For many years, businesses have understood the immense value of data, using it to enhance user experiences and shape strategic decisions based on concrete evidence. With the rise of AI technology, the importance of data has surged, opening new avenues for practical applications in the business world. However, to leverage this potential effectively, companies must invest significant effort into data collection, curation, and preprocessing, while also ensuring compliance with data governance, privacy, and security protocols.
In a discussion with Henrique Lemes, the Americas Data Platform Leader at IBM, we explored the various challenges enterprises encounter when implementing AI in a practical context. The conversation began with the complexity of data itself, which can be broadly categorized into structured and unstructured types. Structured data is organized in a standardized format, making it easy to search and analyze.
Conversely, unstructured data lacks a predefined format and includes a variety of formats such as emails, images, and social media posts. Although unstructured data is more difficult to process, it can provide critical insights that drive innovation and improve business decisions when managed effectively. Currently, less than 1% of enterprise data is utilized by generative AI, with over 90% being unstructured data.
This discrepancy poses trust issues, as organizations often struggle to rely on the available data due to its incomplete and unreliable nature. To facilitate better decision-making, businesses must transform the limited flow of accessible information into a robust influx. Automated data ingestion is a vital solution in this regard, while maintaining governance rules and policies for both structured and unstructured data.
Henrique outlined three key processes for enterprises to maximize the value of their data: first, scalable ingestion, followed by curation and data governance, and finally, making the data available for generative AI. IBM’s unified strategy emphasizes understanding each client’s unique AI journey and provides advanced solutions to efficiently transform data into AI-ready assets. As organizations grow, the variety and volume of data they generate increase, necessitating scalable and flexible AI ingestion processes.
Businesses often struggle to adapt AI solutions designed for specific tasks to broader applications, leading to more complex data management, particularly concerning unstructured data. IBM’s approach focuses on enhancing data accuracy, governance, and compliance, empowering clients to maximize the value of their data across multiple use cases. Establishing effective data processes in AI implementation requires time and a clear vision of potential evolution.
IBM offers a variety of tools to facilitate AI workloads in regulated industries, making them a valuable partner for enterprises looking to unlock the full potential of their data.