designing a fast and scalable data platform in the cloud – a modern approach Prakriteswar Santikary, PhD Vice President and Global Chief Data Officer

ABOUT ERT Founded in 1977; privately held Supporting Pharmacos, Biotechs & CROs around the world Operations in 12 countries 2500+ employees MINIMIZING RISK & UNCERTAINTY, SO YOU CAN MOVE AHEAD QUICKLY WITH CONFIDENCE │ © Copyright ERT 2018

GLOBAL REACH & EXPERIENCE >50% new drug approvals trials sites spanning 114 languages & 106 countries of all FDA approvals over the past 4 years 550+ 13K+ 230K+ patients 3M+ SUPPORT FOR… Boston, MA Boulder, CO Bridgewater, NJ Cleveland, OH Philadelphia, PA Raleigh, NC Rochester, NY Scotts Valley, CA St. Louis, MO Brussels, BE Estenfeld, GER Geneva Nottingham, UK Peterborough, UK Tokyo, JP │ © Copyright ERT 2018

Clinical trial Industry Challenges 01 Data Collection 02 Data Processing and Access real time data ingestion at scale Real time data processing Data siloes – lack of governance Difficult to identify issues and resolve them in real time Lack of visibility into actions being taken to address issues Multiple data sources Multiple devices Multiple data types Multiple vendors Patient recruitment Site qualification Complexity In clinical trials 04 Oversight and Monitoring 03 Data Quality Patient compliance Site compliance Query resolution Visit compliance Regulatory compliance Oversight of trials and sites Oversight of vendors Oversight of systems Monitoring of trial risks Data security and privacy │ © Copyright ERT 2018

SYSTEM DISPARITY is GROWing WITH SPECIALIZATION MORE SYSTEMS ARE INTRODUCED IN CLINICAL RESEARCH EACH YEAR EDC eCOA CTMS PATIENT IDENTIFICATION IVR mHEALTH PATIENT ENGAGEMENT Established eClinical Systems mHealth Images HOME MONITORING EMR Labs And if we peak into the immediate future, we can see that this is a trend that’s going to continue and become even more challenging as we incorporate new technologies like wearables and mHealth sensors. From Patient Apps, to Home Monitoring and integration of EMRs, the data we use to execute clinical research is becoming even more specialized. While this is all exciting stuff, it presents a challenge that organizations need to begin planning for now. eCOA is expanding from validated clinical assessments collected at the site, medication reminders, patient diaries, endpoint data collection, etc. mHealth (incorporating eCOA) is exploding with wearables from Actigraphy, glucose monitoring, ECG patches, etc. Patient Engagement leveraging social networks is increasing along with smarter, targeted advertising EMRs are opening up accessibility through the adoption of FHIR (fire) Home Monitoring: Think Alexa, Google Assistant, Siri Patient Identification: As patient access to data becomes a best practices, identification of patients and security will continue to evolve │ © Copyright ERT 2018

Trials GETTING more complex Increasing interest in building protocols based on real-time outcomes; mitigating risk of protocol change mid trial Increase in Complex Trials Continued move to a single Cloud-based platform as trial methodology of choice powered by data. Allows for agility and speed to market.  Key Insight Implications Technology advancements driving highly individualized patient therapeutics administration. Advancement of Precision Medicine Ability to do smaller targeted trials improving outcomes based on lifestyle, environment and genetic makeup. Shift in more active patient engagement - improving access to trials via mHealth, virtual trials and focusing on outcomes. Patient Centricity Further investment in clinical trials that meet patient needs – less burdensome, increased diversity and better outcomes. An explosion of data from wearables, genomics, social, imaging, etc. – driving advances in data interaction and visualization. More Big Data & AI Modeling Need to standardize data sets - mine previous data sets, and better ingest, analyze and manager larger, more complex data.  The promise of Blockchain to help concerns over privacy, data sharing and reproducibility. Blockchain Increased investment in blockchain technology to improve data security – competitive differentiation. │ © Copyright ERT 2018

Exponential Data growth in Life Sciences VOLUME Implications Organizations must implement scalable infrastructures to deal with increased data volumes More types along with more endpoints VELOCITY Leading customer experiences require contemporary insights that can be acted on Digitization has increased the speed of information VARIETY Flexible data models are required to integrate customer information Clinical, compliance, medical, RWE/commercial and actimetry VERACITY Need to enable multiple user groups with consumable insights Ability to gain insight from aligned data sets │ © Copyright ERT 2018

Waves of information that aren’t actionable Clinical trial teams are drowning in data High administrative burden to extract and re-enter data Into other systems Difficult to filter out noise to identify real issues that require attention More effort is spent confirming the accuracy of data than interpreting it for decision-making Cost overrun and delay to time-to-market Our customers are crying out for help – “Please simplify the complex!” │ © Copyright ERT 2018

MODERN platform architecture     Before we dive into the specifics, let’s set the stage and take a look at some of the drivers of why we need a paradigm shift in our industry. Many of us are familiar with the different factoids floating around the industry that it costs over $2B and take 8 years to develop a compound and bring it to market. But our focus today will be on the the underlying trends that contribute to this dynamic, and how our ability to design efficient studies, manage timelines, and collaborate effectively can impact your organizations succes, and ultimately contribute to managing trials more effectively.

Put high quality data in the hands of people who need it – a business imperative 01 Data Governance 02 Modern Data Platform Disparate data sources Multiple integration paths Dealing with unstructured data Dealing with binary data Dealing with streaming data Real time ingestion at scale Self service data access Who owns what data How data can be used Find data, understand it Find data that matters - fast Trust the data quality Make big data meaningful Framework for collaboration Our Approach 04 Master Data Management 03 Data Quality/Regulatory HIPAA GDPR Mitigate risks Avoid steep penalties Transparent data access Single version of truth Lifecycle of master entities Technology, Process Stewardship, clarity Data Lineage │ © Copyright ERT 2018

Transforming data into strategic business assets using modern, secure, cloud-based data platform Scale Cloud based Modern tech stack governed Real time reporting analytics and data science Data platform architecture at a glance Lambda architecture (batch and speed layers) Modular microservices architecture Serverless Computing Master data management/data governance Data ingestion at scale Adapters and connectors for third party data integration Modern data visualization architecture Customer benefits at a glance Enables real time decision making Enables self-service reporting and analytics Enables data monetization; Reduces complexity and improves quality Enables Data-driven decision making Enable Speed to market of life-saving drugs Centralizes risk and performance management of trials Enables cost savings & operational efficiencies Enables AI and ML capabilities – anticipatory oversight │ © Copyright ERT 2018

ERT’s APPROACH - MODERN DATA ARCHITECTURE AND SERVICES Data Availability Data Quality Data Consistency Data Security Data Auditability Reporting, Analytics and Data Science Services Data Lineage Data Standards Data Policies and Procedures Business Metadata Technical Metadata Master Data Management, Metadata Management and Data Governance Reporting Self-service BI Open Data API Data Science Relational Dimensional In-memory Polyglot ERT’s APPROACH - MODERN DATA ARCHITECTURE AND SERVICES Data Architecture and Data Technology Structured Data Unstructured Data Semi-structured Data Binary Data Data Integration, Data Services and Data Adapters Internal External Third party Future M&A Data Sources │ © Copyright ERT 2018

Microservices Architecture Characteristics Benefits Challenges Decentralized Independent Do one thing well Polyglot API-first Design You build it; You own it Agility Innovation Quality Scalability Availability Distributed systems Monolith->Microservices transition not easy Organizational issues (DevOps) Skillsets Build scalable platform and applications Here are a few key learnings we hope to provide with this presentation that may inspire these organizations to embrace this change as an opportunity to innovate. Paradigm Shift: Companies embarking on new innovation initiatives to determine feasibility of mHealth solutions for clinical research Audience Questions: How many of you currently employ mHealth technologies in your clinical trials? How may of you currently utilize Actigraphy and/or Continous Monitoring devices in your clinical trials? How many of you work in organizations that are now tasked with POCs or pilots using mHealth devices? Data Optimization: Enhancing clinical research with mHealth data requires new techniques against non-conventional sources. mHealth data alone does not enhance clinical research. Practical Applications: Applied use of mHealth in select therapeutic areas in combination with other clinical source data and/or ways where mHealth data may be combined for more visual insights | Copyright ERT 2018

Serverless Architecture

Serverless Architecture Characteristics Benefits Challenges Function-as-a-service Compute as a service (100ms interval) Stateless Ephemeral Event-triggered Vendor lock-in Vendor control Monitoring and debugging Startup latency Code without provisioning No server HW to maintain No server SW to maintain Increase productivity Scale your code with HA Pay-per-use CPU cycle Zero administration Build applications without having to manage Server Here are a few key learnings we hope to provide with this presentation that may inspire these organizations to embrace this change as an opportunity to innovate. Paradigm Shift: Companies embarking on new innovation initiatives to determine feasibility of mHealth solutions for clinical research Audience Questions: How many of you currently employ mHealth technologies in your clinical trials? How may of you currently utilize Actigraphy and/or Continous Monitoring devices in your clinical trials? How many of you work in organizations that are now tasked with POCs or pilots using mHealth devices? Data Optimization: Enhancing clinical research with mHealth data requires new techniques against non-conventional sources. mHealth data alone does not enhance clinical research. Practical Applications: Applied use of mHealth in select therapeutic areas in combination with other clinical source data and/or ways where mHealth data may be combined for more visual insights | Copyright ERT 2018

Lambda ARCHITECTURE │ © Copyright ERT 2018

Data science PLATFORM at scale 01 Data pre-processing 02 Feature Engineering Difficult step in AI/ML Time consuming Requires expert domain knowledge Feature selection Feature extraction Requires Platform that Scales Garbage-in, garbage-out Missing value Outliers Data quality (cleaning) Normalization Transformation Requires Platform that Scales Scalable Data Platform Enables AI 04 Model Development 03 Algorithm choice Analytic sandbox Availability of integrated dataset Require Platform that Scales productization automation Dynamic training of model Requires Platform that Scales │ © Copyright ERT 2018

KEY TAKEAWAYS FROM TODAY’S DISCUSSION Data Governance To unleash the potential of data - Master data management, data quality, data profiling, data policies and standards – fosters cross-organizational collaboration Modern Data Foundation Ingest any data of any type of any velocity, Data processing at scale, API-first design, Self service, Real time and batch, enables advanced analytics capabilities including AI and ML Data security and access Governed data store, transparency, regulatory compliance, privacy and protection │ © Copyright ERT 2018

Questions? Prakriteswar Santikary, PhD prakriteswar.santi@ert.com ? …