Header background

What is predictive AI? How this data-driven technique gives foresight to IT teams

Predictive AI uses statistical algorithms and other advanced machine learning techniques to anticipate what might happen next in a system. By analyzing patterns and trends, predictive analytics enables teams to take proactive actions to prevent problems or capitalize on opportunity.

Predictive AI is an important technology that busy technology and operations teams can use to ensure that applications and digital systems work seamlessly and securely.

Because IT teams handle complex infrastructure to maintain service availability, they need to respond swiftly to incidents as they arise. But when these teams work in largely manual ways, they can miss the first signs of incidents as they’re building. As a result, incidents can build into outages, and teams have to respond to crises instead of innovating on strategic projects that have more value.

By integrating predictive artificial intelligence (AI) into team workflows, they can more easily meet service-level objectives, collaborate effectively, and boost productivity.

What is predictive AI?

Predictive AI is a type of machine learning (ML) that uses advanced algorithms to analyze large datasets to identify hidden patterns and relationships between variables. In data analytics, predictive AI trains ML models to learn from historical data and make predictions about future events, scenarios, or outcomes based on patterns from that data.

Organizations in finance, health care, marketing, manufacturing, and other industries widely use predictive AI. It enables organizations to make better data-driven decisions, optimize processes, and anticipate future trends. How accurate those predictions are based on historical data depends on the training data’s quality and relevance. Predictions’ accuracy also depends on the robustness of the classification and ML models, such as decision trees, neural networks, or regression algorithms.

By analyzing patterns and trends, predictive analytics helps identify potential issues or opportunities, enabling proactive actions to prevent problems or capitalize on advantageous situations.

When teams combine predictive AI with a data lakehouse such as Dynatrace Grail, it can deliver even greater value. With access to complete data sets in context, predictive AI can automatically provide prescriptive insights using data from the digital user experience layer to the infrastructure layer. This access provides full data context using supporting data, such as relationships, dependencies, and usability data within entities and events.

While investigative techniques such as root-cause analysis are essential for teams striving to understand issues that have already occurred, predictive AI techniques such as forecasting and anomaly prediction help teams preempt issues. With the advances in causal AI (that is, AI that can explain cause and effect by identifying root-cause issues), teams want to take it to the next level and combine it with predictive AI to create a seamless foresight-to-hindsight continuum of data-driven answers and prescriptive insights.

The importance of predictive AI for ITOps, DevSecOps, and SRE teams

  1. Early detection of anomalies. Predictive AI empowers site reliability engineers (SREs) and DevOps engineers to detect anomalies and irregular patterns in their systems long before they escalate into critical incidents. By identifying subtle deviations in system behavior, engineers can take preemptive measures to avert potential downtime, performance issues, or security threats.
  2. Proactive resource allocation. Through predictive analytics, SREs and DevOps engineers can accurately forecast resource needs based on historical data. This enables efficient resource allocation, avoiding unnecessary expenses and ensuring optimal performance.
  3. Capacity planning. Understanding future capacity requirements is crucial for maintaining system stability. Predictive AI assists engineers in predicting demand fluctuations and adjusting resource capacities accordingly, ensuring seamless user experiences.
  4. Enhanced incident response. Predictive analytics can anticipate potential failures and security breaches. SREs and DevOps engineers can implement targeted remediation strategies and prioritize incident response efforts to minimize the impact on systems and users.
  5. Continuous improvement. By analyzing past incidents and performance metrics, predictive analytics helps SREs and DevOps engineers identify areas for improvement. This data-driven approach fosters continuous refinement of processes and systems.

Predictive AI-based capacity management and automation

Proactive capacity management is essential for avoiding outages and ensuring that an organization’s applications and services are always available. Operators need to closely observe business-critical resource capacities such as storage, CPU, and memory to avoid outages that are driven by resource shortages. However, traditional capacity management approaches are often reactive and time-consuming. Using Dynatrace Grail and Davis AI, predictive capacity management is straightforward:

  • use Notebooks to explore important capacity indicators;
  • create workflows to trigger forecast reporting at regular intervals; and
  • use Davis AI for Workflows to automate the prediction and remediation of future capacity demands.

Benefits of predictive AI for capacity management

Predictive capacity management is a powerful tool that can help improve the availability and performance of applications and services. By using Dynatrace Grail and Davis AI, you can gain the insights you need to make proactive decisions about capacity planning and gain the following additional benefits:

  • Increased visibility into future capacity demands. Predictive capacity management can help you to anticipate what your future capacity demands will likely be. This provides organizations with the ability to make proactive decisions about capacity planning, such as adding additional resources or scaling back resources that are not being used.
  • Improved decision making for capacity planning. With predictive capacity management, you can make more informed decisions about capacity planning. This is because you have a better understanding of your future capacity demands and the impact of those demands on applications and services.
  • Reduced costs associated with unplanned capacity increases. Unplanned capacity increases are costly. Organizations may need to purchase additional resources or pay for overtime. Predictive capacity management can reduce these costs by enabling organizations to plan for future capacity demands.
  • Increased customer satisfaction. When your applications and services are available and performing well, your customers are happy. Predictive capacity management can help you to improve customer satisfaction by reducing the number of outages and performance problems.

Predictive AI helps teams avoid costly problems

This is just one example of predictive AI in action. But for ITOps, DevSecOps, and SRE teams, predictive AI presents numerous use cases for gaining foresight into issues and pre-emptively addressing them before they escalate into costly problems. They see improved efficiency, reduced risks of security breaches, and better compliance with industry regulations.

Read this blog to discover how organizations can use AI observability to optimize AI costs.