A significant roadblock organizations face when taking advantage of artificial intelligence (AI) and machine learning (ML) is a legacy database. ITP.net reports that nearly 90 percent of businesses are hindered by legacy technologies, and approximately 62 billion data and analytics work hours are lost annually due to their inefficiencies. Organizations frequently grapple with legacy system issues, including security risks, increased costs, poor data accessibility, and slow AI model training.
To avoid issues that slow growth and depress profits, it is imperative for companies to design and implement new database architectures that efficiently support AI workloads. One effective method is to incorporate a scalable data lake for storage and efficient data pipelines for processing and transforming data. An architecture like this optimizes the flow of information to the AI platform.
Utilizing AI and ML in Database Architectures
The first step in making a legacy database AI-ready is to identify specific business problems the organization wants to solve with AI and ML. Then, determine the data needs for those use cases, performance targets, integration processes, and data governance and security requirements. After defining each element, conduct a detailed assessment of the current database architecture. This assessment verifies how the architecture handles tasks, including data storage capabilities, data ingestion and processing, data access and retrieval, and data management and governance. It also includes insight into the architecture’s integration capabilities.
Following this initial assessment process, organizations can begin structuring their databases to efficiently handle the large volumes and diverse data types used in AI. Typical components to create an AI-ready database include scalable storage, high throughput ingestion, support for diverse data types, in-database processing capabilities, high-performance computing like graphics processing units (GPUs), specialized indexing, data partitioning and sharding, data versioning and lineage, data security and access control, data quality management, and integration with AI/ML frameworks and tools.
One company that transformed a legacy database into an AI-ready database recently moved from relational databases and siloed data warehouses to a lakehouse platform that unifies video, network, and customer interaction data, and automatically develops video content recommendations and identifies fraud. A leading retailer migrated its on-premises relational and data warehouse systems to a cloud-based data warehouse and lakehouse, creating a centralized repository for its massive retail and supply chain data.
A British multinational bank and financial services group headquartered in London also recently transitioned from decades-old on-premises relational systems to cloud-based data platforms to enable real-time analytics, and integrated AI for fraud detection, anti-money laundering (AML), and customer personalization. For other organizations to unlock their own AI benefits, it is essential for them to understand how to configure their architecture to maximize the benefits of AI technology.
Creating the Ultimate AI-ready Architecture
When designing an AI-ready architecture, leverage each technology’s unique strength in different stages of the AI lifecycle. For example, data lakes can serve as a central repository for raw data. Data warehouses can store curated and structured data for analytical queries and business intelligence. Use NoSQL for specific AI use cases where flexible schemas require defined data models and access patterns.
Another key aspect of building an AI-ready architecture is ensuring data pipelines contain all the elements needed for success. Those elements may include diverse data connectors, batch and real-time ingestion mechanisms, scalable storage solutions, data cleaning and preprocessing, and centralized repositories for features. They may also involve integration with ML framework, scalable compute resources, model serving infrastructure, API endpoints, data monitoring, model performance monitoring, and feedback mechanisms. Selecting the right elements allows organizations to efficiently manage and store unstructured, semi-structured, and structured data within a new, unified architecture.
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