Category: artificial intelligence technologies

How ZIF Delivers Service Reliability to Financial Institutions Using AIOps

Posted on by Subhadeep Datta

Financial institutions use business applications to provide services to their users. They have to continuously monitor the performance of business applications to enhance service reliability. During peak business time, the number of impactful incident increase that downgrades the performance of business applications. IT experts have to spend more time addressing the incidents one by one. Financial institutions can use an AI-based platform for maintaining business continuity and service reliability. Let us know how ZIF enhances services reliability for financial institutions via AIOps.

What is service reliability?

Financial institutions are undergoing digital transformation quickly. For providing a digital user experience, financial institutions use software systems, applications, etc. The business applications need to perform continuously according to their specifications. If the performance gets deteriorated, business applications may experience downtime. It will have a direct effect on the ROI (Return on Investment).

Service reliability ensures that all the business applications or software systems are error-free. It ensures the continuous performance of IT systems within any financial institution. Business applications should live up to their expectations without any technical error. Financial institutions that have better service reliability also have larger uptime. Service reliability is usually expressed in percentage by IT experts.  

What is AIOps?

AIOps (Artificial Intelligence for IT Operations) is used for automating and enhancing IT processes. AIOps uses the mixture of AI and ML algorithms to induce automation in IT processes. In this competitive era, AIOps can help a business optimize its IT infrastructure. IT strategies can be deployed at a large scale using AIOps.

The use of AI in IT operations can reduce the toll on IT experts as they don’t have to work overtime. Any issues with the IT infrastructure can be addresses in real-time using AI. AIOps platforms have gained popularity in recent times due to the challenges posed by the COVID pandemic. Financial institutions can also use an AIOps platform for better DEM (Digital Experience Monitoring).

What is ZIF?

ZIF (Zero Incident Framework) is an AIOps platform launched by GAVS Technologies. The goal of ZIF is to lead organizations towards a zero-incidence scenario. Any incidents within the IT infrastructure can be solved in real-time via ZIF. ZIF is more than just an ordinary TechOps platform. It can help financial institutions to monitor the performance of business applications as well as automate incidence reporting.

Service reliability engineers have to spend hours solving an incidence within the IT infrastructure. The downtime experienced can cost a financial institution more than expected. ZIF is an AI-based platform and will help you in automating responses to incidents within the IT infrastructure. ZIF can help financial institutions gain an edge over their competitors and ensuring business continuity.

Why use ZIF for your financial institution?

ZIF has multiple use cases for a financial institution. If you are facing any of these below-mentioned challenges, you can use ZIF to solve them:

  • A financial institution may receive alerts at frequent intervals from the current IT monitoring system. An institution may not have enough workforce or time to address such a high volume of alerts.
  • Useful IT operations for a financial institution may face unexpected downtime. It not only impacts the ROI but also drives the customer away.
  • High-impact incidents within the IT infrastructure may reduce the service reliability of a financial institution.
  • A financial institution may have poor observability of the user experience. It will lead to the inability in providing a personalized digital experience to customers.
  • The IT staff of a financial institution may burn out due to the excessive number of incidents being reported. Manual efforts will stop after a certain number of incidents. 

How ZIF is the solution? 

The functionalities of ZIF that can solve the above-mentioned challenges are as follows: 

  • ZIF can monitor all components of the IT infrastructure like storage, software system, server, and others. ZIF will perform full-stack monitoring of the IT infrastructure with less human effort. 
  • ZIF performs APM (Application Performance Monitoring) to measure the performance and accuracy of business applications. 
  • It can perform real-time APM for improving the user experience.
  • It can take data from business applications and can identify relationships between the data. Event correlation alerts by ZIF will also inform you during system outages or failures. 
  • ZIF can make intelligent predictions for identifying future incidents. 
  • ZIF can help a financial institution in mitigating an IT issue before it leaves its impact on operations. 

What are the outcomes and benefits of ZIF?

The outcomes of using ZIF for your financial institution are as follows: 

  • Efficiency: With ZIF, you can enhance the efficiency of your IT tools and technologies. When your IT framework is more efficient, you can experience better service reliability. 
  • Accuracy: ZIF will provide you with predictive insights that can increase the accuracy of business applications. IT operations can be led proactively with the aid of ZIF. 
  • Reduction in incidents: ZIF will help you in identifying frequent incidents and solving them once and for all. The number of incidents per user can be decreased by the use of ZIF. 
  • MTTD: ZIF can help you identifying incidents in real-time. Reduced MTTD (Mean Time to Detect) will have a direct impact on the service reliability. 
  • MTTR: ZIF will reduce the MTTR (Mean Time to Resolve) for your financial institution. With reduced MTTR, you can offer better service reliability. 
  • Cost optimization: ZIF can replace costly IT operations with cost-effective solutions. If any IT operation is not adding any value to your institution, it can be identified with the aid of ZIF. 

ZIF can help you in automating various IT processes like monitoring, incident reporting, and others. Your employees can focus on providing diverse financial services to customers besides worrying about the user interface. ZIF is a cost-effective AIOps solution for your financial institution. 

In a nutshell 

The CAGR (Compound Annual Growth Rate) of the global AIOps industry is more than 25%. Financial institutions are also using AI for intelligent IT operations and better service reliability. Service reliability engineers in your organization will have to put fewer manual efforts with the help of ZIF. Use ZIF for enhancing service reliability! 

How Observability Bolsters Site Reliability Engineering

Posted on by Subhadeep Datta

Are you familiar with the benefits of AIOps (Artificial Intelligence for IT Operation)? Well, artificial intelligence allows businesses to automate IT processes that require fewer human efforts. A key process for automating IT operations is SRE (Site Reliability Engineering). Read on to know about SRE and how it is affected by observability.

What is Site Reliability Engineering?

The IT operations in an organization are performed with the help of various software systems. These software systems are deployed on a large scale and need monitoring. There may be various types of large-scale software systems in an organization like supply-chain management, emergency response, etc. Businesses often hire expert system administrators to oversee the software systems.

To solve the underlying problem, site reliability engineering, a type of software engineering, was introduced. SRE can be termed as a better version of DevOps as it is not divided into multiple teams. SRE aids in building a reliable and scalable software system for an organization. It doesn’t just oversee the software development process but also reduces the friction between the development team and operations team.

Site reliability engineers induce required codes into the software to ensure that it does not need any human intervention The development team constantly wants to launch new updates or software(s). Contrary to that, the operation team only wants to launch an update/software after it is thoroughly tested. SRE removes this conflict between both teams and aids in developing reliable system software.

Understanding Observability in SRE?

How would you work with invisible software? It is difficult to measure the performance of any system software if you are not aware of its internal components that drive the performance. The outputs of system software are analyzed to know about the internal states of system software. High observability allows us to know about the internal states of system software. Observability is the measure of the degree that reflects how well the internal states of system software can be inferred.

Site reliability engineers willingly code their software to provide metrics and logs. These metrics are then used to know about the internal state of the software. Observability may seem similar to monitoring but, it is not. Besides telling about the functioning of the system software, it also provides required data to solve underlying problems in the system.

Due to enhanced observability, site reliability engineers do not have to eliminate a potential risk themselves. They can have access to data insights that contain the possible solution. AIOps with rich observability guide on eliminating a risk/problem in system software. The engineers will only know the external outputs of the system software and can still know about its internal state due to enhanced observability.

Pillars of Observability

The three pillars of observability are as follows:

  • Metrics: Metrics are used by reliability engineers to determine the health of the system. For example, there is a threshold for CPU consumption and, when the consumption goes beyond that number, it is being overused. A metric is defined for CPU consumption and alerts can be triggered when the consumption is more than usual. Site reliability engineers use metrics to find the threshold value and set trigger alerts.
  • Logs: Logs are mostly referred to at the time of any problem with the software system. It is a statement that describes the anomaly that occurred. A log can be generated as plain text or structured texts depending upon the requirements.
  • Traces: A trace helps us in determining the execution of a piece of code. Reliability engineers combine the traces and can find the code execution flow in a distributed software system. One can get to know about the part of code that is being executed in more time as compared to other code blocks.

Knowing about the pillars of observability also highlights its importance. Metrics, logs, and traces can help site reliability engineers to know more about the software system by asking questions from outside. While troubleshooting, reliability engineers do not analyze the pillars of observability separately. The pillars of observability should be analyzed together to gain a better understanding of the system and solve the underlying problems.

Benefits of Observability for Site Reliability Engineers

Some of the main benefits of observability for SRE are as follows:

  • Site reliability engineers study the observability pattern and can easily manage any incidents.
  • It will improve the uptime of software systems in an organization.
  • Observability provides a platform for inspection under SLO (Service -Level Objective).
  • When SLOs are not met and, the system software doesn’t live up to the expectation, observability is used to find the possible solution.
  • An employee has a limit to analyze and understand large chunks of data. With enhanced observability, the cognitive load of the data analysis team is reduced.
  • Observability brings together multiple autonomous teams to work together for a single goal.
  • Observability improves the productivity and innovation standards within an organization.
  • Site reliability engineers do not have to access the internal data of a software system to know about its performance. With enhanced observability, reliability engineers can easily find the performance of the software system by its outputs.

Observability Vs Monitoring

Observability definition may sound similar to monitoring but, it is not. As you delve deeper, you will understand that monitoring only informs us about the underlying problem and not the solution. Contrary to monitoring, observability allows us to find the possible solutions and measure the performance of a system.

The pillars of observability help us in making sure the system software is doing the job it was intended to do. Unlike monitoring, observability can identify and mitigate risks associated with system software(s).

In a Nutshell

For automating business processes and systems, first, you need to know about the underlying problems. Enhanced observability can determine the health of a system and can highlight underlying problems. AI-driven observability is also being preferred by businesses nowadays. Site reliability engineering is strengthened if the systems offer high observability. Go for high observability in your software system(s)!  

Exploring AIOps? Here’s Where to Begin

Posted on by Subhadeep Datta

The IT operations, the lifeline of a company or a business, is where the active response system of the firm is centered. Constant innovation has led to significant changes in the operational infrastructure over the last few years and brought new-age challenges to the fore that continue to test the limitations of the existing structure. Especially with the digital revolution that is increasingly transforming the way businesses function and manifest themselves, the ITOps struggle to cope with the mounting data volume and in leveraging inputs from core systems.

This is prompting businesses to push their boundaries and resort to Artificial Intelligence (AI) solutions with greater bandwidth. AIOps or Artificial Intelligence for IT operations is the way traditional IT services and management can be reassessed by integrating machine-based learning and abilities into the existing database and launching it in a broader spectrum. This involves functions like automation, availability, event correlation alerts, and delivery at par with the rising complexities of the business.

AIOps as evolving platforms

The penetration of AIOps in modern job design has been slow but steady. Big business enterprises are now looking at AIOps as an advanced tool to single-handedly manage extensive data monitoring and analysis while processing burgeoning data and information load at a remarkably fast pace. AIOps are programmed for tackling a diversity of output for exact and efficient error spotting in real-time situations followed by critical problem-solving and addressing high-risk outages. Besides, the predictability of AIOps provides a clear sense of the shortcomings and be prepared for unforeseen disruptions in the workflow.

According to reports by the world’s leading research and advisory company and IT management specialist Gartner, the use of AIOps may rise exponentially in the next 3-5 years at a rate of up to 30% that can help catapult business statuses remarkably. However, harnessing this technology requires a solid investment which is why the businesses are interested in a model that is reliable and can prove to be beneficial to the clients and profitable to these ventures at large.

The roadmap for AIOps

Launching and incorporating a versatile technology like AIOps with the current business machinery can seem daunting initially. However, before proceeding with the AI setup, it is essential to consider both the immediate and long-term implications of such an arrangement. Most often, organizing pre-existing records is the key to implement AIOps to the best of capacity.

Here are a few pointers a business can follow to launch its own AIOps interface:

  • It’s always wise to understand the technology before applying it. AI is no rocket science but requires a focused approach to make sense of its terminology. Persistent engagement with the AI tools can result in a better grasp of the subject matter and an assured involvement with related projects and other stakeholders.
  • Start small and simple and demonstrate the power of AIOps to your team in the most convincing manner possible. Highlight the specific problems that you expect your AIOps to fix including the anomalies that escape human surveillance. This could imply automatic troubleshooting and recovery responses if the system reports any malfunction. In a larger picture, this will helps mobilize isolated IT entries and integrate the work ecosystem with a more robust yet feasible strategy.
  • Allow room for experiments to evaluate the true potential of an AIOps application. Even if the whole initiative is cost-intensive, there are resources available at a reasonable rate that can be used to expand knowledge and tap into its wide range of functionality.
  • Be well-versed with digital analytics and statistical figures to enable the management of big data and helps track and monitor performance updates. This gives a direct insight into the positioning of your business and measures that can be taken to upscale possibilities.
  • Equip your infrastructure with State-of-the-art facilities that can shoulder the AIOps network and fully support the system upgrades that may emerge in the process.
  • When it comes to monetization of the business, the AIOps capitalize on the Return on Investment factor and help the company earn big bucks and invest in furthering the threshold. This can give a fair sense of how to fund AI-driven businesses at every stage.

Conclusion

The partnership between IT and AIOps is the new currency for optimizing the digital operations of a company. AI could very well be the gamechanger but it requires the informed use of this feature to take any enterprise to the next level. AIOps are also ‘guardians of security’ of confidential data owing to their prompt detecting abilities that minimize the risk of breach, leaks, cloning, and other potential threats that may hamper its credibility.

Therefore, embrace this top-notch, persuasive technology without fear or hesitation as it not only beats the operational status quo in theory but also sets revolutionary but achievable standards for your business. As a result, your project becomes more ambitious and future-oriented and the tech more approachable and ubiquitous with each passing day.

AIOps Trends Reinforced by Covid-19

Posted on by Subhadeep Datta

Are you able to manage your IT operations effectively? Well, managing IT operations can be more complex when you decide to scale up your IT infrastructure. The recent COVID pandemic has affected the IT ecosystem adversely. IT experts aren’t able to control the IT operations effectively as they can’t go to the workplace. All these issues led to the rise of AIOps (Artificial Intelligence for IT Operations). AIOps can help an organization to manage its IT operations and systems amidst the COVID pandemic. Read on to know about the AIOps trends reinforced by this global pandemic. 

What is AIOps?

Before we move on to the latest trends in the AIOps market, you should be well aware of the AIOps definition. AIOps use the blend of AI (Artificial Intelligence) and ML (Machine Learning) to solve issues in IT operations. AIOps can enhance or replace any IT process depending upon the requirements of any particular business.

It induces automation in IT operations and makes them less tedious. Various IT processes like data analysis, event correlation, service management, and others can be automated using AIOps. AIOps platforms can cope with the increasing volume and variety of business data. AIOps help businesses to boost productivity and maintain business continuity. Even if no one is there to manage your IT operations, you can still complete key processes using AIOps.

AIOps trends fuelled by the COVID pandemic

The recent COVID pandemic led to the suspension of business activities for a long time in various countries. It was hard for business owners to maintain business continuity in these times. Companies could not provide a reliable IT infrastructure to their employees outside the office premises. AIOps use cases were discovered during this time and, businesses started using them. The COVID pandemic led to the rise of many AIOps companies and use cases. Let us see some of the latest AIOps trends reinforced by the COVID pandemic.

Decrease downtime

This pandemic forced employees to move out of their organizational workplaces. The WFH (Work from Home) culture was quickly adopted by businesses for ensuring business continuity. However, the remote IT infrastructure was not up to the mark and significantly decreased productivity. System administrators and site reliability engineers could not visit each employee’s house for fixing operational issues.

To adapt to the remote work culture, firms started using AIOps tools. AIOps can address IT issues without the need for a system administrator. Responses to common IT operational issues can be automated using AIOps. It helped businesses to decrease the downtime significantly as their IT operations are running without any human intervention.

Cybersecurity

Every business produces large chunks of data that are used to extract meaningful insights. However, with remote work culture, it gets difficult to safeguard the business data. The growing complexity of the IT infrastructure makes it difficult for cybersecurity experts to identify the source of the problem. Also, they cannot rush to the workplace for addressing data breaches due to the COVID pandemic.

An AIOps platform for cybersecurity will help you in identifying the threats and automating responses to them. AIOps solutions for cybersecurity offer strong observability into your organization’s data. The source of a cyber-attack can be easily determined via AIOps. Even if no one is there at the workplace, data breaches can still be stopped using AIOps.

Observability

Better observability lets us know about the internal states of IT systems being used. AIOps connects the IT frameworks and provides end-to-end visibility. Due to the COVID pandemic, engineers cannot visit the home of each employee to know about the internal states of IT systems. With AIOps, you can monitor the performance of IT infrastructure in real-time.

Not only for the employees, but AIOps can also provide a better user experience to customers while interacting with digital interfaces. AIOps also helps CIOs (Chief Information Officers) with digital experience monitoring. With enhanced observability, you can improve the customer experience and productivity.

AIOps & DevOps

DevOps is focused on removing the gap between the development and the operations team. However, due to the COVID pandemic, both the teams are working remotely and, it is hard to collaborate. AIOps enhances the communication between the development and operations team and allows east collaboration. Continuous monitoring of DevOps processes via AIOps will provide better results.

AIOps can automate various DevOps processes like feedback collection, monitoring, deployment, and others. AIOps vendors offer products that are capable of performing testing during the development cycle without any human intervention. AIOps products focus on strengthening the integration between different IT teams so they work together to achieve business goals.

Digital transformation

Due to the COVID pandemic, businesses have to opt for online marketing strategies. The only way left to connect with the audience is via digital interfaces. Digital transformation not only requires adopting the latest technologies but also involves continuous monitoring. You will also have to perform data analysis to extract meaningful insights from the data produced via digital platforms.

AIOps can help you with digital transformation and can also help you make the best out of the business data. AIOps automation can help in analyzing data in real-time and extract meaningful insights. High-end data analytics via AIOps can help you in making better business decisions.

Cost optimization

The recent COVID pandemic has harmed businesses. The market disruptions caused by this pandemic have severely affected the ROI (Return on Investment) of businesses. Business owners are looking to slash costs by downsizing their staff thus, impacting the business adversely.

With AIOps, you can automate IT operations and do not need to affect your business reach. The cost of AIOps may seem high in the beginning but is less in the long run. You will also have to spend less on AIOps training as it is easy-to-use and an automated platform.

In a nutshell, The global AIOps market size will grow with a CAGR of 21.05% by 2026. It is the right time to use AIOps and steer through the challenges posed by the COVID pandemic. Use AIOps for business continuity and better uptime!  

Top 6 Things AIOps Can Do for Your IT Performance

Posted on by Subhadeep Datta

With technological advancement and reliance on IT-centric infrastructure, it is essential to analyze lots of data daily. This process becomes challenging and often overwhelming for an enterprise. To ensure the IT performance of your business is on par with the industry, Artificial Intelligence for IT operations (AIOps) can help structure and monitor large scores of data at a faster pace.

What are AIOps?

It is the application of artificial intelligence, machine learning, and data science to monitor, automate and analyze data generated by IT in an organization. It replaces the traditional IT service management functions and improves the efficiency and performance of IT in your business.

AIOps eliminates the necessity of hiring more IT experts to monitor, manage and analyze the ever-evolving complexities in IT operations. AIOps are faster, efficient, error-free, and reliable in providing solutions to issues and challenges involved in IT.

Top 6 Things AIOps can do for your IT Performance

By moving to AIOps you save a lot of time and money involved in monitoring and analyzing using the traditional methods. You can also eliminate the risk of faulty data or outdated reports by opting for AIOps. Here are six reasons to choose AIOps and how they can enhance your IT performance.

1. Resource Allocation and Utilization

AIOps make it easy for an enterprise to plan its resources. Real-time analytics provides data on the infrastructure necessary for a seamless experience be it the bandwidth, servers, memory, and more details.

AI-based analytics also helps an enterprise plan out the capacity required for their IT teams and reduce operational costs. With AI-driven analytics, the enterprise knows the number of people required to address and resolve events and incidents. It can also plan the work shifts and allocate resources based on the number of incidents during any given time.

2. Real-time Notification and Quick Remediation

Real-time analytics has made it easy to make quick business decisions. With AIOps, businesses can create triggers for incidents and can also narrow down business-critical notifications.

According to a study, about 40% of businesses deal with over a million events daily. Assessing priority events becomes an issue in such cases. AIOps help businesses prioritize and effect quick remedies for anomalies. The priority incidents can then be assigned to the IT team to resolve on priority.

3. Automated Event and Incident Management

Using data collected by AIOps, both historical and real-time, businesses can plan for different events and incidents. Thus, offer automated remedies for such incidences.

Traditionally, detection and resolution of such events took a long time and required larger incident management teams. It also meant that the data collected would not be real-time.

Using AI-based automation reduces the workload and ensures that an enterprise is equipped to handle current incidents and planned events. It also requires less manpower to deal with such incidents saving a business from hiring costs.

4. Dependency Mapping

AIOps help understand the dependencies across various domains like systems, services, and applications. Operators can monitor and collect data to mark the dependencies which are even hidden due to the complexities involved.

AIOps even analyze interdependencies that might be missed unless there is thorough monitoring of data. It helps enterprises in the process of configuration management, cross-domain management, and change management.

Businesses can collect real-time data to map the dependencies and create a database to use in change management decisions like when, how, and where to affect system changes.

5. Root-cause Analysis

For improved IT efficiency and performance, understanding the root cause of anomalies and correlating them with incidents is important. Early detection will help affect quicker remedies.

AIOps let IT teams in a business have visibility on anomalies and their relation to abnormal incidents. Thus, they can respond quickly with efficient resolutions for a smooth experience.

The root-cause analysis also helps in improving the domain and ensuring that the business runs efficiently with less exposure to unknown anomalies. Businesses are equipped to investigate and remedy the issue with better diagnoses.

6. Manage IoT

With many Internet of Things devices used widely, the necessity to manage data and the device complexity is of utmost importance. AIOps sees a wide application in this field and help manage several devices at the same time. The sheer volume of devices can make it overwhelming to manage IT operations.

IoT devices have several variables in play and operators require AIOps to manage them with ease. Machine learning helps leverage IoT and monitor, manage and run this complex system.

AIOps ensure that the IT performance thrives with consistent efficiency. It not just helps monitor large data in real-time but also detects issues, analyzes correlation, and ensures quick resolutions. Automated resolutions and management can eliminate downtime and save time and money for any business.

In a nutshell, AIOps aid in the consolidation of data from various IT streams and ensures you receive the highest benefit out of it. Whether it results in automation, resolving incidents at a quick pace, or finding anomalies and making data-driven decisions, AIOps help an organization while ensuring the IT performance is efficient.

ZIF Offers a Resilient IT Cure to the Healthcare Sector

Posted on by Subhadeep Datta

The healthcare sector is going through a paradigm shift as more and more facilities are undergoing digital transformation. The use of new-age technologies like AI and ML have boosted the productivity of healthcare facilities. Healthcare facilities are now focusing on implementing an organized IT infrastructure. Besides offering products and services, the healthcare industry is also involved in finance processes. To manage these components of the healthcare sector, a robust IT framework can be established. Read on to know how ZIF can offer a resilient IT cure to the healthcare sector.

What is ZIF?

ZIF (Zero Incidence Framework) is an AI-based framework distributed by GAVS Technologies. It is an AIOps (Artificial Intelligence for IT Operations) platform. AIOps platforms are used for inducing automation and resilience in the IT infrastructure. AIOps products use AI and ML to reduce the number of incidents in the IT infrastructure.

ZIF can help you in discovering business applications in your environment. It helps in monitoring the performance of digital interfaces in your environment. You can not only set a reliable IT infrastructure but can also make it resilient. Your IT tools and technologies will be able to recover quickly from any outage/failure with ZIF.

Artificial intelligence in the healthcare industry

Before choosing ZIF for your healthcare facility, you should be aware of the use cases of artificial intelligence. The uses of AI-based platforms in the healthcare industry are as follows:

  • AI is being used for medical imaging by healthcare facilities.
  • AI is used for drug discovery.
  • AI-based platforms are used by healthcare facilities for better IT infrastructure.  
  • AI helps in automating cybersecurity processes in the healthcare sector.
  • AI is used to create virtual health assistants.

ZIF for IT infrastructure in the healthcare sector

Healthcare facilities run critical enterprise applications that are responsible for patient care. If the performance of such critical applications downgrades, it will harm the reliability of the healthcare facility. The IT landscape has changed a lot over the years and, healthcare facilities are finding it hard to keep up. Most healthcare facilities do not hire IT experts and use premade IT frameworks for patient care. The premade frameworks often fail when they experience more load and traffic. All these challenges can be solved by using ZIF for a robust IT infrastructure in your healthcare facility.

ZIF is a reliable AIOps solution that can help you in eliminating risks and incidents from your IT infrastructure. ZIF works on an unsupervised learning model and does not needs more manual efforts. With the growing needs of a healthcare facility, ZIF can help the IT infrastructure to scale up. Your workers can focus on treating the patients while ZIF can handle the service reliability of your digital solutions.

System reliability with ZIF

The healthcare software systems are very sensitive and, a slight mishap can cause a big blunder. The Healthcare industry has to learn from past system failures to make sure it never happens. Healthcare systems should be safe and reliable to provide the best results. Healthcare organizations often face challenges while upgrading their software systems according to the requirements. System reliability in healthcare is measured in terms of failure-free operation of software systems.

ZIF will help you in ensuring that digital systems operate without any failures over time. It will continuously check for any issues with software systems. Once an incidence is reported, ZIF will help you in eliminating it as soon as possible. It will help you in enhancing system reliability and uptime for your healthcare facility.

Enhanced monitoring with ZIF

Due to the recent COVID pandemic, healthcare organizations have started monitoring the health of patients remotely. For online advisory and telemedicine, healthcare facilities have to deploy the required systems. They need to have reliable systems that connect them to the patients. For the continuous performance of these systems, they are connected to a central monitoring system. If the monitoring system is not able to detect the reason for the poor performance of other systems, it may harm the patient’s health.

With ZIF, you can monitor the health of all the consolidated systems under one dashboard. The OEM device monitoring feature of ZIF lets you analyze the health of digital systems anytime. ZIF is a reliable AIOps tool that can let you set thresholds for the maintenance of digital systems. ZIF also provides a consolidated view of your organizational data for high-end analytics. The monitoring of all digital systems via ZIF can significantly increase service efficiency.

Reliability prediction with ZIF

ZIF not only solves the current incidences but also predicts future incidences. ZIF will evaluate the performance of systems and will predict their future failure chances. ZIF will provide you with a failure rate that can define the vulnerability of a system. It will let you make proactive approaches to eliminating future failure chances. You can create resilient IT systems with ZIF for your healthcare facility. Resilient IT systems quickly recover after an incidence and provide effective performance over time.

Autonomous IT systems with ZIF

Do you want your staff members to focus more on patient service than system monitoring? Well, ZIF will help you in automating various day-to-day IT operations. You can set automated responses for a particular type of incidence via ZIF. It is an AIOps solution specifically designed for autonomous and predictive IT processes.

ZIF will monitor user experience and identify latencies and anomalies in real-time. This process will be done automatically by ZIF without any manual efforts. Even if the end-user is not able to identify any anomaly, ZIF will find it out. You can configure ZIF to send automated and real-time alerts for any incidence. It will also provide the SOP for incidence protection in real-time.

In a nutshell

The global AIOps market size will reach around USD 20 billion by 2025. AIOps platforms for healthcare can help them undergo digital transformation quickly. ZIF can help you with device monitoring and enhancing system resiliency. Choose ZIF for system reliability and resiliency!

Transform your Azure Ecosystem with AIOps to Increase Operational Efficiency

Posted on by Subhadeep Datta

The cloud is now a primary place for SMEs and other large enterprises, and Microsoft’s Azure is considered one of the preferred IaaS and PaaS services for most business organizations.

As Artificial Intelligence and Machine Learning are changing the digital way of life, AIOps is set to uplift cloud services and make operations easy for the IT industry. It provides users with a broader range of benefits, including better customer experience, service quality assurance, and productivity boost.

Why Does Your Organization Need AIOps With Microsoft Azure Ecosystem

As cloud usage is in high demand, businesses are facing problems in managing their cloud infrastructure. AIOps for Azure provides better efficiency with the help of AI-driven software, ensuring smoother operations.

By executing AI operations and ML on Microsoft Azure, organizations can be benefited in many ways. Some of these are:

Efficient and Cost-Effective Infrastructure

Microsoft Azure helps lower the overall cost of a business when enabled with AIOPs and MLOps. AI and ML help make Azure cloud a better choice for Machine Learning Operations and Artificial Intelligence Operations.

Edge Computing

Edge processing aims to bring data resources closer to the users, thus improving the overall performance of the cloud infrastructure. It also helps reduce cost and increase processing capacity simultaneously.

Pre-Trained Machine Learning Models

The Microsoft Azure Platform offers pre-trained models. These can be used for a custom model for tailor-made processing of the company’s workloads. Many ML programs can be used as models through MicrosoftML for Python and MicrosoftML for R for various functions.

Manage Your Azure Infrastructure Easily With AIOps

Microsoft Azure is a reliable cloud service that manages data efficiently. As the cloud is always increasing and becomes complex as each day passes, it needs more developers and engineers to make it stable. It can become quite easy to remain at par with the constantly evolving cloud if there were a solution to make data-based decisions automatically.

Not only will this save a lot of time for the resources of your organization, but also make the process more efficient. AIOps and machine learning help streamline the process and assist engineers in taking actions based on the insights from the existing data.

AIOps is based on self-monitoring and requires no human intervention. Automation of services ensures improved service quality, reliability, availability, and performance.

Azure cloud professionals are no longer required to investigate the repeated process and manually operate the infrastructure. Instead, they use AI and ML engineering. AI operations can work independently, and human resources can utilize their time to focus on solving bigger problems and building new functions.

Design Your Own Growth Path by Systemizing Your Operations With AIOps

The AIOps framework can contribute in several ways. The major elements are explained below.

  • Extensive and Diversified IT Data: AIOps is predicted to bring together data from IT operations management and IT service management. Bringing data from different sources helps accelerate root cause identification of a problem and enables automation simultaneously.
  • Big Data Platform: The center of an AIOps platform is big data. As data is collected from different sources, it is required to be compiled together to support next-level analytics. AIOps aggregates big data and makes it accessible to be used in real-time.
  • Machine Learning: Analysing big data is not possible by humans alone. ML automates and analyzes new and diversified data with a speed that is unachievable without the AIOps framework.
  • Observation: It is the emerging of the traditional ITO domain and other non-ITOM data to enable new models and correlations. The combination of AIOps with real-time processing makes root cause identification easier.
  • Engagement: The traditional domain offers bi-directional communication to support data analysis and, thus, auto-creates documentation for audit while maintaining compliance. AIOps help in cognitive classification with routing and intelligence along with user touchpoints.
  • Act: This is the final stop for the AIOps strategy. It provides the codification of human knowledge into automation. It helps automate analysis, workflow, and documentation for further actions.

What Does the Future Have in Store for IT Operations?

Artificial Intelligence for IT operations is bringing a continuous change in the cloud business. In no time, adopting the AIOps way will become a necessity.

  • Accelerate Digital Transformation: Sooner than later, businesses will be able to offer data-driven experiences with the help of AIOps. It won’t be a hassle to migrate systems after systems, as most of the monotonous work will be handled by automated systems. This way, businesses can easily transform digitally to remain relevant
  • Solutions to Various Challenges: Often, when humans spend time performing basic calculations, a lot of time and energy is wasted. Moreover, there is always a chance of human error. Empowering developers with actionable insights, AIOps will make solving problems hassle-free, replacing many traditional monitoring tools
  • Finding Issues Automatically: A faster and more efficient way to improve customer satisfaction involves ensuring that there are no problems with your service or product. However, this can be challenging. With AIOps solutions, identifying issues and mitigating them will be a cakewalk. It will play an essential role in troubleshooting workloads and understanding and predicting customer needs in the current competitive environment, eliminating the need for having a dedicated team of resources to solve simple issues.

How Does AIOps Transform a Business?

1. Digitization of Routine Practices

The AIOps architecture helps digitize routine practices, like user requests, while processing and fulfilling them automatically. It can even evaluate whether an alert requires action and if all the supporting data is under normal parameters.

2. Recognizing Serious Issues Faster and More Accurately

There are chances of human error while looking out for threats. This may lead to an unusual download being ignored. AIOps tools tackle can solve this problem easily. It can run an antimalware function through the system, automatically and when required.

3. AIOps Streamline the Interactions Between Data Center Groups and Various Teams

AIOps shares all the relevant data with each IT group and provides the operations team with what they require. Manually meeting and sending data is no more required, as AIOps monitors data for each team to streamline the interactions between all groups.

Conclusion

With the help of Microsoft Azure, the value of companies associated with this ecosystem is scaling in an upward direction. To conclude, it can be rightly said that AIOps is the infusion of AI into cloud technology. When properly implemented, AIOps can help reduce time and attention on the IT staff of an organization.

AIOps open-source tools allow Azure cloud professionals to observe multiple systems and resources. With better ML capabilities, it can enable software to find the root cause of a problem and accelerate troubleshooting by providing the right remedies for all unusual issues of an IT organization running on Microsoft Azure.

AI in Monitoring Agents

Posted on by Subhadeep Datta

As per Gartner, “AIOps is the application of machine learning and data science to IT operations problems. AIOps platforms combine big data and ML functionality to enhance and partially replace all primary IT operations functions, including availability and performance monitoring, event correlation and analysis, and IT service management and automation.”

They had predicted that large enterprise exclusive use of AIOps will rise from 5% in 2018 to 30% in 2023. Indeed, we have seen a rapid increase in the adoption of AIOps platforms over the past few years. The acceleration of digital transformation brought on by the pandemic has further reinforced the importance of such platforms.

AIOps typically consists of various components such as monitoring agents, analysis components, AI peripherals, highlighting tools and others. In this article, I will be focusing on the monitoring agent.

Monitoring agents proactively monitor, manage, and resolve performance issues across the entire IT landscape before they impact end-user productivity.

Based on its functioning, monitoring agents can be broadly categorized as below:

  • Fundamental functioning
  • AI functioning

Fundamental Functioning of Monitoring Agents

This covers what we are monitoring, how we are monitoring it, what insights are handled and where it is being stored. In this structure, AI and Prediction components are independent. Here, agents will only act as an observer or the catcher of insights.

The below structure is common for most AIOps tools.

AI Functioning of Monitoring Agent

Apart from all the features of fundamental functioning, monitoring agents also have AI algorithms and process mechanisms for self-intelligences. These algorithms turn a monitoring agent into a reactive machine.

Reactive Machines

Reactive machines are a type of AI that work based on predefined algorithms. It does not have any memory, nor does it have predictive capabilities. Reactive machines will respond to identical situations in the exact same way every time. There will never be a variance in action if the input is the same. This feature is desirable when it must be ensured that the AI system is trustworthy. However, it means they can’t learn from the past.

Spam filters and the Netflix recommendation engine are examples of reactive AI.

Reactive machines work well in scenarios that require pattern recognition and where all parameters are known.

Benefits of replacing basic monitoring agents by reactive machines:

In the monitoring aspect, Reactive machines = Reactive AI Agents

  • Data processing & size deduction

Handling huge data and processing those are extraordinarily complex. Moreover, it keeps on growing and needs to be maintained.

Reactive AI agents have their own intelligence to filter polling data’s properties. All insights’ properties are not always used from raw data. Only a few properties are needed in certain impact cases. But, most monitoring agents, don’t have the intelligence to identify situations (impact cases) to filter properties. Agents post all properties each time of monitoring frequency. If we filter the properties of monitoring data on need basis, it will reduce 5-10% overall data size.

In processing aspect, reactive agents will group repeated common properties of various transaction documents at the same polling time. Reactive AI agents will have the intelligence to know what can be grouped, how can it be grouped, and to maintain raw data’s consistency. Example, at a specific time we are collecting 200+ event details, they all will have a machine name, IP address, location, and few more common properties. These properties are going to be repeated on all 200+ documents and these will be a few gigabytes. If we are grouping those details as an independent single document with that independent id on all 200+ documents, it will get reduced to around 15%-20% overall size.

  • Self-healing

Reactive AI agents have self-healing intelligence to optimize its CPU, memory utilization, in-memory refreshment and handle log recycling based on time and log’s information. Also, it will notify status of its force down situation except for a few exceptional cases.

  • Data Accuracy

Reactive AI agents have an accurate unit conversion based on polling unit. It also realigns frequency with the delay of the data polling cycle.

  • Security

Usually, data encryption happens in static encryption key on agents and back-end components. Reactive AI agents generate a unique encryption key, it is frequently updated (by week or month) and it is notified by back-end calls for decryption process. It is more secure than the static one.

Agent’s configuration details also get periodically obfuscated on its own.

  • Dynamic Polling Frequency

Most of the agent’s polling frequency is static. It could be via a back-end component or the agent’s own configuration. But reactive AI agents have the intelligence to decide frequency changes. It will decide frequency based on data impact (low and high frequency level). This frequency changes are notified to back-end components as well and considered based back-end process’s impact.

Monitoring agents usually do not have any intelligence and algorithms. Implementing AI in monitoring agents is much needed to make it more efficient and make AIOps a true enabler of digital transformation.

References

About the Author –

Natarajan Veerasekaran

Natarajan is a Lead Engineer for ZIF Monitoring at GAVS. He is deeply passionate about programming and broadening his technical boundaries.

AIOps for Service Reliability Engineering (SRE)

Posted on by ZIF

Data is the single most accountable yet siloed component within any IT infrastructure. According to a Gartner report, an average enterprise IT infrastructure generates up to 3 times more IT operational data with each passing year. Large businesses find themselves challenged by frequent unplanned downtime of their services, high IT issue resolution times, and consequently poor user experience caused by inefficient management of this data overload, reactive IT operations, and other reasons such as:

  • Traditional legacy systems that do not scale
  • Siloed environments preventing unified visibility into IT landscape
  • Unattended warning signs due to alert fatigue
  • Lack of advanced tools to intelligently identify root causes of cross-tier events
  • Multiple hand-offs that require manual intervention affecting problem remediation workflow

Managing data and automation with AIOps

The surge of AI in IT operations or AIOps is helping bridge the gap between the need for meaningful insights and human intervention, to ensure service reliability and business growth. AIOps is fast becoming a critical need since effective management of the humongous data volumes has surpassed human capabilities. AIOps is powered by AI/ML algorithms that enable automatic discovery of infra & applications, 360o observability into the entire IT environment, noise reduction, anomaly detection, predictive and prescriptive analytics, and automatic incident triage and remediation!

AIOps provides clear insights into application & infrastructure performance and user experience, and alerts IT on potential outages or performance degradation. AIOps delivers a single, intelligent, and automated layer of intelligence across all IT operations, enabling proactive & autonomous IT operations, improved operational efficiencies through reduction of manual effort/fatigue/errors, and improved user experience as predictive & prescriptive analytics drive consistent service levels.

The Need for AIOps for SRE

SRE mandates that the IT team always stays ahead of IT outages and proactively resolves incidents before they impact the user. However, even the most mature teams face challenges due to the rapidly increasing data volumes and expanding IT boundaries, created by modern technologies such as the cloud, and IoT. SRE faces challenges such as lack of visibility and technology fragmentation while executing these tasks in real-time.

SRE teams have started to leverage AI capabilities to detect & analyze patterns in the data, eliminate noise & gain meaningful insights from current & historical data. As AIOps enters the SRE realm, it has enabled accelerated and automated incident management and resolution. With AI at the core, SRE teams can now redirect their time towards strategic initiatives and focus on delivering high value to users.

Transform SRE with AIOps

SREs are moving towards AIOps to achieve these main goals:

  • Improved visibility across the organization’s remote & distributed systems
  • Reduced response time through automation
  • Prevention of incidents through proactive operations

AIOps Platform ZIFTM from GAVS allows enterprises focused on digital transformation to become proactive with IT incidents, by delivering AI-led predictions and auto-remediation. ZIF is a unified platform with centralized NOC powered by AI-led capabilities for automatic environment discovery, going beyond monitoring to observability, predictive & prescriptive analytics, automation & self-remediation enabling outcomes such as:

  • Elimination of digital dirt
  • IT team empowered with end-to-end visibility
  • Breaking away the silos in IT infrastructure systems and operations
  • Intuitive visualization of application health and user experience from the digital delivery chain
  • Increasing precision in intelligent root cause analyses helping drastic cut in resolution time (MTTR)
  • ML algorithms for continuous learning from the environment driving huge improvements with time
  • Zero-touch automation across the spectrum of services, including delivery of cloud-native applications, traditional mainframes, and process workflows

The future of AIOps

Gartner predicts a rapidly growing market size from USD 1.5 billion in 2020. Gartner also claims that the future of IT operations cannot operate without AIOps due to these four main drivers:

  • Redundancy of traditional approaches to handling IT complexities
  • The proliferation of IoT devices, mobile applications & devices, APIs
  • Lack of infrastructure to support IT events that require immediate action
  • Growth of third-party services and cloud infrastructure

AIOps has a strong role in five major areas — anomaly detection, event correlation and advanced data analysis, performance analysis, automation, and IT service management. However, to get the most out of AIOps, it is crucial to choose the right AIOps platform, as selecting the right partner is critical to the success of such an important org initiative. Gartner recommends prioritizing vendors based on their ability to address challenges, data ingestion & analysis, storage & access, and process automation capabilities. We believe ZIF is that AIOps solution for you! For more on ZIF, please visit www.zif.ai.

Evolving Telemedicine Healthcare with ZIF™

Posted on by Subhadeep Datta

Overview

Telemedicine is a powerful tool that was introduced in the 1950s to make healthcare more accessible and cost effective for the general public. It had helped patients especially in rural areas to virtually consult physicians and get prompt treatment for their illnesses.  

Telemedicine empowers healthcare professionals to gain access to patient information and remotely monitor their vitals in real time.

In layman terms, Telemedicine is the virtual manifestation of the remote delivery of healthcare services. Today, we have 3 types of telemedicine services;

  • Virtual Consultation: Allowing patients and doctors to communicate in real time while adhering to HIPAA compliance
  • EHR Handling:  Empowering providers to legally share patient information with healthcare professionals
  • Remote Patient Monitoring: Enabling doctors to monitor patient vitals remotely using mobile medical devices to read and transmit data.

The demand from a technology embracing population has brought in a higher rate of its adoption today.

Telemedicine can be operated in numerous ways. The standard format is by using a video or voice-enabled call with a HIPAA compliant tool based on the country of operation. There are also other ways in which portable telemedicine kits with computers and medical devices are used for patient monitoring enabled with video.

AIOps based Analytics Platform

Need of the Hour

The COVID-19 pandemic has forced healthcare systems and providers to adapt the situation by adopting telemedicine services to protect both the doctors and patients from the virus. This has entirely changed the scenario of how we will look at healthcare and consultation services going forward. This adoption of the modern telemedicine services has proven to bring in more convenience, cost saving and new intelligent features that enhance the doctor and patient experience and engagement significantly.

The continuous advancements and innovation in technology and healthcare practices significantly improve the usability and adoption of telemedicine across the industry. In the next couple of years, the industry is to see a massive integration of telemedicine services across practices in the country.

it operations analytics platform service

A paper titled, “Telehealth transformation: Covid19 and the rise of Virtual Care” from the journal of the American Medical Informatics Association, analyzes the adoption of telemedicine in different phases during the pandemic.

During the initial phase of the pandemic when the lockdown was enforced, telemedicine found the opportunity to scale as per the situation. It dramatically decreased the proportion of in-person care and clinical visits to reduce the community spread of the virus.

As the causalities from the pandemic intensified, there was a peak in demand for inpatient consultations with the help of TeleICUs. This was perfectly suited to meet the demands of inpatient care while reducing the virus spread, expanding human and technical resources, and protecting the healthcare professionals.

With the pandemic infection rates stabilizing, telemedicine was proactive in engaging with patients and effectively managing the contingencies. As restrictions relaxed with declining infection rates, the systems will see a shift from a crisis mode to a sustainable and secure system that preserve data security and patient privacy.

The Future of Telemedicine

With the pandemic economy serving as an opportunity to scale, telemedicine has evolved to a cost effective and sustainable system. The rapid advances in technology enable telemedicine to evolve faster.

The future of Telemedicine revolves around Augmented reality with the virtual interactions simulated in the same user plane. Both Apple and Facebook are experimenting with their AR technology and are expected to make a launch soon.

Now Telemedicine platforms are evolving like service desks, to measure efficiency and productivity. This helps to track the value realizations contributed to the patients and the organization.

The ZIF™ Empowerment

ZIF™ helps customers scale their telemedicine system to be more effective and efficient. It empowers the organization to manage healthcare professionals and customer operations in a New Age Digital Service Desk platform. ZIF™ is a HIPAA compliant platform and leverages the power of AI led automation to optimize costs, automate workflows and bring in an overall productivity efficiency.

ZIF™ keeps people, processes and technology in sync for operational efficiency. Rather than focusing on traditional SLAs to measure performance, the tool focuses more on end user experience and results with the help of insights to improve each performance parameter.

Here are some of the features that can evolve your existing telemedicine services.

AIOps based Predictive and Prescriptive Analytics Platform

Patient engagements can be assisted with consultation recommendations with their treatment histories. The operations can be streamlined with higher productivity with quicker decision making and resolutions. A unified dashboard helps to track performance metrics and sentiment analytics of the patients.

AI based Voice Assistants and Chatbots

Provide consistent patient experience and reduce the workload of healthcare professionals with responses and task automations.

Social Media Integration

Omnichannel engagement and integration of different channels for healthcare professionals to interact with their patients across social media networks and instant messaging platforms.

Automation

ZIF™ bots can help organizations automate their workflow processes through intuitive activity-based tools. The tool offers over 200+ plug and play workflows for consultation requests and incident management.

Virtual Supervisor

The Native machine learning algorithms aid in initial triaging of patient consultation requests to the right healthcare professional with its priority assignment and auto rerouting tickets to the appropriate healthcare professional groups.

ZIF™ empowers healthcare organizations to transform and scale to the changing market scenarios. If you are looking for customized solutions for your telemedicine services with the help of ZIF™, feel free to schedule a Demo with us today.

https://zif.ai/

About the Author –

Ashish Joseph

Ashish Joseph is a Lead Consultant at GAVS working for a healthcare client in the Product Management space. His areas of expertise lie in branding and outbound product management.

He runs two independent series called BizPective & The Inside World, focusing on breaking down contemporary business trends and Growth strategies for independent artists on his website www.ashishjoseph.biz

Outside work, he is very passionate about basketball, music, and food.