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Data, Data Everywhere: a How to Guide to Data Hubs in Life Sciences

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Data, Data Everywhere: a How to Guide to Data Hubs in Life Sciences PhUSE US Connect 2019 Paper AB05 Data, Data Everywhere: A How to Guide to Data Hubs in Life Sciences Bernard Panes, Accenture, London, United Kingdom INTRODUCTION All life sciences organisations can access more data than ever before, Electronic Medical Records, personal genomic data, lifestyle data, and data from wearable devices for example. Organizing this responsibly to derive insight and change behaviour is at once a great challenge and opportunity. Many organisations we work with are seeking an architectural approach to marshal relevant data from the ecosystem. We're frequently asked questions like How many hubs are needed? What are guiding principles for the split of hubs? How do we treat structured and unstructured data within a hub? Which type of data is put into a data lake, a data hub and data mart? In this paper we'll draw a line right through from Strategy to Implementation and from Data to Action, covering themes like Data Veracity and Frictionless Business to explore approaches that enable you to crack open the data goldmine. PHARMA AT A TIPPING POINT The pharma industry is at a tipping point. The convergence of biology, data science and digital technologies will dramatically drive progress in medicines, fundamentally reforming research and development (R&D) and healthcare. Those who prepare, will lead the transformation. Technology giants are disrupting the pharmaceutical industry, with heavy investment in healthtech. For example, Google developed a smart lens to detect blood sugar levels, with a visual alert to users if levels are dangerous, while Apple launched its ResearchKit platform and within 24 hours, 11,000 iPhone users registered for a study—more than most clinical studies find in a year. There are new competitors entering the life sciences space and many of them, especially Apple & Google, have two critical capabilities that enable them to drive great personalized value to patients and other stakeholders throughout the R&D value chain. 1. A user design focus that allows them to create great experiences. 2. They are fuelled by data; a tremendous capability to capture, integrate, and identify the insights from large disparate datasets, which they can turn into value for themselves and their customers. NEW ECONOMIC REALITY • Unsustainable rise in costs: The cost of caring for our population is outpacing growth, squeezing governments, and pressuring providers, The life sciences market payers, and patients. • Outcomes-based reimbursement: The transition from fee-for- shift has its origin in three service to fee-for-value is bolstered by governments, e.g. In the US fundamental forces; new 50% of Medicare payments via alternative models in 2018. • New business models: The blurring of lines between payers and economic realities, shifting providers drives new outcomes-based business models for Life demand dynamics, and Sciences organizations. • Patent cliff: A weak pipeline of new blockbuster drugs points to long- digital alignment. term financial struggles for established pharmaceutical companies. 1 SHIFTING DEMAND DYNAMICS • Consumerization of healthcare: Individuals bear a greater share of costs and therefore play a greater role in their healthcare management— from choosing their health plans and providers, to more actively managing their care. • Chronic disease: An increase in cardiovascular disease, obesity, diabetes, and more, is driving increased emphasis on wellness, fitness, and prevention. • Silver tsunami: As baby boomers reach retirement, the demographic shift creates a wave of higher medical spend and demand for new means of engagement. • Emerging markets: There’s a growing demand for healthcare services in emerging economies that struggle with cost, quality, and access. DIGITAL ALIGNMENT AND ARTIFICIAL INTELLIGENCE • Data accessibility and advanced analytics: Higher volumes of data generation and transmission require greater access and management while advanced analytic techniques allow us to gain new insights. • Augmentation and automation: Complementing humans with robot workers increased productivity and accuracy. For example, Robotic Process Automation reduces or eliminates manual manipulation of data. • Collaborative platforms: Technologies, such as distributed ledgers and API led architectures enable organisations to interact with consumers and with each other in new ways. • Internet-of-Things: Increased connection between and embedded intelligence within everyday objects e.g. weighing scales, pill dispensers enables them to act more intelligently and act together Individually, each of the drivers of disruption would be a powerful force for change in life sciences. But as they converge, they’re forcing tectonic shifts across the industry, and the points of value creation and realization will shift in the new, digitally enabled economy. To be relevant participants in the new health- and wellness-based economies, life sciences companies will need to change their business models and move towards ecosystem partnerships, as well as fully embrace technological advances. In our view, Data Hubs are one of the core enabling capabilities that Life Sciences organisations must master to evolve to new value vectors. NEW VECTORS OF VALUE DRIVEN BY DATA We foresee the evolution of life sciences companies through several possible value vectors, all fueled by data: 1. Focus on brilliant products: Great science is at the heart of the traditional Pharma model. How can all available data be leveraged to drive breakthrough science through to launch and delivery of great products? 2. Differentiation by creating enhanced smart products that can use Realtime context relevant data to build services that engage patients 3. Adaptation to KPI driven outcome-focused models, becoming accountable to payers for the economic value of the system. 4. Evolution into a true platform player, combining the data and capabilities of multiple organisations to connect all the pieces and create substantive value across the full health and wellness economy. DATA IS A SOURCE OF TRAPPED VALUE Accenture research (https://www.accenture.com/gb-en/insights/consulting/innovation-investment-value) into how organisations can unlock the value of their innovation investments and become a ‘high-growth’ company indicates that o just 14% of companies in our survey have grown, and expect to grow, both profits and market Cap above the industry average. High-growth companies apply innovation practices to fundamentally change their way of doing business, being ‘Data Driven’, generating, sharing and deploying data to deliver new product and service innovations safely & security, is one of the seven common characteristics we identified 2 Ultimately, life sciences organisations need to grow and evolve, and Data Hubs are a core capability that enables organisations to achieve this by becoming more data driven. High growth companies are more data driven compared to others A WORD ABOUT VALUE Data in and of itself has indirect value; the real benefit is achieved once that data turned into insight that can be used to create brilliant experiences for people further down the line is the impact on human experiences that is valuable. We identify several patterns for realising value from data: INTELLIGENT AUTOMATION Cognitive capabilities on top of automation technologies with the following abilities: self-learning, autonomous, reactive, and proactive. Results drive enhanced profitability through more efficient processes, activities, and services. ENHANCED JUDGEMENT Leverage Artificial Intelligence capabilities to augment Human intelligence on core Human-driven Processes. Enable growth by improving quality and effectiveness of the human decision making. ENHANCED INTERACTION Deliver Superior experience to customers and users based on hyper-personalization and curation of real- time information. Drives growth in customer acquisition, retention, and overall satisfaction. INTELLIGENT PRODUCTS Artificial Intelligence is enabling a new class of products and services – applying AI into new and innovative products, services, and new business models. Accelerate growth by introducing new products and services with speed and quality. RESPONSIBLE AI Build trust within the organization using AI (e.g., compliance, transparency) and how AI is used. 3 Enables lower costs to govern and oversee the organization. Drives organization trust and limits disruption and costs from AI implementation. DIGITAL TWINS A digital twin is a digital representation of a real-world entity or system. Gartner Predicts that in the “Future models of humans, that could include rich biometric and medical data, will be used for advanced simulation, operations and analysis” (Gartner top 10 strategic technology trends for 2018) Each of these warrants several papers and is the focus of many peoples’ careers. However, the common thread is that some sort of data substrate is required to deliver data at the right time and quality. A data hub is the best way to do this. SO, WHAT IS A DATA HUB? This is a surprisingly difficult question to answer, there are many different definitions and so I’ll settle on the Gartner one: “a conceptual, logical and physical "hub" for mediating semantics (in support of governance and sharing data) between centrally managed and used data (i.e., widely used) and locally managed and used data (typically single-use data). Everything that persists or uses information is a hub….” I like the Gartner definition because it seems comprehensive, but also captures the important functional
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