Gain Access to the Untapped 99% of Your Data: Optimized for AI Use

Companies of various sizes have long understood the immense value that data brings. This data is crucial for enhancing customer and user experience, as well as creating strategic plans grounded in solid evidence.

With AI becoming more attainable and usable for practical business solutions, the potential value derived from existing data has surged exponentially. Successfully integrating AI demands considerable work in gathering, organizing, and preprocessing data. Furthermore, careful attention must be paid to aspects like data management, privacy, anonymization, regulatory adherence, and security right from the start.

In a dialogue with Henrique Lemes, who leads the Data Platform in the Americas at IBM, challenges that enterprises encounter when applying AI practically across various use cases were discussed. We began by analyzing the essential nature of data, its different categories, and its pivotal role in driving efficient AI-led applications.

Henrique emphasized that merely labeling all business information as 'data' does not capture its complexity. Today’s enterprises operate amidst a fragmented array of diverse data types with varying quality, especially when comparing structured versus unstructured data sources.

Put simply, structured data consists of information that is arranged in a uniform and easily searchable format, enabling seamless analysis and processing by computer systems.

Unstructured data, on the other hand, lacks a predefined structure or organization, making it more intricate to handle and analyze. It encompasses varied formats such as emails, social media posts, videos, images, documents, and audio files. Despite its lack of clear organization, unstructured data holds valuable insights. When managed effectively using advance analytics and AI, it can spur innovation and guide strategic business decisions.

Henrique noted, “At present, less than 1% of enterprise data is harnessed by generative AI, and over 90% of this data is unstructured, which influences trust and quality directly.”

Trust in data is a crucial factor. Organization leaders need to have confidence that their data is complete, dependable, and ethically sourced. However, findings suggest that less than half of the business data is utilized in AI, often due to the complex nature of processing unstructured data and ensuring its compliance, especially on a large scale.

To access improved decision-making based on comprehensive empirical data, there needs to be an amplification from a slow trickle of information to a rapid flow. Henrique suggests that this can be achieved through automatic data ingestion, although governance and data policies must still be applied to both structured and unstructured data.

Henrique outlined the three-step process allowing companies to extract the intrinsic value of their data. “Firstly, scale ingestion; automating this process is crucial. Secondly, focus on curation and data governance. Thirdly, ensure data is available for generative AI application, achieving over 40% ROI compared to traditional RAG scenarios.”

IBM offers a unified approach that taps into a comprehensive understanding of an organization’s AI journey and expertise in advanced software solutions. This method ensures secure transformation of structured and unstructured data into AI-ready resources, all within the framework of current governance and compliance standards.

“We integrate the people, processes, and tools. It’s not straightforward, but we streamline it by aligning the necessary resources,” he shared.

As businesses evolve, their data volume and diversity grow. Thus, the data ingestion process for AI must be scalable and adaptable to accommodate this growth.

“[Companies] face challenges while scaling since their initial AI solutions were tailored for specific tasks. Expanding beyond this often results in complexity in data pipelines and managing unstructured data becomes critical, necessitating robust data governance,” he explained.

IBM's strategy involves understanding each client’s AI path thoroughly, crafting a roadmap to realize ROI with effective AI execution. “We focus on data precision, whether structured or unstructured, ensuring data ingestion, lineage, governance, compliance with industry regulations, and necessary visibility. These features empower our clients to expand over numerous use cases and fully unlock the value of their data,” Henrique concluded.