Skip to main content

Digital Business Success Depends On Real-time Continuous Intelligence

Bruno Kurtic
Sumo Logic

In the near future, people who follow technology trends will look back on 2020 and ask: What was the key driver to industry-wide acceleration of digital transformation? Business growth? Business efficiency? COVID-19?

The answer will most likely be — all of the above. Granted, no one will argue that the most defining moment of 2020 was the emergence of COVID-19 and how governments and communities responded. But COVID-19 also accelerated digital transformation by restricting physical in-person interactions and pushing people to adopt digital substitutes such as online retail and online media/entertainment. Some businesses were ready and have been able to shift seamlessly and capitalize on the opportunity. Others were not and, as a result, are losing market share. Some are confronting their very viability.

In an era where digital is the product successful businesses will be those that transform enormous volumes of real-time data generated by their digital services into insights in order to improve how they run their businesses and serve their customers. The ability to do this in real-time, all the time, across multiple functional disciplines, lies at the heart of Continuous Intelligence.


From Hype to Reality

Sumo Logic recently commissioned an independent market research study to understand the industry momentum behind continuous intelligence — and the necessity for digital organizations to embrace a cloud-native, real-time continuous intelligence platform to support the speed and agility of business for faster decision-making, optimizing security, driving new innovation and delivering world-class customer experiences.

The global report included the insights from 765 professionals with cloud-migration leadership responsibilities. Some of the key findings include:

■ 88% of C-suite executives surveyed said they believe their company will benefit from continuous intelligence

■ 74% believe continuous intelligence will help drive companies’ speed and agility

■ 76% indicated they are likely to employ continuous intelligence within the next 12 months

■ 62% believe continuous intelligence is a new approach to data that many companies will need to embrace as they become more software-driven to drive revenue.

Further proof of this is in 2019, Gartner named continuous intelligence a top ten data trend that will change business, predicting that, "by 2022, more than half of major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions."

An Analytics Model for the Next Chapter of Business

The need for analytics to drive better business decisions has been around since the early days of the computer. Analytics systems have evolved as business models have evolved over the
last 100+ years. Continuous intelligence represents the analytics model for the next chapter of business: real-time software driven, digital businesses that deliver new digitally enabled products and services faster and drive better holistic customer experience. It allows organizations to more rapidly deliver reliable applications and digital services, protect against modern security threats and consistently optimize their business processes in real time. This empowers employees across all lines of business, development, IT and security teams with the data and insights needed to address the technology and collaboration challenges required for modern business.

Key Trends Driving Continuous Intelligence

The need for continuous intelligence results from the convergence of a variety of trends including:

Accelerating cloud migration - It should come as no surprise that applications are increasingly being hosted in the cloud. Fully 75% of applications are currently hosted via cloud technologies. More notably, the outlook for growth in cloud hosting is strong – fully 93% of respondent companies anticipate increasing their use of the cloud for hosting their applications. As businesses move to the cloud, data management challenges increase exponentially. Applications must scale to handle bigger workloads. Security issues must be identified and resolved in real-time across thousands of nodes, containers, serverless functions and other IaaS services running across multiple clouds. Application functionality must evolve to anticipate and address changing customer requirements.The key to success in this modern environment will be rapid data collection and analysis with insights that illuminate the path to immediate implementation.

Strategic importance of "real-time" data analytics - As businesses increasingly grapple with digital transformation, what has emerged is the widespread appreciation of the importance of "real-time" data analytics and the recognition that insights from these analytics have strategic value — across the entire organization. Research shows widespread agreement with a variety of statements depicting the importance of real-time analytics and business success.

The rise of DevSecOps - The focus on developing modern applications means that enterprises must continually strive to deliver high performance, highly scalable, always-on digital services. These services require custom "modern architectures" — an application stack with new tiers, new technologies and microservices running on cloud platforms such as AWS, Azure or Google Cloud Platform. The need to manage these modern architectures effectively has given rise to the practice of DevSecOps — the process and practice of development that makes every team member responsible for safety. DevSecOps is next generation secure development, a shift from older, reactive security models.

Conclusion

Those businesses that will be best-equipped to succeed in this era of uncertainty, will be those that are truly real-time businesses. The most successful real-time business operations are embracing continuous intelligence as a core strategy for providing the analytics and insights they need to accelerate decision-making, ensure competitive advantage and deliver engaging customer experiences for sustained success.

Bruno Kurtic is Founding VP, Strategy & Solutions, at Sumo Logic

The Latest

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

Digital Business Success Depends On Real-time Continuous Intelligence

Bruno Kurtic
Sumo Logic

In the near future, people who follow technology trends will look back on 2020 and ask: What was the key driver to industry-wide acceleration of digital transformation? Business growth? Business efficiency? COVID-19?

The answer will most likely be — all of the above. Granted, no one will argue that the most defining moment of 2020 was the emergence of COVID-19 and how governments and communities responded. But COVID-19 also accelerated digital transformation by restricting physical in-person interactions and pushing people to adopt digital substitutes such as online retail and online media/entertainment. Some businesses were ready and have been able to shift seamlessly and capitalize on the opportunity. Others were not and, as a result, are losing market share. Some are confronting their very viability.

In an era where digital is the product successful businesses will be those that transform enormous volumes of real-time data generated by their digital services into insights in order to improve how they run their businesses and serve their customers. The ability to do this in real-time, all the time, across multiple functional disciplines, lies at the heart of Continuous Intelligence.


From Hype to Reality

Sumo Logic recently commissioned an independent market research study to understand the industry momentum behind continuous intelligence — and the necessity for digital organizations to embrace a cloud-native, real-time continuous intelligence platform to support the speed and agility of business for faster decision-making, optimizing security, driving new innovation and delivering world-class customer experiences.

The global report included the insights from 765 professionals with cloud-migration leadership responsibilities. Some of the key findings include:

■ 88% of C-suite executives surveyed said they believe their company will benefit from continuous intelligence

■ 74% believe continuous intelligence will help drive companies’ speed and agility

■ 76% indicated they are likely to employ continuous intelligence within the next 12 months

■ 62% believe continuous intelligence is a new approach to data that many companies will need to embrace as they become more software-driven to drive revenue.

Further proof of this is in 2019, Gartner named continuous intelligence a top ten data trend that will change business, predicting that, "by 2022, more than half of major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions."

An Analytics Model for the Next Chapter of Business

The need for analytics to drive better business decisions has been around since the early days of the computer. Analytics systems have evolved as business models have evolved over the
last 100+ years. Continuous intelligence represents the analytics model for the next chapter of business: real-time software driven, digital businesses that deliver new digitally enabled products and services faster and drive better holistic customer experience. It allows organizations to more rapidly deliver reliable applications and digital services, protect against modern security threats and consistently optimize their business processes in real time. This empowers employees across all lines of business, development, IT and security teams with the data and insights needed to address the technology and collaboration challenges required for modern business.

Key Trends Driving Continuous Intelligence

The need for continuous intelligence results from the convergence of a variety of trends including:

Accelerating cloud migration - It should come as no surprise that applications are increasingly being hosted in the cloud. Fully 75% of applications are currently hosted via cloud technologies. More notably, the outlook for growth in cloud hosting is strong – fully 93% of respondent companies anticipate increasing their use of the cloud for hosting their applications. As businesses move to the cloud, data management challenges increase exponentially. Applications must scale to handle bigger workloads. Security issues must be identified and resolved in real-time across thousands of nodes, containers, serverless functions and other IaaS services running across multiple clouds. Application functionality must evolve to anticipate and address changing customer requirements.The key to success in this modern environment will be rapid data collection and analysis with insights that illuminate the path to immediate implementation.

Strategic importance of "real-time" data analytics - As businesses increasingly grapple with digital transformation, what has emerged is the widespread appreciation of the importance of "real-time" data analytics and the recognition that insights from these analytics have strategic value — across the entire organization. Research shows widespread agreement with a variety of statements depicting the importance of real-time analytics and business success.

The rise of DevSecOps - The focus on developing modern applications means that enterprises must continually strive to deliver high performance, highly scalable, always-on digital services. These services require custom "modern architectures" — an application stack with new tiers, new technologies and microservices running on cloud platforms such as AWS, Azure or Google Cloud Platform. The need to manage these modern architectures effectively has given rise to the practice of DevSecOps — the process and practice of development that makes every team member responsible for safety. DevSecOps is next generation secure development, a shift from older, reactive security models.

Conclusion

Those businesses that will be best-equipped to succeed in this era of uncertainty, will be those that are truly real-time businesses. The most successful real-time business operations are embracing continuous intelligence as a core strategy for providing the analytics and insights they need to accelerate decision-making, ensure competitive advantage and deliver engaging customer experiences for sustained success.

Bruno Kurtic is Founding VP, Strategy & Solutions, at Sumo Logic

The Latest

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...