Organizations worldwide invest in data infrastructure; Only 41.4% usable for AI.
Organizations worldwide invest in data infrastructure; Only 41.4% usable for AI. What is data infrastructure? Data infrastructure refers to the systems, tools and capabilities that allow organizations to collect, store, process, govern and use data. Modern data infrastructures can include components such as cloud-based storage systems, on-premises or hybrid storage, scalable compute resources, data pipelines, governance tools and analytics platforms. They underpin many of the critical functions and operations that organizations depend on, allowing them to fully leverage their data assets for decision-making and analysis. Effective data infrastructure is also the cornerstone of trustworthy and high-performance artificial intelligence (AI). In fact, inadequate infrastructure is among the top barriers preventing enterprises from successfully adopting AI, according to research conducted by IBM’s Institute for Business Value (IBV).1 An organization’s data infrastructure is the foundation that makes data analysis, decision-making and innovation possible. It manages, unifies and prepares enterprise data for effective use—which is a complex challenge in today’s big data environments where information arrives quickly and in high volumes. Consider that unstructured data represents 80% to 90% of the world’s digital information and the majority of data generated by businesses.2 It’s the emails, PDFs, chat logs and meeting notes created and shared every day. Unlike structured data, which tends to follow a predefined schema, unstructured data can be inconsistent or context-dependent. As a result, organizations can’t tap into its value without proper management and processing. A strong data infrastructure also creates the unified data foundation necessary for AI systems to operate. “Enterprise AI at scale is finally within reach,” IBM Vice President and Chief Data Officer Ed Lovely said in a recent IBV report.3 “The technology is ready—as long as organizations can feed it the right data.” Research conducted by the IBV shows that, on average, only 41.4% of surveyed organizations’ proprietary data is usable for AI (sufficiently clean, labeled, standardized, governed or otherwise cleared for modeling).4 The main data challenges inhibiting that use include issues with completeness (50.4%), data integrity (48.8%), and accuracy and consistency (both 47.1%), illustrating how the strength of an organization’s data infrastructure shapes its ability to deploy AI effectively. Finally, strong data infrastructure supports data governance, security and compliance. As regulatory requirements increase and data privacy becomes more important—including under frameworks such as the General Data Protection Regulation (GDPR)—organizations need clear policies that define who can obtain data, how it’s used and how it’s protected. Stay up to date on the most important—and intriguing—industry trends on AI, automation, data and beyond with the Think newsletter. See the IBM Privacy Statement. Well-designed data infrastructure builds data trust, aligns insights with business needs and strengthens competitive advantage. The benefits of a strong data infrastructure include: Data infrastructure can optimize data quality by providing the technologies and systems that transform, clean and validate data, such as data warehouses, automated ETL ...