Unlocking the Value of Data for AI-Powered Applications

For decades, companies of all sizes have recognized the significant value of available data in enhancing user and customer experiences and informing strategic plans based on empirical evidence. With the increasing accessibility and practicality of Artificial Intelligence (AI) for real-world business applications, the potential value of available data has grown exponentially.

The Challenges of Implementing AI

Successfully adopting AI requires substantial effort in data collection, curation, and preprocessing. Moreover, crucial aspects such as data governance, privacy, anonymization, regulatory compliance, and security must be addressed carefully from the outset. In a conversation with Henrique Lemes, Americas Data Platform Leader at IBM, we explored the challenges enterprises face in implementing practical AI in various use cases. We began by examining the nature of data itself, its diverse types, and its role in enabling effective AI-powered applications.

The Complexity of Enterprise Data

Henrique highlighted that referring to all enterprise information simply as "data" understates its complexity. The modern enterprise navigates a fragmented landscape of diverse data types and inconsistent quality, particularly between structured and unstructured sources. Structured data refers to information organized in a standardized and easily searchable format, enabling efficient processing and analysis by software systems. Unstructured data, on the other hand, does not follow a predefined format or organizational model, making it more complex to process and analyze.

The Value of Unstructured Data

Unstructured data includes diverse formats like emails, social media posts, videos, images, documents, and audio files. While it lacks the clear organization of structured data, unstructured data holds valuable insights that can drive innovation and inform strategic business decisions when effectively managed through advanced analytics and AI. Henrique stated, "Currently, less than 1% of enterprise data is utilized by generative AI, and over 90% of that data is unstructured, which directly affects trust and quality."

The Importance of Trust in Data

Decision-makers in an organization need to have complete trust that the information at their fingertips is reliable, complete, and properly obtained. However, evidence suggests that less than half of the data available to businesses is used for AI, with unstructured data often being ignored or sidelined due to the complexity of processing and examining it for compliance, especially at scale.

Automated Ingestion and Governance

To make better decisions based on a fuller set of empirical data, the trickle of easily consumed information needs to be turned into a firehose. Automated ingestion is the answer, but governance rules and data policies must still be applied to both unstructured and structured data. Henrique outlined the three processes that enable enterprises to leverage the inherent value of their data: ingestion at scale, curation and data governance, and making data available for generative AI.

IBM’s Unified Strategy

IBM provides a unified strategy, rooted in a deep understanding of the enterprise’s AI journey, combined with advanced software solutions and domain expertise. This enables organizations to efficiently and securely transform both structured and unstructured data into AI-ready assets, all within the boundaries of existing governance and compliance frameworks. "We bring together the people, processes, and tools. It’s not inherently simple, but we simplify it by aligning all the essential resources," Henrique said.

Image: Henrique Lemes, Americas Data Platform Leader at IBM

Scalability and Flexibility in AI Data Ingestion

As businesses scale and transform, the diversity and volume of their data increase. To keep up, the AI data ingestion process must be both scalable and flexible. Companies often encounter difficulties when scaling because their AI solutions were initially built for specific tasks. When they attempt to broaden their scope, they often aren’t ready, and managing unstructured data becomes essential. This drives an increased demand for effective data governance.

IBM’s Approach to AI Implementation

IBM’s approach is to thoroughly understand each client’s AI journey, creating a clear roadmap to achieve ROI through effective AI implementation. "We prioritize data accuracy, whether structured or unstructured, along with data ingestion, lineage, governance, compliance with industry-specific regulations, and the necessary observability. These capabilities enable our clients to scale across multiple use cases and fully capitalize on the value of their data," Henrique said.

Enabling Data Pipelines for AI

Like anything worthwhile in technology implementation, it takes time to put the right processes in place, gravitate to the right tools, and have the necessary vision of how any data solution might need to evolve. IBM offers enterprises a range of options and tooling to enable AI workloads in even the most regulated industries, at any scale. To find out more about enabling data pipelines for AI that drive business and offer fast, significant ROI, visit this page.