“Data is the new oil.” This axiom, first used nearly 20 years ago, highlights data’s strategic value when properly extracted and processed. However, this saying has new meaning in the AI era: Just as combustion engines grind to a halt without oil, the engines of AI innovation do the same without data.
Yet data is frequently overlooked as a requirement for enterprise-grade AI initiatives, as the focus is often more on AI outputs than its inputs. Data scientists can piece together enough data for a proof of concept, but effective and comprehensive deployment isn’t possible without data that’s AI-ready at scale. Organizations must invest in establishing a modern data landscape to handle the demands of AI workloads.
The Need for Data Platform Modernization
As noted in our recent blog, a strong foundation is the solution to modern data dilemmas. The ever-increasing volume and velocity of data, as well as real-time processing needs, create new storage, security, and management requirements for supporting AI. However, organizations often find that outdated data systems and infrastructures are ill-equipped to handle the combination of disparate data sources and very large datasets—a baseline requirement to support the next generation of AI initiatives.
Challenges with data typically fall into one of three broad categories—architecture, quality, and accessibility—with integration issues as a common denominator. Fragmentation is at the center of many of these challenges making data difficult to access across heterogeneous platforms. In addition, data architecture challenges extend to outdated management and storage systems that don’t properly talk or interface with one another.
To pave the way for AI adoption, businesses must first remove the architectural barriers they encounter by making large volumes of structured and unstructured data more accessible and useful. However, since every business has its own investments, needs, and objectives, data platform modernization efforts must be tailored accordingly to support the unique requirements of your organization.
Connecting Disparate Data Sources
Most businesses operate within a siloed data landscape where disconnected data stores, make it difficult to standardize, integrate, and reconcile data. In these disparate environments, AI models can’t leverage the high-quality data needed at scale to ensure viable outputs or learning. Successful AI implementations require clean, accessible data from multiple sources—which data pipelines have helped broker.
Traditional data pipelines can automate the movement and transformation of data, ensuring it is standardized and accessible. They ingest data from various sources, cleanse it by removing errors and inconsistencies, and then aggregate, structure, and standardize it according to business logic. However, scaling AI initiatives remains a challenge, as conventional data pipelines often struggle to handle the high volume and velocity of data required by AI-driven workloads—a challenge that is becoming more apparent with the advent of GenAI and large language models.
Data Lakehouses: Create An AI-Ready Platform
To unlock AI’s full potential, businesses must build a modern data platform—one designed for speed, security, and seamless integration with diverse data repositories.
For complex business environments, a data lakehouse provides a robust foundation and standardized architecture for AI adoption. By merging the scalability and flexibility of data lakes with the structured management of data warehouses, lakehouses overcome the common limitations of traditional data pipelines—enabling organizations to manage growing data volumes and create a unified source of truth for enterprise data.
Data is processed through distributed computing engines with built-in governance, access controls, and high-performance query capabilities, optimizing data utilization and enhancing analytics. This architecture eliminates silos, simplifies data ecosystems, and establishes a centralized platform to power AI, GenAI, and other data-driven initiatives.
Get Started with Ntirety Managed Data Lakehouse Service
While data lakehouses are the gold standard for supporting AI adoption, building and optimizing them on your own requires significant expertise, time, and resources. Instead of navigating this complexity alone, Ntirety Managed Data Lakehouse offers a fully managed experience, delivering everything needed to successfully leverage a data lakehouse for AI initiatives. Nitrety unifies your disparate data sources into a single repository, ensures fast, consistent, and efficient access to data, and establishes proper connections to BI, AI/ML, analytics, and reporting solutions. This means you can unlock the full power of data and AI—all while reducing the operational overhead needed to establish and manage a well-architected lakehouse.
To learn more about our Managed Data Lakehouse Service or the full portfolio of Ntirety Data Services, access our recent eBook.
