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.
Tips for Successful Transformation
Organizations often run into seven pitfalls that prevent them from successfully transforming a legacy database into an AI-ready one. Here is how to avoid each of these common mistakes:
Underestimating data migration complexity. Conduct a thorough data assessment, then use a phased migration strategy to deal with issues promptly and ensure they don’t “snowball.” Not understanding AI/ML workload requirements. Avoiding this mistake requires strong collaboration between AI/ML teams, benchmarking AI workloads, designing for flexibility, and keeping scalability needs in mind throughout the process. Neglecting data governance and security. Integrate governance and security from the beginning of the initiative and implement data classification, access controls, and data encryption. Overlooking integration with existing systems and tools. Avoid this by engaging in comprehensive integration planning, compatibility assessment, and gradual integration. Insufficient performance testing and optimization. Ensure rigorous performance testing and optimization and establish strong performance baselines. Neglecting monitoring. It is crucial for organizations to implement robust monitoring, which includes setting up alerting mechanisms and establishing logging and tracking systems to maintain high security. Lack of a clear roadmap. Developing a comprehensive blueprint that prioritizes phased implementation and an iterative approach mitigates this concern.
To successfully transform a legacy database into an AI-ready database, organizations can use automation and DevOps principles to significantly enhance efficiency, reliability, and scalability. Harness scaling, backup, recovery, monitoring and alerting, self-service database provisioning, and policy as code to ensure seamless management and deployment of AI-ready database architectures. Organizations can also incorporate infrastructure as code, configuration management, automated provisioning, and continuous integration/continuous delivery (CI/CD) for database schema and configuration changes.
Another Key to Success–Talented Staff
The quality of the personnel who will build and implement an AI-ready database architecture is essential to the initiative’s success. Effective and ethical AI/ML implementation within database architectures is a shared responsibility that spans various organizational roles. AI/ML engineers and data scientists, data engineers, database administrators, AI architects, data governance and compliance teams, and business leaders and stakeholders all hold varying degrees of responsibility. That makes it vital for organizations to employ the right talent.
They can accomplish this by clearly defining roles and responsibilities, integrating ethics and governance into hiring criteria, providing specialized training and upskilling, fostering cross-functional collaboration, establishing centers of excellence or working groups, promoting a culture of continuous learning, seeking external expertise, implementing robust review processes, and investing in tools and technologies. Organizations that take the time to assemble a talented, competent team and conduct thorough analysis and planning before implementation can efficiently turn their legacy database into a modern architecture that maximizes performance flexibility, reduces model training time, and enables faster deployment of highly effective AI-driven solutions. These benefits can power companies to new heights in the future.
About the Author
Vignyanand (Viggy) Penumatcha is a cloud database modernization expert with more than 15 years of experience in database architectures. For the last eight years, he has specialized in cloud migrations for the healthcare, insurance, telecom, education, and financial industries. His expertise spans DevOps, infrastructure as code, automation, and delivering scalable, secure, and efficient solutions across relational and NoSQL databases. He holds a master’s degree in engineering and technology management from George Washington University. He is also a Senior Member at IEEE and a Fellow Member at Soft Computing Research Society. Connect with Viggy on LinkedIn.