By 2026, over 80% of enterprises will have adopted AI APIs or generative AI applications. AI models, and the data to train and fine-tune them, can take applications from generic to effective, delivering tangible value to customers and businesses.
For example, the Masters' generative, AI-driven golf fan experience uses real-time and historical data to provide insights and commentary for over 20,000 video clips. The quality and quantity of data can make or break AI, and organizations that effectively leverage and manage data will reap the greatest benefits. But things aren't that simple. Data is exploding in both volume and variety.
According to the International Data Corporation (IDC), by 2025, stored data will increase by 250%. Data governance and management are becoming increasingly important across on-premise and cloud platforms. With growth comes complexity. Multiple data applications and formats make it difficult for organisations to effectively access, govern, manage and use all their data for AI. Leaders must rethink the use of prohibitive on-premise approaches and monolithic data ecosystems while reducing costs and ensuring proper data governance and self-service access to more data across disparate data sources.
Leveraging technology, people, and processes to scale data and AI
Leveraging data as an AI differentiator requires a balance of technology, people, and process. To expand your AI use cases, you must first understand your strategic goals for data, which may be changing with generative AI. Align your data strategy with your future architecture, while considering existing technology investments, governance, and autonomous management. Leverage AI to automate tasks such as data onboarding, data classification, organization, and tagging. This requires evolving your data management processes and updating your learning paths.
Building an open and trusted data foundation
To access trusted data for AI, organizations must focus on building an open and trusted data foundation. Open means building a foundation for storing, managing, integrating, and accessing data based on open and interoperable capabilities across hybrid cloud deployments, data storage, data formats, query engines, governance, and metadata. This will ease integration with existing technology investments, eliminate data silos, and accelerate data-driven transformation.
Building a trusted data foundation enables high-quality, reliable, secure, governed data and metadata to power analytics and AI applications while meeting data privacy and regulatory compliance needs. The following four components help build an open and trusted data foundation:
1. Modernizing data infrastructure to hybrid cloud for applications, analytics, and AI generation
Adopting a multi-cloud and hybrid strategy is becoming a necessity, requiring databases that support flexible deployment across hybrid clouds. Gartner predicts that 95% of new digital initiatives will be developed on cloud-native platforms, which is essential for AI technologies that require massive data storage and scalability.
2. Power data-driven applications, analytics, and AI with the right database and open data lakehouse strategy
For data storage and analysis, The right database for the right workloaddata type, and price performance. This ensures a data foundation that can scale with your data needs, no matter where your data resides. Your data strategy should incorporate a database designed with open, integrated components that allow seamless integration and access to data for advanced analytics and AI applications within your data platform. This helps organizations unlock valuable insights and drive informed decision-making.
For example, in an organization High performance, secure and resilient Transactional Database Manage your most critical operational data. Hybrid cloud capabilities enable organizations to use databases to modernize legacy apps, build new cloud-native apps, and power AI assistants and enterprise applications.
As data types and applications evolve, you may need specialized NoSQL databases to handle them. Diverse data structures and specific application requirements. These include time series, document, messaging, key-value, full-text search, and in-memory databases to serve a variety of needs including IoT, content management, and geospatial applications.
Providing power AI and Analytics Workloads Across transactional and proprietary databases, you need to ensure they can seamlessly integrate with your open data lakehouse architecture without duplication or additional extract, transform, and load (ETL) processes. An open data lakehouse gives you access to a single copy of your data, no matter where it resides.
An open data lakehouse handles multiple open formats (such as Apache Iceberg on cloud object storage) and combines data from different sources and existing repositories across the hybrid cloud. The most cost-effective data lakehouses use multiple open source query engines to separate storage and compute, and integrate with other analytical engines to optimize workloads and deliver superior price performance.
This includes integration with data warehouse engines that must balance real-time data processing and decision making with cost-effective object storage, open source technologies, and a shared metadata layer to seamlessly share data with the data lakehouse. Open data lakehouse architectures can now optimize data warehouse workloads for better cost performance and modernize traditional data lakes with better performance and governance for AI.
Enterprises may have petabytes, if not exabytes, of valuable proprietary data stored on their mainframes that need to be unlocked to create new insights and ML/AI models. With an open data lakehouse that supports data synchronization between mainframes and open formats like Iceberg, organizations can better identify fraud, understand constituent behavior, and build predictive AI models to understand, predict, and influence advanced business outcomes.
Before you build Trusted generative AI Businesses need the right data architecture to prepare this disparate data and transform it into quality data. For generative AI, the right data foundation includes a variety of knowledge stores, such as a NoSQL database for conversations, a transactional database for contextual data, a data lakehouse architecture to access and prepare data for AI and analytics, and vector embedding capabilities to store and retrieve embeddings for search augmentation generation (RAG). A shared metadata layer, governance to catalog data, and data lineage enable trusted AI output.
3. Establishing a foundation of trust: Data quality and governance for enterprise AI
As organizations become increasingly reliant on artificial intelligence (AI) to drive critical decisions, the importance of data quality and governance cannot be overstated. According to Gartner, by 2025, 30% of generative AI projects will be abandoned due to poor data quality, inadequate risk management, rising costs, and unclear business value. The impacts of using poor quality data are far-reaching and can include eroded customer trust, regulatory non-compliance, and financial and reputational loss.
Effective data quality management is essential to mitigate these risks. A well-designed data architecture strategy is essential to achieving this goal. Data Fabric provides a robust framework for data leaders to profile data, design and apply data quality rules, detect data quality violations, cleanse data, and enrich data. This approach ensures that data quality initiatives deliver accuracy, accessibility, timeliness, and relevance.
Additionally, Data Fabric allows for continuous monitoring of data quality levels through data observability, allowing organizations to identify data issues before they become major problems, and transparency into data flows allows data and AI leaders to identify potential issues and ensure the right data is used to drive decision-making.
By prioritizing data quality and governance, organizations can build trust in their AI systems, minimize risk, and maximize the value of their data. It's important to recognize that data quality is not just a technical issue, but a critical business imperative that requires attention and investment. Adopting the right data architecture strategy can help organizations realize the full potential of their AI initiatives and drive business success.
4. Managing and distributing data for AI
From building AI models with the right data sets, to tuning AI models with industry-specific enterprise data, to building RAG AI applications (such as chatbots, personalized recommendation systems, and image similarity search applications) with vectorized embeddings, data is the foundation of AI.
Trusted, governed data is essential to ensure accuracy, relevance, and precision in AI. To unlock the full value of data for AI, enterprises must be able to navigate complex IT environments to break down data silos, integrate data, and prepare and deliver trusted, governed data for AI models and applications.
An open data lakehouse architecture using open formats allows you to connect and access critical data from existing data estates (including data warehouses, data lakes, and mainframe environments) to build and tune AI models and applications using a single copy of enterprise data.
The semantic layer allows us to generate data enrichments that enable our clients to search and understand effectively structured data across previously esoteric data estates in natural language through semantic search, accelerating data discovery and delivering data insights faster, without the need for SQL.
With a vector database embedded directly within the lakehouse, data can be seamlessly stored and queried as vectorized embeddings for RAG use cases, improving the relevance and accuracy of AI output.
Build and create value with data products, AI assistants, AI applications and business intelligence
With an open and trusted data foundation, you can unlock the full potential of your data and create value from it by building data products, AI assistants, AI applications, and business intelligence solutions that leverage AI and data platforms that use trusted data.
For example, data products are reusable packaged data assets, such as predictive models, data visualizations, and data APIs, that can be used to drive business value. AI assistants, applications, and AI-powered business intelligence help users make better decisions by providing insights, recommendations, and predictions. With the right data, you can build a data-driven organization that drives business value and innovation.
To start building the data foundation for AI, consider data management solutions with IBM® Database, watsonx.data™ and Data Fabric to augment your AI with trusted data.
Explore our solutions and learn how to design and build your ideal data estate
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