Blog Article
March 25, 2025
Shift Left Data Conference
March 30, 2025
Data Quality
March 5, 2024
August 14, 2024
Join industry-leading CEOs Chad (Data Contracts), Tristan (Data Transformations), Barr (Data Observability), and Prukalpa (Data Catalogs) who are pioneering new approaches to operationalizing data by “Shifting Left.” This engaging panel will explore how embedding rigorous data management practices early in the data lifecycle reduces issues downstream, enhances data reliability, and empowers software engineers with clear visibility into data expectations. Attendees will gain insights into how data contracts define accountability, how effective transformations ensure data usability at scale, how proactive how proactive data and AI observability drives continuous confidence in data quality, and how catalogs enable data discoverability, accelerating innovation and trust across organizations.
April 1, 2025
Real-time web data is one of the hardest data streams to automate with trust since web sites don't want to be scraped, are constantly changing with no notice, and employ sophisticated bot blocking mechanisms to try to stop automated data collection. At Sequentum we cut our teeth on web data and have come out with a general purpose cloud platform for any type of data ingestion and data enrichment that our clients can transparently audit and ultimately trust to get their mission critical data delivered on time and with quality to fuel their business decision making.
Wayfair’s multi-year Data Mesh journey involved shifting from a monolithic, centralized data model to a decentralized, domain-driven architecture built on microservices. By embracing Data Mesh principles, Wayfair empowered domain teams to take end-to-end ownership of their data. Key enablers included a data contract management platform ensure trusted, discoverable data products, and the development of Taxon, an internal ontology and knowledge graph that unified semantics across domains while supporting the company's tech modernization. Organizationally, Wayfair introduced an Embedded Data Engineering model – embedding data engineers within domain teams – to instill a “Data-as-a-Product” mindset among data producers. This sociotechnical shift ensured that those who create data also own its quality, documentation, and evolution, rather than relying on a centralized BI team. As a result, Wayfair’s data producers are now accountable for well-defined, high-quality data products, and data consumers can more easily discover and trust data through the unified catalog and ontology. The presentation will highlight how Wayfair has adopted the “shift left” (pushing data ownership and quality to the source teams) and next heading towards “shift right” (focusing on consumer-driven data products and outcomes) to unlock business outcomes. This session will share both technical strategies and business results from Wayfair’s Data Mesh journey.
Artificial Intelligence is reshaping the landscape of software development, driving a fundamental shift towards empowering developers to take control earlier in the development lifecycle—known as "shift left." In this panel, venture capital leaders and industry experts will explore how emerging trends in AI and data technologies are influencing investment decisions, creating new opportunities, and transforming development workflows. Attendees will gain valuable insights into the evolving market dynamics, understand the strategic significance of shifting left in today's AI-driven world, and discover how organizations and developers can stay ahead in this rapidly changing environment.
This talk covers Adevinta Spain's transition from a best-effort governance model to a governed data integration system by design. By creating source-aligned data products, this shift aims to enhance data quality and reliability from the moment data is ingested.
As Glassdoor scaled to petabytes of data, ensuring data quality became critical for maintaining trust and supporting strategic decisions. Glassdoor implemented a proactive, “shift left” strategy focused on embedding data quality practices directly into the development process. This talk will detail how Glassdoor leveraged data contracts, static code analysis integrated into the CI/CD pipeline, and automated anomaly detection to empower software engineers and prevent data issues at the source. Attendees will learn how proactive data quality management reduces risk, promotes stronger collaboration across teams, enhances operational efficiency, and fosters a culture of trust in data at scale.
Data DevOps applies rigorous software development practices—such as version control, automated testing, and governance—to data workflows, empowering software engineers to proactively manage data changes and address data-related issues directly within application code. By adopting a "shift left" approach with Data DevOps, SWE teams become more aware of data requirements, dependencies, and expectations early in the software development lifecycle, significantly reducing risks, improving data quality, and enhancing collaboration. This session will provide practical strategies for integrating Data DevOps into application development, enabling teams to build more robust data products and accelerate adoption of production AI systems.
A core idea behind shifting Data Left is simple but often overlooked: data is code. Or more accurately—data is produced by code. It’s not just some downstream artifact that lives in tables and gets piped into dashboards and spreadsheets. Every record, event, or log starts somewhere—created, updated, or deleted by a line of code. And just like DevOps demonstrated, if you want to manage something well, you start at the point of creation.
Data governance
Schema on read promised flexibility—but instead created chaos. By removing constraints without guardrails, we unleashed brittle, unmanageable data systems. It’s time to fix the mistake. Learn how data contracts, enforced at the source, can restore trust and eliminate downstream firefighting.
March 24, 2025
