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.

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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.

Related Links

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How AI Agents Are Reshaping DataOps for the Always-On Enterprise

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 ...

AI Deepfakes: Rethinking Trust in the Workplace

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 ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

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 ...

Why ITOps Need Right-Sized AI, Not Bigger Models

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 ...

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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 ...

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