The Vital Role of High-Quality Data in Training AI Models for IT 

Explore the Enterprise Strategy Group infographic on the role of high-quality data for training AI for IT

For as long as most of us can remember — whether you’re an L1 technician working in the IT trenches or an end-user who’s having a bad day with your laptop or a new application that’s not working well — IT support has been a reactive thing. Living in a constant state of reaction is not pleasant for anyone, in any realm. Remember the printer scene in “Office Space”? Not good. 

All the while that IT is constantly responding to fire drills, there are missed opportunities for IT technicians to focus that invaluable time on more strategic projects and IT transformation projects such as a Windows 11 rollout. Not to mention the impact a poor digital employee experience has on employee satisfaction and productivity.  

How can you shift your IT team toward a proactive IT posture — and even a predictive one? Through AI purpose-built for IT, Lakeside is leading the way for what we call AI that speaks IT™. We’re not just jumping on the AI bandwagon for the sake of AI. Instead, we are using AI specifically trained to simplify IT. IT teams can use the AI engine embedded in the Lakeside SysTrack platform to keep endpoints working flawlessly thanks to predictive analytics used for anomaly detection. These endpoints could include enterprise laptops, retail point-of-sale systems, hospital digital carts, airport displays, and self-serve kiosks. 

As with any AI model, their efficacy hinges significantly on the quality of data they are trained on. Feeding an AI model with well-structured, high-quality, and well-labeled data is crucial for several reasons, the most important being AI accuracy and efficacy in the model training. Both facets are essential for building trust in the AI model. 

The Importance of High-Quality Data in AI Models for IT

1. Accuracy and Performance 

High-quality data directly affects the accuracy of AI models. In the IT sector, where precision is critical, an AI model’s ability to correctly identify anomalies, predict system failures, or optimize resource allocation is paramount. Well-structured and clean data ensure that the model learns the correct patterns and makes fewer mistakes.  

Since Lakeside has been in the IT data intelligence business for 27 years, we can deliver next-level AI capabilities that are accurate and, by extension, trustworthy. In a recent webinar with our CTO Elise Carmichael, Enterprise Strategy Group Senior Analyst Gabe Knuth noted that, “Lakeside has such a history of collecting a lot of data and using it in interesting ways. Machine learning is something Lakeside’s been doing for the entirety of Lakeside’s existence.”  

Indeed, Lakeside’s robust data sets are what makes our AI for IT stand out, because in order to get to the level of trust and accuracy needed for IT use cases, you must have a lot of high-quality data. In fact, 31% of IT leaders surveyed by Enterprise Strategy Group cite “limited availability of quality data” as the top challenge their organization has encountered while implementing AI. This lack of high-quality data is why Lakeside Founder Mike Schumacher has said that not every company is (or will become) an AI company. 

2. Efficiency in Learning 

AI models, especially those based on deep learning, require substantial computational resources and time for training. High-quality data reduces the noise and irrelevant information that the model has to sift through, thereby accelerating the model’s learning process as it becomes more and more finetuned over time.  

As Gabe Knuth explains in the white paper, “The Essential Role of Data and Data Quality in IT-related AI Model Training,” “Data is food for AI, and what’s true for humans is also true for AI: You are what you eat. Or, in this case: The better the data, the better the AI.” Again, data will make or break the pragmatic value of the AI. 

The Role of Well-Labeled Data in AI Models for IT 

1. Improving Model Interpretability 

Well-labeled data helps in making AI models more interpretable. In IT, where understanding the decision-making process of an AI system is often necessary for debugging and compliance purposes, having clearly labeled data is crucial. Labels provide a clear link between input data and the expected outcome, making it easier to trace the model’s learning path and identify any biases or errors.  

2. Facilitating Supervised Learning 

Supervised learning, a common approach in AI, relies heavily on labeled data. The labels act as a guide, helping the model understand the relationship between inputs and outputs. In IT applications, such as automated troubleshooting or predictive maintenance, the accuracy of the labels directly influences the effectiveness of the AI system. Incorrect or ambiguous labels can lead to poor model performance and unreliable predictions. 

3. Enhancing Data Annotation Processes 

High-quality labeled data is also vital for improving the data annotation processes. As AI models are often used to assist in the annotation process, a strong foundation of well-labeled data can bootstrap the model’s performance. In IT, this can mean better automated log analysis, enhanced monitoring systems, and more accurate tagging of network traffic or user actions. 

Trustworthy AI That Speaks IT 

Feeding AI models with well-structured, high-quality, and well-labeled data is not just a best practice; it is a necessity for achieving reliable and effective AI models that are purpose-built for IT.  As the IT landscape continues to evolve (not to mention the introduction of AI regulations), ensuring that AI systems are built on a foundation of robust, high-quality, and well-structured data will be key to unlocking their full potential for simplifying IT. AI that speaks IT must be cloaked in accuracy and trust.  

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