Head of Data Management and Analytics

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LivaNova is a global medical technology company built on nearly five decades of experience and a relentless commitment to improving the lives of patients around the world. Our advanced technologies and breakthrough treatments provide meaningful solutions for the benefit of patients, healthcare professionals, and healthcare systems.  The company is listed on the NASDAQ stock exchange under the ticker symbol “ LIVN .”  LivaNova is headquartered in London (UK) with a presence in over 100 countries and a team of more than 3,000 employees worldwide.

Position Summary:

The enterprise data management leader has the primary enterprise accountability of the organization’s data and analytics (D&A) assets to drive value for business stakeholders. This includes creation and management of data and analytics strategy, and execution of related programs and practices that drive measurable business outcomes. This role involves establishing, leading and operating the D&A function; developing talent and mature D&A culture; building trust and managing data; and evolving technology capabilities

General Responsibilities:

• Build partnerships with executive leadership and board members to establish the vision for managing data as a business asset — to exploit data and analytics capabilities to maximize the value derived from data assets.

• Provide strategic direction and oversight for the design, development, operation and support of data management capabilities that fulfill the needs of the organization.

• Define data and analytics vision, strategy and associated practices. Lead the creation (and assure the ongoing relevance) of the organization’s data and analytics strategy in collaboration with the business leaders.

• Define data management framework that is aligned with evolving regulatory, privacy and security requirements

• Institute an enterprise operating model for data and analytics that is consistent with the capabilities and competencies required to execute the strategy.

• Foster the creation of a data-driven culture, related competencies and data literacy across the enterprise. Lead these transformation efforts by developing D&A talent and maturing the capability of the organization.

• Oversee delivery models, methods and practices for creation of data products and to ensure consistent application and use of data and analytics solutions.

• Establish and maintain trust in data assets by instituting governance mechanisms for data and algorithms used for analysis, analytical applications and automated decision making.

• Lead data-driven innovation for the enterprise, including adoption and exploitation of artificial intelligence.

• Foster the creation of a data-driven culture, related competencies and data literacy across the enterprise. Lead these transformation efforts by developing data and analytics talent and maturing the capability of the organization.

• Focus on business outcomes, not reporting. Inventory the business’ KPIs, not just for the purpose of reporting on the business with dashboards and reports. Modern data and analytics strategies use KPIs that describe how data and analytics will be used to improve the business, actually achieving those business outcomes through specific business actions.

Data and Analytics

The Head of Data Management and Analytics needs to have a broad understanding of the full range of strategic data and analytics capabilities, and the ability to communicate these concepts, methods and techniques in ways easily understood by other stakeholders:

• Data and analytics strategy expertise: Acumen for strategic business and technology planning and execution, including policy development and maintenance.

• Data-driven culture change: Playing a critical role in driving and overseeing major business change to deliver enterprise value by managing major data-driven change initiatives.

• Data literacy: the ability to describe business use cases/outcomes, data sources and management concepts, and analytical approaches/options. The ability to translate among the languages used by executive, business, IT and quant stakeholders.

• Analytics and business intelligence: diagnostic, descriptive, predictive and prescriptive analytics approaches.

• Data science and AI: graph analysis, machine learning and natural language processing.

• Data management: data integration (ETL) or metadata.

• Information/data architectures: the differences between data fabric, data mesh, data warehouse, data lake or data hub. Identify and manage the most important business information assets across the organization.

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