As enterprises across India accelerate their digital transformation journeys, observability is rapidly evolving from a backend IT function into a strategic business imperative. From improving customer experiences and strengthening cyber resilience to enabling AI-driven innovation and operational agility, modern observability platforms are now playing a central role in enterprise decision-making. In this exclusive interaction, Rajeev Ranjan, Editor, Digital Terminal, speaks with Prashant Chaudhary, Area Vice President, Splunk India about how Indian organizations are leveraging observability to drive business outcomes, strengthen collaboration between security and operations teams, embrace AI-powered resilience, and prepare for the next era of OpenTelemetry and observability-as-code.
Rajeev: Observability has moved from IT dashboards to boardroom discussions. How are Indian enterprises using observability today to directly influence business outcomes like revenue growth, customer experience, and innovation?
Prashant: Observability has fundamentally transformed from a technical monitoring function into a strategic business catalyst that directly influences the bottom line. Globally, according to a latest Splunk report, 74% of organisations prioritise monitoring critical business processes, fundamentally shifting from asking "What's broken?" to "How can we use data to drive growth?"
The report also suggests that organisations are recognising how observability positively influences revenue and significantly impacts product roadmaps. Observability teams are now embedded in boardroom discussions, bringing real-time data to inform customer engagement strategies and broader business decisions.
PUMA provides a useful illustration of how organisations can leverage observability to strengthen business performance and operational resilience. PUMA progressed from basic monitoring, which was limited to system availability indicators, to an observability-driven approach powered by Splunk Cloud Platform alongside an AIOPS monitoring solution. This shift enabled the organisation to move beyond reactive troubleshooting and adopt proactive detection. Teams could identify issues that traditional uptime metrics could not reveal, including transaction failures, payment disruptions, and inventory system anomalies.
By gaining deeper operational visibility, PUMA was able to detect and resolve order-related problems before they escalated into customer-impacting incidents. They were able to boost revenue per hour by $10,000. The PUMA India team were also able to recognise patterns in failed payments, isolate contributing factors, and support timely remediation.
Rajeev: The report shows 81% of Indian tech teams actively share observability data with security teams, higher than the global average. What does this say about India’s maturity in cross-functional collaboration, and how is this improving digital resilience?
Prashant: This 81% figure (compared to 74% globally) is particularly significant and reveals India's advanced organisational maturity in breaking down traditional silos. Even more important, 74% of respondents in India can accurately trace application and infrastructure performance issues back to security root causes, substantially higher than the 65% global average.
India's collaborative edge originates from several factors including growing complexity of digital services in sectors like banking, telecom, and e-commerce that demand integrated approaches as well as Indian IT organisations historically being early adopters of integrated service delivery models. There is an emphasis on delivering high-quality services at scale which requires cross-functional visibility.
The benefits of collaboration are tangible and has a direct impact on digital resilience as well. When observability and security teams work in parallel with shared context and intelligence rather than in isolation, they can quickly distinguish disruptions caused by an application error, a performance issue or a security incident. With Splunk's unified security and observability platform, India could emerge as a "fusion centre" model where observability (NOC) and security (SOC) teams become a unified operations, strengthening digital resilience at scale.
Rajeev: With 82% of Indian respondents saying AI allows teams to focus more on innovation than maintenance, how is AI-powered observability accelerating India’s journey toward AI-ready enterprises?
Prashant: AI-powered observability is helping Indian organisations fundamentally rebalance how engineering time is spent. Indian teams are leveraging AI across multiple dimensions. With more than 80% of global organisations saying AI enables teams to focus more on innovation than maintenance, the impact is already visible at an operational level.
By automating repetitive analysis, improving signal quality, and accelerating root cause identification, AI allows teams to move faster with fewer manual dependencies. This is particularly important in India, where scale and transaction volumes make traditional approaches unsustainable.
At the same time, fewer teams report spending excessive time responding to alerts compared to global peers, indicating early efficiency gains. Together, these shifts are creating the operational maturity required for AI readiness not just adopting AI tools but supporting them reliably at scale.
Rajeev: Alert quality strongly impacts observability ROI for 58% of Indian organisations, significantly above the global benchmark. How should enterprises rethink alerting strategies to reduce noise and drive faster, smarter decisions?
Prashant: Alert quality has an outsized influence on observability outcomes, and the data signals why enterprises must rethink traditional alerting models. Indian organisations report that alerts play a stronger role in shaping both operational and security decisions, while more than half acknowledged that false alerts negatively affect team morale. This points to a known challenge — the issue is not just around alert volume alone, but the downstream impact of noisy, low-context detections that impact the speed of decision-making and the team’s efficiency as whole.
The answer lies in an integrated approach that addresses the full lifecycle of a threat signal: detection, investigation, and response. Splunk Enterprise Security is built around unifying TDIR (Threat Detection, Investigation, and Response), within a single AI-powered SecOps platform. Rather than treating these as sequential, siloed steps that different teams handle at different speeds, Splunk Enterprise Security brings them together so that a detected signal immediately feeds investigation context, and that context directly informs the response action. For Indian enterprises, where alerts already carry significant weight in shaping security decisions, this end-to-end coherence is particularly valuable, it means fewer handoffs, less information loss, and faster resolution in this agentic AI era.
Within this framework, automation becomes the strategic lever that makes alerting genuinely intelligent. Rather than relying solely on human-driven triage, enterprises can use automation to transform alerts from interruptions into orchestrated workflows. This is where Splunk SOAR becomes strategically important. Security and operations teams are increasingly constrained by alert overload, tool sprawl, and limited analyst bandwidth. Splunk SOAR addresses this by automating repetitive, low-risk tasks such as alert triage and data enrichment, enabling teams to respond faster and more consistently while preserving human oversight for high-value decisions.
For Indian enterprises, this automation-first approach delivers multiple advantages. Automated playbooks can validate, enrich, and prioritise alerts before analysts engage, reducing cognitive load and alert fatigue. Enriched alerts carrying contextual intelligence allow teams to move directly toward resolution instead of investigation-heavy workflows. Consistent, machine-driven responses minimise variability and prevent the risky practice of ignoring or suppressing alerts.
Automation connects workflows across security and observability functions, a critical capability in India where alerts heavily influence security actions.
Rajeev: Looking ahead, how will emerging approaches like OpenTelemetry and observability-as-code redefine enterprise observability, and what opportunities do you see for Indian organisations to lead this next phase globally?
Prashant: OpenTelemetry (OTel) has become the strategic foundation for modern observability. Organisations adopting these standard measurable improvements across revenue growth operating margins and brand perception. By standardising how traces, metrics, and logs are generated and enriched with metadata, OTel enables future-proofed architectures that scale with increasingly distributed and cloud-native systems.
OpenTelemetry’s model is particularly relevant for India’s heterogeneous technology ecosystem, where enterprises often operate diverse stacks spanning legacy systems, cloud platforms, and modern microservices. Standardised telemetry reduces fragmentation, lowers switching costs, and enables innovation without architectural lock-in. This is an advantage for fast-growing digital businesses.
Complementing this is the shift toward ‘Observability-as-code'— moving observability from a post-deployment activity to an embedded component of the development lifecycle. This ensures consistency, version control, and automation across environments. Observability becomes programmable, repeatable, and inherently aligned with DevOps practices rather than manually configured afterthoughts.
For Indian organisations, the opportunity is especially significant. India already has distinctive strengths that align with next-generation practices. The country’s observability teams show higher-than-global (81% vs 74%) collaboration with security functions, strong AI adoption momentum, and an operational culture accustomed to managing extreme scale and system complexity. collaboration with security functions, strong AI adoption momentum, and an operational culture accustomed to managing extreme scale and system complexity.
Similarly, observability-as-code fits naturally with India’s deep engineering-led development culture. As Indian enterprises accelerate cloud-native and AI-driven transformations, the ability to codify observability policies, automate instrumentation, and scale governance becomes a competitive differentiator. Teams move faster not by reacting to incidents, but by engineering resilience directly into software delivery pipelines.
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