strategies help organizations harness their data effectively. Selecting the right data management strategy depends on specific requirements and circumstances, often resulting in a combination of these methods.
The Data Fabric approach outlined earlier provides a roadmap for developing a data-driven organization augmented by AI. This enables better distribution and consumption of data within the organization, meeting the needs of data consumers with greater efficiency and impact.
As data continues to expand, presenting both challenges and opportunities, SAP S/4HANA serves as a core element, seamlessly integrating with edge computing and acting as a crucial source system within the Data Mesh and Data Fabric landscape.
written by
Dawid Solowianiuk
Advisor