A pharmaceutical company saved US$7 million in projected hardware and software purchases, increased virtual machine density by three times, and realized ROI in three months.
A bank saved $2.2 million in server hardware over two years, cut business service downtime by 50 percent, and achieved an ROI of 268 percent.
An international hospital group reduced time to resolution for critical incidents by 68 percent and achieved $1.2 million of annualized savings.
These are just some of the many examples of how predictive intelligence can help businesses reduce their costs significantly. This forward-looking IT management approach and corresponding technologies provide the most effective and efficient line of defense for critical business services.
Predictive intelligence technologies deduce what is normal and abnormal in the environment by studying usage patterns. The technologies then trigger targeted actions to isolate the root cause of future threats and eliminate them before services are impacted.
Predictive intelligence technologies intercept, prioritize, and resolve incidents in order of business impact. As a result, they offer rapid returns on your technology investments and slash your capital and operating expenses.
From Reactive to Proactive to Predictive Management
For a number of years, IT professionals have focused significant attention on moving from reactive to proactive management of their IT environment. A reactive approach entails addressing an IT problem or incident after it has already occurred. This approach poses the most risk of prolonged business service disruptions because the identification and resolution of each incident are not started until a service disruption is reported.
A proactive approach involves setting predefined thresholds for key performance measurements and enacting a specified course of action (such as triggering an alert) each time these thresholds are crossed.
Proactive management is an important step forward. With the growing complexity of the IT environment, however, it’s no longer enough to be proactive. Static thresholds in the proactive IT management model are set arbitrarily through a time-consuming manual process.
Measurement of even a single threshold can vary widely over time as the environment changes and your infrastructure evolves. Adjusting the thresholds regularly can be tedious and inefficient.
Predictive intelligence is made possible by the convergence of several highly advanced technologies: dynamic thresholding, event correlation and analysis, predictive modeling, and impact management.
- Dynamic thresholding. This technology enables you to set a threshold based on past behavior. It then watches the behavior of your applications over time to learn what truly is normal and abnormal for your applications in your environment. It uses what it learns to adjust thresholds automatically, so you don’t have to reset them manually.
- Event correlation and analysis. In most IT environments, the sheer volume of data generated by alerts and events far exceeds the time available to prioritize their resolution. Real-time root cause analysis, a part of event correlation and analysis, eliminates this problem by filtering through the noise. It considers the behavior of an application or device within the context of activity across the entire infrastructure. It closes sympathetic alerts (which are symptoms of the root cause), clears up duplication, identifies the relationships among the remaining alerts, and determines what those relationships are. In essence, the technology intelligently discovers the root cause and how it relates to other alerts in the queue. Event correlation and analysis solutions speed problem resolution by translating events into actionable, business-relevant information.
- Predictive modeling. With this technology, you establish baselines and rules that frame your approach and reflect your current performance and usage patterns. Analysis and correlation of performance data uncovers recurring patterns and relationships. These relationships are then correlated to understand how they link to your overall business and resource needs. Predictive modeling helps you in many ways: identifying areas where you can better utilize IT resources, balancing workloads across servers, accurately consolidating physical workloads and servers onto a virtual platform, and accurately forecasting future capacity requirements. The ability to optimize existing IT resources — while ensuring delivery on service levels and accurately predicting future requirements — can translate into millions of dollars saved in capital and operating expenditures.
- Pre-assigned business impact priority. Predictive intelligence technology can automatically detect the priority for addressing events to best support business services. For example, you can detect that one router supports a small, remote sales office, while the other supports the entire European customer base. The bottom line is that this technology provides the service desk with detailed incidents containing event and root-cause information with pre-assigned business impact priority. Consequently, it helps agents shorten overall time-to-resolution and enables you to meet or exceed service level agreements with the business.
Predictive Intelligence in Action
The convergence of these and other technologies that support predictive intelligence can reduce the noise level by 60 to 70 percent, eliminating both sympathetic and duplicate events, according to industry studies.
Predictive intelligence is an important byproduct of a comprehensive, properly integrated service assurance solutions set. Service assurance solutions deliver adaptive, automated, and predictive technology across the enterprise, dramatically reducing the risk of service disruptions and delivering the consistent levels of service required by the business.
The process flow for predictive intelligence includes detection, diagnosis, isolation, and correction.
- IT incidents are identified in the detect phase. Detection can be as simple as an end-user call to the service desk, or may involve the proactive use of advanced IT tools.
- In the diagnose phase, duplicate and sympathetic events are drastically reduced, enabling you to determine what you should really focus on.
- By completion of the isolate phase, the technology determines, in effect, “What’s the business relevance of this? Is one incident more important than others? Are there capacity problems I need to address?”
- The final phase, correct, resolves issues in order of business priority. Automated fixes can even be embedded here to further streamline repetitive tasks – improving overall mean time to repair (MTTR).
Reaping the Rewards Now
Predictive intelligence isn’t a future vision of how IT will work 10 or 20 years from now. It’s here today. It is an important byproduct of a comprehensive, properly integrated service assurance solutions set. Service assurance solutions deliver adaptive, automated, and predictive technology across the enterprise, dramatically reducing the risk of service disruptions and delivering the consistent levels of service required by the business.
Enterprises such as the pharmaceutical company, bank, and hospital group profiled at the beginning of this article are already profiting from such solutions. Their successes provide proof that predictive intelligence delivers rapid payback in the form of direct savings, increased revenue, faster mean time to repair (MTTR), and better service levels to support strategic business goals.
About the Author
Mahendra Durai, IT CTO, BMC Software, leads the global infrastructure and information security organizations within BMC’s IT organization. For the past 20 years, he has held progressive IT roles in multiple industries, bringing effective alignment of IT to business needs.