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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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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

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 ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

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

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

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 ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...