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Driving Your Business Decisions Using Predictive Analytics

Did you have trouble getting a good rate on your home mortgage? Are you happy with your FICO score? Well, whether you know it or not, you are the victor or victim of predictive analytics. Predictive analytics evaluates the current and past and makes predictions about the future. It is made up of numerous methods utilizing game theory, neural networks, statistics, time series analysis, to name a few. It is the hidden decision engine that drives many innovative businesses today.

In fact, predictive analytics is now part of our everyday lives and the decisions we make. For example, your medical prescriptions are tested using predictive analytics; underwriting the risk in your insurance policy uses predictive analytics and even identifying fraud against your credit card utilizes one or all of these methods. By building and employing models that depict the relationships among numerous variables such as credit card charges and location of those charges, then applying one of the predictive methods against it, inferences can be made as to future events or occurrences such as a potential fraudulent transaction. Predictive analytics’ use will continue to grow as more and more data is collected in nearly every walk of life.

Importance of Predicting IT Operations

One very important use of predictive analytics is in the information technology arena, specifically IT Operations. Historically, IT Operations has lagged behind the rest of the business in using predictive analysis to fuel innovation and further the business. That is changing slowly and many businesses are starting to harness predictive technologies to help fuel their growth.

The credit card transactions and FICO score described above are analyzed and calculated in the IT Operations area of the given financial service firms that provide this information. The systems used to collect the data and then provide back the inferred information is provided via services from the originating organization in the examples used earlier. These services are critical to these businesses and can have a huge financial impact on them.

What if the same methodologies for predictive business models could be turned inward to the supporting infrastructure to help IT get ahead of growing demand and performance of the other critical services they deliver, such as email, CRM, etc. The monitoring and management of these critical services in many leading organizations is accomplished using a Business Service Management product. A limited number of BSM vendors include predictive analytics in their offerings, which is a natural evolution of the BSM concept. Whereas traditionally BSM focuses on how well IT is supporting the business today, BSM+Predictive Analytics takes the next logical step to encompass how IT will support the business 3, 6, 12 or more months from now.

Many of these critical services are provided using numerous technologies such as wide-area networks, e-commerce, cloud computing, virtualization, virtual storage and others. And it seems like another new, more innovative technology is always around the corner. This rapid evolution of technologies and the criticality of the provided services are fundamentally changing how organizations are thinking about IT and IT projects.

We used to think in terms of technology projects with 12+ month lifecycles and long procurement processes. Capacity analysis for example took place once a year, usually in conjunction with the budgeting process, but not anymore. Now innovative businesses utilizing cutting-edge technology components have their resources allocated on-demand as business needs are identified.

Being able to predict outcomes and resource requirements provides a significant competitive advantage. If a company’s IT operations staff is able to predict when they will be running low on storage; identify bottlenecks in the WAN; identify potential performance enhancements; and reduce potential issues by not over-stressing resources using predictive analytics, then it can dramatically increase the scalability and agility of its business and allow it to take on more customers or provide more services.

From a cost perspective, accurate knowledge of future resource requirements, particularly storage and power, enable IT to make the right purchase at the right market conditions and maximize the return on every dollar spent. These kinds of capabilities are no longer optional for thriving concerns, instead they are critical to competing in the 21st century.

Predictive Analytics in the Future

As predictive analytics becomes more prevalent and more businesses open up to the idea of using it to help drive their growth, it will become imperative that predictive analysis becomes aligned with the company’s goals.

Businesses that provide on-demand services will increasingly tax IT Operations’ ability to provide the critical services in a scalable and highly available manner. Entire business models and supporting operations will be transformed overnight to adapt to changing business conditions and timelines will continue to shrink. Therefore, using predictive analytics will become critical to determining innovative ways to define and drive success not only in IT, but also for the entire business.

About Mark Lynd

In addition to his role as President and COO of Firescope, Mark Lynd is the chief architect of all FireScope solutions. Lynd previously served as Global CTO at Lone Star Funds\Hudson Advisors, a multi-billion dollar global private equity firm. He served six years as CEO of Vectrix, a venture-backed company and has served in various executive, senior and technical management roles with Metromedia, Perot Systems, Amerada Hess, DC Systems and GTE, with more than 23 years of experience in IT, information security, media and ITSM. Mark holds a Bachelor's degree from the University of Tulsa, along with industry certifications such as the CISSP, ISSAP, ISSMP, PMP and CEH. He was named an Ernst & Young's "Entrepreneur of Year - SW Region" Finalist. Mark has served and serves on several corporate boards including FireScope, SMU's Cox School of Business, Vectrix and others. Mark served honorably in the United States Army's 3rd Ranger Battalion, and the 82d Airborne Division.

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If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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 ...

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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 ...

Driving Your Business Decisions Using Predictive Analytics

Did you have trouble getting a good rate on your home mortgage? Are you happy with your FICO score? Well, whether you know it or not, you are the victor or victim of predictive analytics. Predictive analytics evaluates the current and past and makes predictions about the future. It is made up of numerous methods utilizing game theory, neural networks, statistics, time series analysis, to name a few. It is the hidden decision engine that drives many innovative businesses today.

In fact, predictive analytics is now part of our everyday lives and the decisions we make. For example, your medical prescriptions are tested using predictive analytics; underwriting the risk in your insurance policy uses predictive analytics and even identifying fraud against your credit card utilizes one or all of these methods. By building and employing models that depict the relationships among numerous variables such as credit card charges and location of those charges, then applying one of the predictive methods against it, inferences can be made as to future events or occurrences such as a potential fraudulent transaction. Predictive analytics’ use will continue to grow as more and more data is collected in nearly every walk of life.

Importance of Predicting IT Operations

One very important use of predictive analytics is in the information technology arena, specifically IT Operations. Historically, IT Operations has lagged behind the rest of the business in using predictive analysis to fuel innovation and further the business. That is changing slowly and many businesses are starting to harness predictive technologies to help fuel their growth.

The credit card transactions and FICO score described above are analyzed and calculated in the IT Operations area of the given financial service firms that provide this information. The systems used to collect the data and then provide back the inferred information is provided via services from the originating organization in the examples used earlier. These services are critical to these businesses and can have a huge financial impact on them.

What if the same methodologies for predictive business models could be turned inward to the supporting infrastructure to help IT get ahead of growing demand and performance of the other critical services they deliver, such as email, CRM, etc. The monitoring and management of these critical services in many leading organizations is accomplished using a Business Service Management product. A limited number of BSM vendors include predictive analytics in their offerings, which is a natural evolution of the BSM concept. Whereas traditionally BSM focuses on how well IT is supporting the business today, BSM+Predictive Analytics takes the next logical step to encompass how IT will support the business 3, 6, 12 or more months from now.

Many of these critical services are provided using numerous technologies such as wide-area networks, e-commerce, cloud computing, virtualization, virtual storage and others. And it seems like another new, more innovative technology is always around the corner. This rapid evolution of technologies and the criticality of the provided services are fundamentally changing how organizations are thinking about IT and IT projects.

We used to think in terms of technology projects with 12+ month lifecycles and long procurement processes. Capacity analysis for example took place once a year, usually in conjunction with the budgeting process, but not anymore. Now innovative businesses utilizing cutting-edge technology components have their resources allocated on-demand as business needs are identified.

Being able to predict outcomes and resource requirements provides a significant competitive advantage. If a company’s IT operations staff is able to predict when they will be running low on storage; identify bottlenecks in the WAN; identify potential performance enhancements; and reduce potential issues by not over-stressing resources using predictive analytics, then it can dramatically increase the scalability and agility of its business and allow it to take on more customers or provide more services.

From a cost perspective, accurate knowledge of future resource requirements, particularly storage and power, enable IT to make the right purchase at the right market conditions and maximize the return on every dollar spent. These kinds of capabilities are no longer optional for thriving concerns, instead they are critical to competing in the 21st century.

Predictive Analytics in the Future

As predictive analytics becomes more prevalent and more businesses open up to the idea of using it to help drive their growth, it will become imperative that predictive analysis becomes aligned with the company’s goals.

Businesses that provide on-demand services will increasingly tax IT Operations’ ability to provide the critical services in a scalable and highly available manner. Entire business models and supporting operations will be transformed overnight to adapt to changing business conditions and timelines will continue to shrink. Therefore, using predictive analytics will become critical to determining innovative ways to define and drive success not only in IT, but also for the entire business.

About Mark Lynd

In addition to his role as President and COO of Firescope, Mark Lynd is the chief architect of all FireScope solutions. Lynd previously served as Global CTO at Lone Star Funds\Hudson Advisors, a multi-billion dollar global private equity firm. He served six years as CEO of Vectrix, a venture-backed company and has served in various executive, senior and technical management roles with Metromedia, Perot Systems, Amerada Hess, DC Systems and GTE, with more than 23 years of experience in IT, information security, media and ITSM. Mark holds a Bachelor's degree from the University of Tulsa, along with industry certifications such as the CISSP, ISSAP, ISSMP, PMP and CEH. He was named an Ernst & Young's "Entrepreneur of Year - SW Region" Finalist. Mark has served and serves on several corporate boards including FireScope, SMU's Cox School of Business, Vectrix and others. Mark served honorably in the United States Army's 3rd Ranger Battalion, and the 82d Airborne Division.

Hot Topics

The Latest

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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 ...