Harnessing 99% of Your Data: Navigating the Landscape of AI and Analytics
In an era where data reigns supreme, the ability to harness it effectively has become a pivotal element for businesses striving for success. Companies, regardless of size, have long understood that the data they collect can significantly enhance user experiences and inform strategic decision-making based on solid evidence.
With the growing accessibility of artificial intelligence (AI), the potential value of this data has escalated dramatically. However, it's essential to note that successfully leveraging AI necessitates a considerable amount of effort. This includes data collection, curation, and preprocessing. Additionally, enterprises must carefully consider crucial factors such as data governance, privacy, anonymization, compliance with regulations, and security right from the get-go.
To shed light on these complexities, I spoke with Henrique Lemes, the Americas Data Platform Leader at IBM. Our conversation delved into the challenges businesses face when adopting AI for different use cases, focusing on the essence of data and the diverse types that enable effective AI applications.
Henrique pointed out that it's misleading to lump all enterprise information under “data.” In reality, organizations wrestle with a fragmented landscape of various data types, often grappling with inconsistencies in quality—especially when contrasting structured and unstructured sources. You see, structured data is neatly organized in a standardized format, making it easy for software systems to process and analyze.
On the flip side, unstructured data defies a predefined format or organizational model, presenting challenges for processing and analysis. This category includes everything from emails and social media posts to videos, images, and documents. Despite its lack of organization, unstructured data is a goldmine of insights. When properly managed through advanced analytics and AI, it can fuel innovation and guide strategic business decisions.
Henrique stated a fascinating fact: “Currently, less than 1% of enterprise data is utilized by generative AI, and over 90% of that is unstructured.” This statistic vividly illustrates how unprocessed data can directly impact trust and quality within an organization. Decision-makers need to trust that the information they use is comprehensive, reliable, and ethically sourced. Yet, studies show that less than half of available data is harnessed for AI, with unstructured data often overlooked due to its complex processing requirements.
To facilitate better decision-making that draws from a broader dataset, businesses need to transform the trickle of data into a torrent. Automated ingestion is key here, according to Henrique, though governance rules and data policies must also apply to both unstructured and structured data. It’s like finding the right balance between speed and security.
Henrique elaborated on three processes essential for enterprises to maximize their data's inherent value: first, ingestion at scale, which emphasizes automation; second, proper curation and data governance; and third, ensuring availability for generative AI applications. Impressively, applying these strategies can result in over 40% ROI compared to conventional use cases.
IBM's comprehensive strategy integrates a deep understanding of an enterprise's AI journey with cutting-edge software solutions and extensive domain knowledge. This synergy enables companies to convert both structured and unstructured data into AI-ready assets while adhering to existing governance and compliance standards. Essentially, it's about bringing together the right people, processes, and tools. Although it's not inherently simple, they streamline this by coordinating essential resources.
As businesses expand, so too does the variety and volume of their data. Consequently, the AI data ingestion process must exhibit both scalability and flexibility. Henrique identified a common obstacle: many companies develop AI solutions tailored to specific tasks. When attempting to broaden their scope, they often encounter complications, and unstructured data management becomes critical. This reality underscores an increased necessity for effective data governance.
IBM's method involves a thorough understanding of each client's AI journey, laying out a clear roadmap to achieve ROI. “We prioritize data accuracy, whether structured or unstructured, along with ingestion, lineage, governance, regulatory compliance, and the right level of monitoring. These capabilities empower our clients to scale effectively across various use cases and fully leverage their data’s potential,” he explained.
As with any valuable technological implementation, establishing the right processes, selecting the appropriate tools, and having a clear vision of how a data solution should evolve takes time. IBM presents a variety of options and tools to support AI workloads, even in the most regulated sectors.
To discover more about enabling data pipelines for AI that can drive significant business results and quick ROI, explore here.
