"Garbage in, garbage out" is a statement that particularly holds true in the exploding field of artificial intelligence (AI). As organizations integrate AI to drive innovation, support business processes, and enhance decision-making, it is crucial to consider that the AI’s reliability is influenced by the quality of data feeding and training the model.
Organizations have taken note of this relationship between high-quality data and effective AI, prompting many to reconsider approaches to data governance and analysis. According to a Gartner survey of 479 senior executives in data and analytics roles, 61 percent of organizations are reassessing their data and analytics (D&A) frameworks in response to disruptive AI technologies.
Furthermore, 38 percent of these leaders expect a complete overhaul of D&A architectures within the next 12 to 18 months to stay relevant and effective in the evolving landscape.
The quality of data is crucial throughout any AI implementation journey, particularly when actionable insights are the desired outcome. High-quality data is accurate, complete, and well-structured; originates from trustworthy sources; and is constantly updated to stay relevant. In rapidly changing environments, the lack of quality or consistency can result in poor AI outputs, leading to compromised decision-making.
The initial quality of data during model training is especially important, affecting the model's capacity to identify patterns and produce relevant, explainable recommendations. By carefully selecting and standardizing data sources, organizations can enhance their AI use cases.
For instance, when AI is used to monitor IT infrastructure performance or improve the IT help desk (in turn, improving the digital employee experience), feeding the model with specific data, such as CPU usage, uptime, network traffic, and latency, ensures more accurate and explainable outputs from the AI. The IT team can gain relevant, data-driven insights about potential endpoint failures.
Employing Machine Learning (ML), a subset of AI that builds predictive models to learn from data, this maintenance scenario enables technical support teams to gain early insights from the ML’s anomaly detections. This proactive and predictive approach helps to reduce downtime, boost operational efficiency, and support employee productivity.
Not all organizations, however, have access to reliable data for building accurate and responsible AI models. To tackle this issue and build trust in data and AI initiatives, organizations must prioritize regular data updates.
High-quality data should be error-free, come from reliable sources, and be validated for accuracy since incomplete data or inconsistent input methods can lead to dubious AI recommendations. Additionally, the repercussions of poor data quality extend to other challenges associated with AI implementations, such as increased operational costs and challenges in measuring ROI or business impact.
Alarmingly, AI processes any data it receives without assessing its quality. Therefore, advanced data structuring practices and rigorous human oversight, often referred to as "human in the loop," are essential to ensure that only the highest quality data is used. This human oversight becomes particularly crucial in proactive IT management scenarios. While ML, supported by extensive data collection, can enhance anomaly detection and predictive capabilities, it is human input that ensures the insights are explainable and pertinent.
Most enterprise IT vendors are now incorporating some level of AI into their solutions, but the quality and diversity of data used can vary significantly. Superior AI results not just from collecting data more frequently from multiple endpoints, but also from how that data is structured.
An AI specifically designed for IT operations illustrates the importance of data quality. By analyzing and categorizing performance data collected every 15 seconds from more than 10,000 endpoints using more than 1,000 sensors, ML can proficiently detect anomalies. It can predict potential future outages or IT issues proactively while simultaneously enhancing employee productivity and satisfaction.
By integrating this vast dataset into ML, IT teams can also efficiently handle large-scale queries using natural language. Examples include analyzing average Microsoft Outlook usage or identifying employees who are underutilizing costly software licenses that were deployed organization-wide without assessing individual needs. Ultimately, AI serves as a reliable co-pilot for technology teams, from C-level executives and IT support agents to systems engineers.
IT buyers should prioritize AI-driven software that not only gathers data from diverse sources but also integrates it consistently, guaranteeing strong data handling and structural integrity. The depth, breadth, history, and quality of the data are crucial factors when selecting a vendor.
As AI progresses, its success will come from having a solid foundation of high-quality data, so businesses that competently gather and manage their data will ensure AI usage can improve decision-making, increase operational efficiency, and spur innovation.
On the other hand, overlooking the quality of data can critically undermine the integrity of AI projects. Looking ahead, organizations must meticulously collect and organize their extensive data sets to fully realize the potential of AI implementations.
