AI and the internet are transforming our understanding of how the future happens, enabling us to acknowledge the chaotic unknowability of our everyday world.

Back when we humans were the only ones writing programs, data looked like the oil fueling those programs. But now that machines are programming themselves, data looks more like the engine than its fuel. This is changing how we think about the world from which data arises, and that data is now shaping as never before. We’ve accepted that the intelligence of machine intelligence resides in its data, not just its algorithms—particularly in the countless, complex, contingent, and multidimensional interrelationships of data. But where does the intelligence of data come from? It comes from the world that the data reflects. That's why machine learning models can be so complex, we can't always understand them. The world is the ultimate black box. Weinberger looks at the implications of this for people who work with data.

iRobot, the leading global consumer robot company, designs and builds thoughtful robots and intelligent home innovations that make life better for customers across the globe. With over 50 million units sold worldwide, iRobot relies on accurate, highly reliable data to drive decision making and power operations across the business to fuel their explosive growth. Leber discusses why and how the team invested in data observability with Monte Carlo to improve data quality across the business and help its company maximize the potential of their data.

Sponsored by

Are you drowning in data but lacking in insight? Eighty percent of business leaders say data is critical in decision-making, yet 41% cite a lack of understanding of data because it is too complex or not accessible enough. Companies are using graph databases to leverage the relationships in their connected data to reveal new ways of solving their most pressing business problems and creating new business value for their enterprises. Mohr shows real-world use cases that include real-time recommendations, fraud detection, network and IT operations, AI/ML, supply chain management, and more.

Sponsored by

Data silos continue to impede access to needed information. Solutions are on the horizon.

With a 133-year history, Northern Trust has a backbone of IT infrastructure built decades ago, when on-premises solutions dominated the technology landscape. Due to the complexity of global regional regulatory requirements and the limitations of legacy systems, valuable data assets are maintained and isolated only in online transactional processing (OLTP) databases. The company faced challenges in data sharing, management, and governance in supporting enterprise-level analytics projects to meet business needs and growth. A digital modernization initiative took place that had a data mesh ecosystem as a critical component, leveraging cloud services on Azure and other modern technologies.

Enable your data team to get the most value from their time and quickly deliver needed business insights through a next-generation data fabric methodology.

MacWilliams introduces a technology agnostic methodology that solves the common challenges facing data teams and focuses on the processes among technologies—on-board data faster, flex automatically when data changes, create solutions that are manageable across technologies, and provide the foundation to be able to monitor and maintain your data fabric for the future. The methodology integrates with already existing technologies. Starting with the end in mind, MacWilliams covers how to best monitor and maintain your platform, how to maximize data team capacity, how to utilize meta-data to streamline your team’s development, and where to build custom and leverage modern technologies.

Moving to the cloud is now a normal function but presents interesting new challenges.

As companies look to scale, they face new and unique challenges related to data management in the cloud. Data mesh offers a framework and a set of principles that companies can adopt to help them scale a well-managed cloud data ecosystem. Learn how Capital One approached scaling its data ecosystem by federating data governance responsibility to data product owners within their lines of business and hear how companies can operate more efficiently by combining centralized tooling and policy with federated data management responsibility.

Making sense of all your input data isn’t fun, especially when consuming inputs from 10s to 1,000s of data sources daily. If your data teams are orchestrating massive amounts of data across multiple data pipelines, it’s nearly impossible to feel confident in the data quality within your data warehouse. Instead of retroactive data monitoring, it’s time for a more proactive approach to ensure better data quality for your warehouse.

New technologies can transform companies’ data journeys.

Data fabrics and data meshes are promising paradigms for helping organizations on their data journeys. Data fabric is a new approach complementing the existing infrastructure and data management technology, accessing the data on demand as it’s needed by the consumers of the data, with centralized metadata and governance. Data mesh accesses the data on demand, providing the metadata and governance capabilities at the edges of the organization, where the data resides, enabling agility and autonomy throughout the organization. While much of the conversation around data fabrics and data mesh has been primarily about which approach or architecture is “better,” Fried discusses how the real value of these concepts isn’t rooted in an “either/or” approach and why they must be viewed as complementary.

Data architectures tend not to be static and new approaches are always welcome.

The concept of data mesh has resonated strongly with both data professionals and the broader engineering community. Loose coupling, enablement of federated development, and data sharing ease the difficulty of data management in both large and small organizations, as well as bringing data systems closer to parity with modern microservice-based systems. Cordo explores how the adoption of an event-based data architecture can enable an organization's sustainable transition to data mesh. This includes an overview of event-based architecture, architectural patterns for event-based data systems, and organizational considerations.