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The Role of Data Quality in AIOps Ensuring Accurate Predictions

    AIOps (artificial intelligence for IT operations) has enabled IT departments to tackle the ever-growing complexity and pace of the digital space they operate within. These systems leverage artificial intelligence (through machine learning algorithms) to elevate their operational capabilities, mainly by learning how to make predictions about their operations and acting on those predictions.


    Among many others, the main benefit of AIOps is that it can detect anomalies and make predictions faster and more accurately than its human counterpart in the IT room… as long as the data it receives is high quality. How could the quality of data, the data collection processes, and data analytics affect the AIOps application performance?

    What is Data Quality and Why Does it Matter for AIOps Tools?

    It is easy to assume that the amount of data is the most crucial factor when developing and training an AI program. However, what is most important is data quality because it is the foundation upon which the machine learning systems will learn to make their predictions. Building a predictive system on sub-par data would be akin to building a superstructure on sand. Not a great idea.

    According to a recent study by NTT “Potential hurdles include data availability and quality, algorithm selection, model training and validation, deployment and integration with existing systems, and maintenance and support. Additionally, organizations have to ensure their AI models are designed ethically and securely to prevent bias or the misuse of data.”

    But what is quality data?

    • Complete: incomplete data sets will inevitably make the AI system miss correlations and patterns that would otherwise be noticeable and predictable.
    • Accurate: if data is inaccurate or misleading, biases will develop, making the predictive capabilities of an AIOps system essentially useless.
    • Relevant: for predictive capabilities to be helpful, the data that is ingested by this system must be relevant to the task at hand and the objectives of the system.
    • Timely: data “freshness” matters for AIOps systems. Environments change too quickly to rely on old and outdated data.
    • Consistent: the structure and format of the data should be consistent to avoid unnecessary confusion and misinterpretation.

    How Does High Quality Data Affect Accurate Predictions?

    When machine learning algorithms are fed accurate data, they can consistently make accurate predictions in the target environment or system. High-quality data begins to make an impact at the beginning of the collection and analysis stage and makes a remarkable difference by the end of the entire process. How does this process work?

    • Data Collection and Analysis: this is where the information is gathered from multiple systems, sensors and other information sources and analyzed in real-time.
    • Anomaly Detection: with this influx of big data, the analyses will determine if any anomalies need to be dealt with. If the data is of poor quality, it is more difficult to properly identify anomalies.
    • Root Cause Analysis: AIOps will determine the root cause of any anomaly it finds, quickly discovering where it came from.
    • Predictions:with the available data, the AI system will make predictions about whether action must be taken and if there are likely to be more anomalies based on the information gathered.
    • Notification/ Resolution: if the AI predicts more anomalies or events in the future, it can either notify a human IT operative or work through a solution automatically, resolving the problem before the need to include a person from the IT department.

    Who Benefits When AIOps Solutions Use Data to Make Predictions?

    All areas of business operations benefit from accurate AIOps predictions. IT departments benefit from a more balanced workload. Customers that use applications that have AIOps systems experience fewer problems, leading to higher customer satisfaction and improved customer experiences. Efficiency increases, saving time and money.

    High-quality data makes a difference, and it is worth the effort to ensure that your AIOps system is getting the highest quality data possible.

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