Eric Walk
Director, Enterprise Data Strategy at Perficient
Cambridge, Massachusetts, United States
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Eric Walk is the Director of Enterprise Data Strategy at Perficient. As Director of Enterprise Data Strategy, he focuses on the intersection of strategy, data and technology, and business outcomes that drive growth. Eric has spent his career in consulting, taking advantage of opportunities to expand and grow. He started in Enterprise Document Management and Business Automation working with clients to modernize platforms and take advantage of the data trapped in their warehouses of virtual paper. He jumped at the opportunity to lead some early exploration of Big Data technologies with hybrid cloud architectures (Hadoop + AWS) and eventually found himself leading a segment of that practice at Perficient. Eric has since transitioned to lead Perficient’s Data Strategy capability across geographies and practices. In this capacity he serves as an advisor to executives both clients and internally on topics related to data discovery, availability, and trust. He serves as the editor-in-chief of thought leadership aligned to the firm’s Data + Intelligence pillar.
Eric graduated from Vanderbilt in 2011. He holds a Bachelor of Engineering in Biomedical and Electrical Engineering with a minor in Engineering Management and currently resides in Cambridge, Massachusetts.
Area of Expertise
Topics
Simplified Cross Platform DataOps
Using simple tools and standard DevOps practices to achieve the long-term dream of an operationalized data platform. We'll show how we can use dbt, GitHub, and Grafana to build a standardized approach regardless of underlying cloud data platform. The focus will be on Test Automation, Observability, and Pipeline Orchestration for our demonstration.
Adapting AI Governance for Responsible Generative AI Adoption
As early adopter organizations are making headlines with new and exciting Generative AI capabilities, those weekly headlines remind us that GenAI solutions also carry additional risks. Understandably, many organizations are beginning to turn attention to governance and management practices that allow them to capture value quickly while also ensuring responsible use of powerful tools.
In this discussion, we’ll explore:
* Dimensions of Risk: Identify the risk factors with GenAI solutions and how they compare to traditional AI
* Intellectual Property and Observability: Navigate the complexities of public-vs-private models and training data to manage legal, ethical and data protection implications
* Redefining “Quality”: Adapt quality monitoring of input and output data to improve predictability of GenAI solutions
* Critical Participation: Recognize key voices across the organization and how their roles change
* Supporting Initiatives: Advocate for Data Governance, MLOps and other programs to support risk management
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