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Optimizing Time to Insight to Power Business Success

Michael Segal

Businesses everywhere continually strive for greater efficiency.

By way of illustration, more than a third of IT professionals cite "moving faster" as their top goal for 2018, and improving the efficiency of operations was one of the top three stated business objectives for organizations considering digital transformation initiatives.

As a result, Time To Insight, or TTI, which refers to the measure of the time it takes to collect, organize, and analyze the amount of information necessary to generate the intelligence an organization requires, has become intrinsic to business success.

81% of organizations say an hour of downtime costs them over $300,000

In today's competitive environment, where everything happens in real time, it's no longer viable to take hours — or even days — to analyze the volume and variety of data needed to provide a business with any meaningful insight. In addition, service downtime can have huge implications for businesses; 81% of organizations say an hour of downtime costs them over $300,000. Therefore, the shorter a company's TTI, the faster, more responsive, more efficient and more profitable they will be.

However, for businesses to gain the insight, they need to resolve issues or mitigate risks in the quickest possible time, they need to adjust their approach to data.

Real-Time Meaningful and Actionable Insights

It has been suggested that 90 percent of all the world's data has been created within the last two years. With such explosive growth and evolution of advanced analytics techniques that can convert this information into actionable intelligence, data is now widely considered to be as valuable as oil. As a result, businesses have been collecting and storing increasingly large volumes of information of data from a myriad of devices, systems and applications in the hope that it will become lucrative.

But, as the volume of data continues to grow, companies need to consider re-evaluating the way they handle data, and look toward a smart data approach. This involves harvesting all the important information from every action and transaction that traverses the entire enterprise infrastructure through traffic flows and compressing it into metadata at its source.

Importantly, a smart data approach represents greater efficiency. Once collected, smart data is normalized, organized, structured in a service-contextual fashion, and made available in real-time, all of which will significantly increase the efficiencies of analytics, improve the quality of the intelligence and reduce an organization's TTI.

Furthermore, smart data offers a high level of veracity. Constant monitoring of the wire data that traverses the infrastructure allows users to harvest all relevant key service assurance and threat indicators, which means that rather than simply having a select snapshot of sampled data, businesses are able to access contextualized data that provides real-time, continuous, and actionable insights across their entire IT infrastructure.

Cutting Costs

In addition, smart data's advantages can extend beyond just business efficiencies.

The traditional approach to service assurance, threat management, and business analytics involves collecting large amounts of data from multiple systems, applications and infrastructure components and sending it to a central location for storage and processing. This approach increases the required storage, processing and networking capacity and cost and has environmental implications, when you consider that server farms and networks account for 50 percent of the electricity consumption in our connected world.

With a smart data approach, the volume of the collected data is significantly compressed, since the raw traffic flows are processed at the source and metadata is created. It is therefore possible to keep only the data that is valuable for the task at hand, and discard the unnecessary overhead, thus both saving costs and reducing energy consumption.

What's more, as business everywhere undergo a form of digital transformation to enhance the speed and agility of their operations, smart data is able to significantly improve their Time To Insight, in strategic areas of service assurance and threat management. Through this approach, they are able to gain an additional edge in an increasingly competitive market. Considering the overwhelming benefits smart data can offer, it won't be long before it becomes the dominant approach to service assurance, threat management, operations management and business analytics.

Hot Topics

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Optimizing Time to Insight to Power Business Success

Michael Segal

Businesses everywhere continually strive for greater efficiency.

By way of illustration, more than a third of IT professionals cite "moving faster" as their top goal for 2018, and improving the efficiency of operations was one of the top three stated business objectives for organizations considering digital transformation initiatives.

As a result, Time To Insight, or TTI, which refers to the measure of the time it takes to collect, organize, and analyze the amount of information necessary to generate the intelligence an organization requires, has become intrinsic to business success.

81% of organizations say an hour of downtime costs them over $300,000

In today's competitive environment, where everything happens in real time, it's no longer viable to take hours — or even days — to analyze the volume and variety of data needed to provide a business with any meaningful insight. In addition, service downtime can have huge implications for businesses; 81% of organizations say an hour of downtime costs them over $300,000. Therefore, the shorter a company's TTI, the faster, more responsive, more efficient and more profitable they will be.

However, for businesses to gain the insight, they need to resolve issues or mitigate risks in the quickest possible time, they need to adjust their approach to data.

Real-Time Meaningful and Actionable Insights

It has been suggested that 90 percent of all the world's data has been created within the last two years. With such explosive growth and evolution of advanced analytics techniques that can convert this information into actionable intelligence, data is now widely considered to be as valuable as oil. As a result, businesses have been collecting and storing increasingly large volumes of information of data from a myriad of devices, systems and applications in the hope that it will become lucrative.

But, as the volume of data continues to grow, companies need to consider re-evaluating the way they handle data, and look toward a smart data approach. This involves harvesting all the important information from every action and transaction that traverses the entire enterprise infrastructure through traffic flows and compressing it into metadata at its source.

Importantly, a smart data approach represents greater efficiency. Once collected, smart data is normalized, organized, structured in a service-contextual fashion, and made available in real-time, all of which will significantly increase the efficiencies of analytics, improve the quality of the intelligence and reduce an organization's TTI.

Furthermore, smart data offers a high level of veracity. Constant monitoring of the wire data that traverses the infrastructure allows users to harvest all relevant key service assurance and threat indicators, which means that rather than simply having a select snapshot of sampled data, businesses are able to access contextualized data that provides real-time, continuous, and actionable insights across their entire IT infrastructure.

Cutting Costs

In addition, smart data's advantages can extend beyond just business efficiencies.

The traditional approach to service assurance, threat management, and business analytics involves collecting large amounts of data from multiple systems, applications and infrastructure components and sending it to a central location for storage and processing. This approach increases the required storage, processing and networking capacity and cost and has environmental implications, when you consider that server farms and networks account for 50 percent of the electricity consumption in our connected world.

With a smart data approach, the volume of the collected data is significantly compressed, since the raw traffic flows are processed at the source and metadata is created. It is therefore possible to keep only the data that is valuable for the task at hand, and discard the unnecessary overhead, thus both saving costs and reducing energy consumption.

What's more, as business everywhere undergo a form of digital transformation to enhance the speed and agility of their operations, smart data is able to significantly improve their Time To Insight, in strategic areas of service assurance and threat management. Through this approach, they are able to gain an additional edge in an increasingly competitive market. Considering the overwhelming benefits smart data can offer, it won't be long before it becomes the dominant approach to service assurance, threat management, operations management and business analytics.

Hot Topics

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...