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We have observed significant surge in SaaS adoption even during economic slowdown: Naveen Oli, Regional Sales Lead at Rakuten SixthSense

In an exclusive interview with Express Computer, Naveen Oli - Regional Sales Lead at Rakuten SixthSense talks about the complexities of managing tech infrastructure, future of observability and SaaS adoption.

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Edited excerpts:

Naveen Oli, Regional Sales Lead, Rakuten SixthSense

While the business world is deeply technical, can you explain what observability really means?

As a corporate leader, it’s important to understand the extreme degree of complexity behind managing digital infrastructure and workloads in today’s world. In simple terms, whenever you use digital offerings, whether it’s credit card transactions or streaming; there are thousands of servers, microservices and applications working together to give you a complete experience. At Rakuten SixthSense, we deliver full-stack observability that instruments all of the systems, data and machines to capture deep real-time visibility of applications. This helps engineering, operations, product, and business teams understand how their digital assets are behaving and why, whether they’re making money, and if customers are satisfied. Critically, it helps save costs and increase operational efficiency by eliminating impact from high-risk threats since teams can proactively solve anomalies before they devolve into pain-points.

Do you expect the complexity of managing tech infrastructure and workloads to increase or decrease over time?

As organizations continue to adopt new technologies and expand their digital footprint, the number of systems, applications, and services that need to be managed will continue to grow. Additionally, with hybrid and multi-cloud environments and different public and private cloud providers, increased complexity is anticipated. This is not a bad thing in itself as it allows more components to be combined innovatively, resulting in even more powerful applications delivering better value. However, difficulties increase too as outfall from underperformance can be detrimental to revenue growth. That’ why our solution is designed to help customers navigate the complexities of today and tomorrow with comprehensive unified monitoring, smart alerting, elimination of false positives and predictive analytics. Ultimately, our focus is on helping customers understand what is happening across digital assets and how to modify and fix issues when they arise, regardless of system complexity or macroenvironmental uncertainty.

How has the global recession affected customer behaviour towards SaaS adoption?

We have seen a surge in SaaS adoption even during stress with consumer trends highly predictable. Large enterprises that have established infrastructure and steady financial are pushing adoption as it promotes resource efficiency. Even companies operating cyclically and experiencing drastic demand fluctuations are using SaaS due to its agility in responding to unexpected changes. Overall, we have seen more companies and CTOs prioritising investment in digital transformation and shifting from monitoring to pre-emptive observability.

How do you ensure that monitoring and observability practices are aligned with business goals?

To ensure that monitoring and observability practices are aligned with business goals, it’s important to establish clear metrics and KPIs that are directly tied to the success of the organization. These metrics should be regularly tracked and analyzed to identify areas for improvement and ensure efforts are having the desired impact. In addition, it’s important to involve stakeholders from across the organization to seal any gaps from the bottom-up.

What lies in the future of the observability space?

In the future, it’s likely that observability will continue to evolve to meet the needs of developers and operations teams as they face more challenging problems. Deep observability will become normative major advances in machine learning and artificial intelligence on the horizon, enabling observability data to pre-empt issues before they occur.  For example, machine learning models can be trained to identify patterns in system data that are associated with specific types of problems. These models can then be used to identify potential issues and trigger automated responses to mitigate them. This will help companies improve system reliability and reduce downtime, which can have a significant impact on profitability.

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