Sean Jacobs, Director of Research and Development IT and Architecture Services at Eisai, recently joined Immuta and Databricks at Amazon Web Services’ Cloud Data Lake Dev Day. This blog recaps his presentation about building a data warehouse, analytics, and governance strategy from scratch.
Eisai is a research-focused pharmaceutical company with a range of specialties including neurology and oncology. The organization is committed to providing “human healthcare,” the prioritization of direct, personal connections with patients and their family.
To improve patient outcomes, scale cost efficiencies, and fuel drug development and innovation, Eisai needs to leverage predictive analytics and machine learning on sensitive health data.
Eisai’s ability to deliver the best service possible to its customers through data was inhibited by three primary issues: an outdated data storage solution, inaccessible and siloed data, and enterprise-wide reliance on sensitive data use.
The company’s most pressing challenge was siloed data use among business groups and data owners. Business groups––like Eisai’s oncology and neurology teams––use distinct types of data, ranging from patient health statistics to medical and X-ray imagery. Storing multiple types of sensitive data means the Eisai team must comply with a long list of privacy regulations, and therefore, had to create different data governance solutions for siloed data sets. This complex compliance strategy was incredibly manual, time intensive, and risky, so consolidating and automating the governance strategies was a huge priority.
The compliance team’s lack of visibility and authority across the organization also introduced potential violations of compliance and privacy. For example, personal data containing PII and PHI were frequently downloaded to individual workstations, data was regularly shared over email, and third-party tools were installed without proper vetting for security compliance. These risky practices were not a product of the data team’s ignorance but rather a result of a lack of methodology. Regardless of who was accountable, these data practices needed to be fixed.
To start the process of building a new data strategy, the data team worked backwards. Considering general company goals and collaborating with data scientists and analysts across departments, they discovered that Eisai needed a cloud-based, big data repository with a global catalog for medical images and textual data, as well as a unified data science and a BI/analytics platform. The platform needed to be capable of:
- Atomic storage
- Producing a detailed data audit trail
- Querying data at a point in time
- Securing share data across multiple business units
- Analyzing, reporting, and dashboarding data use
- Supporting compliance with a large number of privacy and data regulations
The Eisai data team began its data transformation with governance and security in mind. From the start, they knew that they wanted to use a data lake, a single repository for structured and unstructured data. Despite offering auditing, flexibility, and analytic capabilities, when data lakes are mismanaged they can become inaccessible and diminish in value. Immuta’s automated, fine-grained data access control, audit, and data cataloging capabilities empowered the Eisai team to make data access governance a priority and helped them unlock the full capabilities of Amazon Web Services (AWS) and Databricks.
Eisai originally chose AWS as a cost effective, cloud-based storage solution with a wide variety of advanced compute and analytics capabilities. The Eisai team was excited by AWS’ capabilities but ultimately needed to add a more diverse data analytics solution.
To extend support for SQL, R, and Scala––and gain access to advanced auditing capabilities––Eisai adopted Databricks. Databricks offers Delta Lake as an open format storage solution that increases data lake reliability, security, and performance. Delta Lake offers widespread support for analytics languages, as well as the ability to dashboard and audit within the product or interface with other leading reporting tools. Databricks’ flexibility and heightened reporting capabilities enabled Eisai to consolidate many of its central data analytics activities in a single, unified platform.
After setting up storage and analytics platforms, the Eisai team turned its attention to finding a method for securing these new data storage and analytics platforms. Immuta ultimately stood out for its superior features and ease of integration. With Immuta, the Eisai team was able to run fine-grained access controls, dynamic data masking, and automated compliance policies across platforms, without oversight or manual attention. They also discovered that Immuta’s auditing and cataloging capabilities were more powerful than their initially desired processes.
To fill the need for a modern and secure system to control and monitor data use, the Eisai team uses Immuta, AWS, and Databricks as its 100% cloud-based, unified data warehouse and analytics solution with the following features:
- Delta Lake as a big data repository
- Shareable data catalog for structured and unstructured data
- Unified data science platform that analyzes data with deep learning models and enables inference
- Business intelligence and analytics platform with the ability to analyze, report, and dashboard data after ETL and/or data science experimentation
- Row-, column-, and cell-level detailed governance audit trail of all activities across Immuta, Databricks, and AWS
- Data sharing across business groups, eliminating silos across the company as a whole
- Automated compliance and regulatory controls
- A purpose-built toolset
With faster time to data access, more efficient governance and compliance efforts, reduced cost on data storage and management, and increased collaboration across teams, Eisai’s data warehouse, governance, and analytics transformation empowers and enables improved patient treatment and outcomes.
Immuta works with your desired data storage solution and analytics platform to strengthen privacy, compliance, and access management. Request a demo to see automated data access governance in action or browse more data governance success stories on our customer page.