Digital technology is further embedded in mission-critical services and customer-facing channels, which has led to increased observability as strategic business requirements. Organizations cannot rely on manually monitoring the performance and availability of applications and infrastructure. It requires a real-time, end-to-end view of the operation of your IT system and a direct impact on business outcomes such as customer experience, conversions, and revenue. This is only possible with the observability of AI-powered businesses.
By integrating technical and business metrics and turning them into actionable insights using AI, organizations can turn business observability from “good” to key enablers for excellence, customer satisfaction and growth. Business observability and AI capabilities allow organizations to unlock the full value of cloud investment, identify risks before escalating, and create a seamless user experience.
Challenges faced by leaders
Many business leaders understand the importance of digital transformation, but lack the complete visibility needed to connect IT performance to business outcomes. The amount of telemetry data generated by modern digital services and its users exceeds the human ability to analyze and act. This disconnect can lead to unexpected downtime, reduced customer satisfaction, compliance risks, and loss of revenue. for example:
- System failures can disrupt critical services and create ripple effects across your business.
- Siloed teams have no access to shared insights, which can slow down problems and negatively affect collaboration.
- Insufficient data analysis can lead to missed opportunities to optimize system performance, reduce costs and identify emerging risks.
- The lack of data context means that an organization has no idea about the data, impeding its ability to use it for effective decision-making.
For leaders, critical challenges are clear. How do you translate tsunamis of telemetry data into actionable insights that can help you manage highly complex systems? The answer lies in leveraging the observability of AI-driven businesses to promote effective collaboration across the organization.
Observability is a strategic essential item
Observability is not just an IT feature, but a strategic need for modern organizations that aim to thrive in a cloud-driven world.
Cloud-based applications offer unparalleled scalability and flexibility, but also introduce challenges such as increased system interdependencies, short-lived containers, and unpredictable behavior in distributed environments. Traditional IT surveillance approaches are inappropriate for this modern cloud-native world.
Previously, organizations relied on siloed monitoring tools dedicated to specific layers of the technology stack, such as networks, applications, databases, and infrastructure. These tools were time-consuming and relied on manual configuration and data correlation from a variety of tools to unlock practical insights. As such, they do not end up providing comprehensive, full-stack context and real-time insights where teams need to effectively predict and prevent issues.
The observability of AI-powered businesses addresses this gap by collecting and analyzing data from multiple sources. Combine customer behavior, IT performance, and business intelligence in a single, cohesive view in real-time. These approaches, such as optimizing customer experiences, strengthening security measures, meeting regulatory compliance, and maximizing operational efficiency, enable organizations to actively tackle challenges before they impact the final line.
Unlock business possibilities with observability
Recognizing that business observability is a game changer for modern digital companies leads to discovering actionable insights into both technical performance and business impact. By leveraging these insights, organizations can unlock business possibilities in a variety of ways, including:
- Proactive problem detection and risk mitigation involves early identification of IT issues to prevent business disruption by highlighting vulnerabilities, identifying the root cause, and using real-time monitoring that provides solutions. For example, financial institutions reduced route cause analysis time by 40% and reduced security controls by 20% by using observability to distinguish potential risks and reduce vulnerability risk and enhanced compliance.
- Enhanced decision making through data context combines technical metrics with business goals to enable informed decisions. Correlate performance metrics with factors such as revenue, customer journeys, and resource allocations, allowing organizations to ensure that strategic outcomes are seamlessly aligned with operational goals.
- Optimized customer and user experiences are achieved by analyzing customer behaviors to improve user journeys and eliminating friction points. For example, the e-commerce platform has leveraged real-time observability data to improve the customer experience and provided recommendations for customized products that lead to a 35% increase in customer retention.
- Operational efficiency and sustainability are enhanced through end-to-end system visibility, helping to optimize resources and reduce costs. This includes enabling predictive self-enhancement and rights for cloud resources, as well as contributing to sustainability goals. By adding AI-driven insights to the equation, organizations can take advantage of accurate answers that can embrace automation, thus radically transforming application delivery and cloud operational processes. For example, global logistics companies implemented observability to manage their vast supply chain networks, improving operational efficiency by 30%. Fast response times to logistics challenges increased coordination between business units.
- Agility and innovation are achieved through streamlining DevOps and product cycles that accelerate time to market. For example, organizations can speed up app delivery when eliminating bottlenecks through automation and orchestration of their DevOps pipeline.
- Strategic compliance and security focuses on continuous transaction tracking, anomaly detection and mitigating cybersecurity risks.
Deloitte and Dynatras for Better Business
Deloitte and Dynatrace partner to revolutionize organizational observability by addressing the complexities of hybrid and multi-cloud environments. Through a Dynatrace AI-powered observability platform that integrates deep surveillance, AI-driven operations (AIOPS), and security automation, organizations gain accurate insights and efficient infrastructure management. Deloitte complements this with extensive cloud expertise and develops a custom observability framework that drives resilience, reliability and innovation.
The value of this partnership lies at the intersection of adjacent platforms, creating a solution that is more refined than the sum of that portion. As a leading global system integrator, Deloitte is based on Dynatrace capabilities by seamlessly integrating with complementary technologies such as ServiceNow, AWS, and Red Hat. This synergy allows organizations to unlock unparalleled efficiency and increase their ability to navigate and manage modern cloud-native ecosystems.
The collaboration also uses predictive AI, business analytics, and automation to optimize system performance, meet technical standards such as ISO 27001 and DORA, and cleverly manages the vast amount of data generated by cloud-native systems to reduce carbon emissions. Deloitte and Dynatrace simplify complexity, empower organizations with AI-driven insights, and transform data into actionable strategies to achieve sustainable success.
Download the Deloitte whitepaper “Observability Solutions: Facilitate the Observability of Action” and download a comprehensive guide on how to accelerate digital transformation and achieve better business outcomes through AI-powered observability.
