Causal AI can accurately pinpoint why an event occurred, but the effectiveness of AI depends on high-quality data. Discover common data quality challenges, how to improve data quality, and more.
Traditional analytics and AI systems rely on statistical models to correlate events with possible causes. While this approach can be effective if the model is trained with a large amount of data, even in the best-case scenarios, it amounts to an informed guess, rather than a certainty. That’s where causal AI can help.
Causal AI is a different approach that goes beyond event correlations to understand the underlying reasons for trends and patterns. It uses fault-tree analysis to identify the component events that cause outcomes at a higher level. Causal AI is particularly effective in observability. It removes much of the guesswork of untangling complex system issues and establishes with certainty why a problem occurred.
Causal AI applies a deterministic approach to anomaly detection and root-cause analysis that yields precise, continuous, and actionable insights in real time. But to be successful, data quality is critical. High-quality data creates the foundation for credible insights organizations can use to make sound decisions.
In what follows, we’ll discuss how to assess data quality, common data quality challenges, how to overcome them, and more.
Key considerations for assessing data quality
Assessing data quality requires organizations to consider several key factors, including the following:
Accuracy. Teams need to ensure the data is accurate and correctly represents real-world scenarios. Additionally, it’s important to consider all variables.
Completeness. Is any information missing from the data set? Omissions can create wrong conclusions and contribute to bias.
Consistency. Ensure there are no discrepancies in the data. Contradictory or inconsistent data confuses AI models and increases the risk of errors.
Timeliness. The data should be up-to-date and relevant to the current context. Timeliness is a critical factor in AI for IT operations (AIOps). Because IT systems change often, AI models trained only on historical data struggle to diagnose novel events. Causal AI requires real-time updates to the training model.
Relevancy. The data needs to be appropriate for the questions asked. In AIOps, this means providing the model with the full range of logs, events, metrics, and traces needed to understand the inner workings of a complex system.
How can organizations improve data quality?
Improving data quality is a strategic process that involves all organizational members who create and use data. It starts with implementing data governance practices, which set standards and policies for data use and management in areas such as quality, security, compliance, storage, stewardship, and integration.
Data stewardship is an increasingly important factor in data quality. It ensures the data people and departments generate and maintain is clean, consistent, and complete. Data mesh is a popular new concept that encourages the people who create data to treat it as a product to be managed like any other product. But it suffers from limitations such as multiple copies of data. High-quality operational data in a central data lakehouse that is available for instant analytics is often teams’ preferred way to get consistent and accurate answers and insights.
Data-cleaning tools and methods are needed to identify and fix errors. Additionally, teams should perform continuous audits to evaluate data against benchmarks and implement best practices for ensuring data quality.
Common data quality challenges to consider
Organizations may encounter numerous barriers to ensuring data quality. For starters, the sheer amount of data can make management daunting. Modern, cloud-native architectures have many moving parts, and identifying them all is a daunting task with human effort alone. Modern observability solutions that automatically and instantly detect all IT assets in an environment — applications, containers, services, processes, and infrastructure — can save time.
Fragmented and siloed data storage can create inconsistencies and redundancies. Stakeholders need to put aside ownership issues and agree to share information about the systems they oversee, including success factors and critical metrics.
Another common impediment is manual data tagging and handling, an error-prone process that teams should minimize. Observability solutions automate much of the task of identifying the variables that go into application performance and availability. Human involvement should be limited to verifying the features or attributes machine learning algorithms use to make predictions or decisions.
Improving data quality management using causal AI
Causal AI can be a powerful tool for improving systems management, observability, and troubleshooting. It can highlight inconsistencies or outliers in data sets that indicate anomalies and pinpoint the root causes. It also enables an AIOps approach with proactive visibility that helps companies improve operational efficiency and reduce false-positive alerts by 95%, according to a Forrester Consulting report.
Causal AI informs better data governance policies by providing insight into how to improve data quality. It improves time management and event prioritization by helping developers, administrators, and site reliability engineers identify the alerts that matter most. Identifying issues before an application or service outage occurs can reduce costs. IT teams can focus on strategic initiatives to drive business success, rather than firefighting — and it accelerates digital transformation through automation and self-maintaining systems.
Unleash the power of causal AI
Dynatrace provides an AI-powered, automated IT performance monitoring platform with advanced observability and analytics capabilities. It enables real-time health and performance tracking, intelligent anomaly detection, data quality controls, and automated issue resolution.
By accurately assessing, managing, and continuously improving data quality, organizations can use causal AI to its full potential. Platforms such as Dynatrace help ensure that data quality rises to the standard required for effective causal analysis.