How ZIF Delivers Service Reliability to Financial Institutions Using AIOps

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. 
  • MTTDZIF 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! 

Why You Should Outsource Your AIOps Needs

Are you scaling up the IT infrastructure for your business? Well, upscaling IT infrastructure comes with its challenges. You will need more employees to manage the IT operations effectively. This is where AIOps come into action. AIOps (Artificial Intelligence for IT Operations) is being adopted by firms to automate their key IT processes. Read on to know more about AIOps and why you should outsource your AIOps needs. 

What is AIOps?

AIOps is a new-age solution for IT operations that works on smart algorithms. The smart algorithms behind an AIOps platform are powered by artificial intelligence and machine learning. AIOps platforms for businesses are multi-layered platforms that reduce human intervention. It not only automates mundane IT tasks but also increases productivity. Repetitive IT tasks like performance monitoring, event correlation, and others can be automated via AIOps. 

AIOps is capable of managing the ever-growing IT infrastructure for a business. A business may not require the services of system administrators after using AIOps. AIOps is also capable of handling high volumes of business data that are always increasing. The data generated by IT processes can be easily analyzed via AIOps. This helps the management to access meaningful insights and make informed decisions.

Why does my business need AIOps?

AIOps tools are beneficial for a business and can boost productivity and administration. The main reasons that highlight the importance of AIOps tools for your business are as follows: 

  • Digitalization: Every business wants to dive into this new era of digitalization. With digital transformation, you can save time, effort, and money. AIOps can help in enhancing the visibility of the IT infrastructure and digital applications in your organization. 
  • Cloud enablement: IT services and applications can be deployed and operated via the cloud. AIOps can help you with enabling IT services via the cloud for your business. You can also automate cloud operations and can also monitor the health of the cloud system. 
  • Easy deployment: Organizations perform IT monitoring to identify the issues in the IT infrastructure. When an issue is detected, it takes hours to mitigate it and get the system online. With AIOps, you can automate the actions in response to IT issues thus saving time and effort. 
  • MTTD and MTTR: MTTD (Mean Time to Detect) and MTTR (Mean Time to Resolve) are important metrics for organizations to solve problems like system outages. With AIOps, you can reduce the MTTD and can identify issues quickly. Reduced MTTD via AIOps will help in increasing the uptime of your system software(s). 
  • Real-time analysis and automation: AIOps platforms record and IT data produced by the system software(s). It applies various algorithms to the data in real-time to produce meaningful insights. With AIOps, you can diagnose issues in real-time with the help of actionable insights. 
  • Security AutomationAIOps can help you automate the first-level incident response for your systems. It can also help with virus elimination and access management. You can pre-define a response to any particular system issue and it will be automatically applied next time via an AIOps platform. 

These were some of the main business processes that can be automated with the aid of AIOps. AIOps has diverse applications and can help in better administration and management of system software(s). According to studies, around 30% of businesses will be using AIOps for monitoring applications and business infrastructure by 2023. You can also outsource your AIOps needs and ensure better business resilience and continuity. 

Why outsource AIOps processes?

Developing and deploying an AIOps platform requires knowledge about the new-age technologies. It is hard to find AI/ML experts that can work full-time for your business. A reliable third-party that offers AIOps solutions will already have AI/ML experts. You don’t have to go through the recruitment process to hire in-house AI/ML experts.

If you go for recruiting AIOps experts, you will have to spend funds for recruitment and training. By outsourcing your AIOps needs, you can save money and also time. It will also be beneficial in the long run as you can automate key business processes via AIOps. IT operations are often affected by the high volume of data produced every day. AIOps can help team leaders to analyze this data and act upon it.

Different IT teams work on their respective operations and it makes it tough to address any immediate incident. Outsourcing your AIOps needs will help you in automating responses to such urgent incidents. Your full-time employees will have to put less effort into ensuring resilience and business continuity. 

How to start outsourcing my AIOps needs? 

The recent COVID pandemic has influenced various market disruptions. Organizational workplaces were also affected due to the COVID pandemic. System administrators are finding it hard to monitor the system software(s) remotely. It is better to adopt AIOps for the automation of system software(s). Some of the tips for outsourcing your AIOps needs are as follows: 

  • Adapt AIOps for smaller IT operations first that require fewer efforts. This way you will start small and can see the immediate benefits of AIOps. Once AIOps is successful for your initial test cases, you can apply the same to other IT operations. 
  • Look for areas that require more human effort and are costing you a lot. Such IT operations can be automated via AIOps. You can use your skilled workforce for other business processes. 
  • Free AIOps platforms are also available in the market but are not capable of handling complex IT operations. You should focus on building a customized AIOps platform for your business that can resolve complex operational issues. 
  • Partner with a reliable outsourcing firm that offers an effective AIOps platform
  • Influence your employees and stakeholders to use AI-based technologies for better business performance and uptime. 
  • Identify IT areas with greater downtime and apply AIOps for those operations first. 

In a nutshell 

The global AI market size will be more than $260 billion by 2027. More and more businesses are using AIOps for ensuring business continuity and sustainability. You can outsource your AIOps needs for cost optimization and reducing manual efforts. Choose an AIOps platform for your business! 

Large Language Models: A Leap in the World of Language AI

In Google’s latest annual developer conference, Google I/O, CEO Sundar Pichai announced their latest breakthrough called “Language Model for Dialogue Applications” or LaMDA. LaMDA is a language AI technology that can chat about any topic. That’s something that even a normal chatbot can do, then what makes LaMDA special?

Modern conversational agents or chatbots follow a narrow pre-defined conversational path, while LaMDA can engage in a free-flowing open-ended conversation just like humans. Google plans to integrate this new technology with their search engine as well as other software like voice assistant, workplace, gmail, etc. so that people can retrieve any kind of information, in any format (text, visual or audio), from Google’s suite of products. LaMDA is an example of what is known as a Large Language Model (LLM).

Introduction and Capabilities

What is a language model (LM)? A language model is a statistical and probabilistic tool that determines the probability of a given sequence of words occurring in a sentence. Simply put, it is a tool that is trained to predict the next word in a sentence. It works like how a text message autocompletes works. Where weather models predict the 7-day forecast, language models try to find patterns in the human language, one of computer science’s most difficult puzzles as languages are ever-changing and adaptable.

A language model is called a large language model when it is trained on enormous amount of data. Some of the other examples of LLMs are Google’s BERT and OpenAI’s GPT-2 and GPT-3. GPT-3 is the largest language model known at the time with 175 billion parameters trained on 570 gigabytes of text. These models have capabilities ranging from writing a simple essay to generating complex computer codes – all with limited to no supervision.

Limitations and Impact on Society

As exciting as this technology may sound, it has some alarming shortcomings.

1. Biasness: Studies have shown that these models are embedded with racist, sexist, and discriminatory ideas. These models can also encourage people for genocide, self-harm, and child sexual abuse. Google is already using an LLM for its search engine which is rooted in biasness. Since Google is not only used as a primary knowledge base for general people but also provides an information infrastructure for various universities and institutions, such a biased result set can have very harmful consequences.

2. Environmental impact: LLMs also have an outsize impact on the environment as these emit shockingly high carbon dioxide – equivalent to nearly five times the lifetime emissions of an average car including manufacturing of the car.

3. Misinformation: Experts have also warned about the mass production of misinformation through these models as because of the model’s fluency, people can confuse into thinking that humans have produced the output. Some models have also excelled at writing convincing fake news articles.

4. Mishandling negative data: The world speaks different languages that are not prioritized by Silicon Valley. These languages are unaccounted for in the mainstream language technologies and hence, these communities are affected the most. When a platform uses an LLM which is not capable of handling these languages to automate its content moderation, the model struggles to control the misinformation. During extraordinary situations, like a riot, the amount of unfavorable data coming in is huge, and this ends up creating a hostile digital environment. The problem does not end here. When the fake news, hate speech, and all such negative text is not filtered, it is used as training data for the next generation of LLMs. These toxic linguistic patterns then parrot back on the internet.

Further Research for Better Models

Despite all these challenges, very little research is being done to understand how this technology can affect us or how better LLMs can be designed. In fact, the few big companies that have the required resources to train and maintain LLMs refuse or show no interest in investigating them. But it’s not just Google that is planning to use this technology. Facebook has developed its own LLMs for translation and content moderation while Microsoft has exclusively licensed GPT-3. Many startups have also started creating products and services based on these models.

While the big tech giants are trying to create private and mostly inaccessible models that cannot be used for research, a New York-based startup, called Hugging Face, is leading a research workshop to build an open-source LLM that will serve as a shared resource for the scientific community and can be used to learn more about the capabilities and limitations of these models. This one-year-long research (from May 2021 to May 2022) called the ‘Summer of Language Models 21’ (in short ‘BigScience’) has more than 500 researchers from around the world working together on a volunteer basis.

The collaborative is divided into multiple working groups, each investigating different aspects of model development. One of the groups will work on calculating the model’s environmental impact, while another will focus on responsible ways of sourcing the training data, free from toxic language. One working group is dedicated to the model’s multilingual character including minority language coverage. To start with, the team has selected eight language families which include English, Chinese, Arabic, Indic (including Hindi and Urdu), and Bantu (including Swahili).

Hopefully, the BigScience Project will help produce better tools and practices for building and deploying LLMs responsibly. The enthusiasm around these large language models cannot be curbed but it can surely be nudged in a direction that has lesser shortcomings. Soon enough, all our digital communications—be it emails, search results, or social media posts —will be filtered using LLMs. These large language models are the next frontier for artificial intelligence.

References

About the Author –

Priyanka Pandey

Priyanka is a software engineer at GAVS with a passion for content writing. She is a feminist and is vocal about equality and inclusivity. She believes in the cycle of learning, unlearning and relearning. She likes to spend her free time baking, writing and reading articles especially about new technologies and social issues.

Evolving Telemedicine Healthcare with ZIF™

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.

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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.

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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.

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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.

Balancing Management Styles for a Remote Workforce

Operational Paradigm Shift

The pandemic has indeed impelled organizations to rethink the way they approach traditional business operations. The market realigned businesses to adapt to the changing environment and optimize their costs. For the past couple of months, nearly every organization implemented work for home as a mandate. This shift in operations had both highs and lows in terms of productivity. Almost a year into the pandemic, the impacts are yet to be fully understood. The productivity realized from the remote workers, month on month, shaped the policies and led to investments in different tools that aided collaboration between teams. 

Impact on Delivery Centers

Technology companies have been leading the charge towards remote working as many have adopted permanent work from home options for their employees. While identifying cost avenues for optimization, office space allocation and commuting costs are places where redundant operational cash flow can be invested to other areas for scaling.

The availability and speed of internet connections across geographies have aided the transformation of office spaces for better utilization of the budget. Considering the current economy, office spaces are becoming expensive and inefficient. The Annual Survey by JLL Enterprises in 2020 reveals that organizations spend close to $10,000 on global office real estate cost per employee per year on an average. As offices have adopted social distancing policies, the need for more space per employee would result in even higher costs during these pandemic operations. To optimize their budgets, companies have reduced their allocation spaces and introduced regional contractual sub-offices to reduce the commute expenses of their employees in the big cities. 

With this, the notion of a 9-5 job is slowly being depleted and people have been paid based on their function rather than the time they spend at work. The flexibility of working hours while linking their performance to their delivery has seen momentum in terms of productivity per resource. An interesting fact that arose out of this pandemic economy is that the number of remote workers in a country is proportional to the country’s GDP. A work from home survey undertaken by The Economist in 2020 finds that only 11% of work from home jobs can be done in Cambodia, 37% in America, and 45% in Switzerland. 

The fact of the matter is that a privileged minority has been enjoying work from home for the past couple of months. While a vast majority of the semi-urban and rural population don’t have the infrastructure to support their functional roles. For better optimization and resource utilization, India would need to invest heavily in these resources to catch up on the deficit GDP from the past couple of quarters.

Long-term work from home options challenges the foundational fabric of our industrial operations. It can alter the shape and purpose of cities, change workplace gender distribution and equality. Above all, it can change how we perceive time, especially while estimating delivery. 

Overall Pulse Analysis

Many employees prefer to work from home as they can devote extra time to their family. While this option has been found to have a detrimental impact on organizational culture, creativity, and networking. Making decisions based on skewed information would have an adverse effect on the culture, productivity, and attrition. 

To gather sufficient input for decisions, PWC conducted a remote work survey in 2020 called “When everyone can work from home, what’s the office for“. Here are some insights from the report

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Many businesses have aligned themselves to accommodate both on-premise and remote working model. Organizations need to figure out how to better collaborate and network with employees in ways to elevate the organization culture. 

As offices are slowly transitioning to a hybrid model, organizations have decentralized how they operate. They have shifted from working in a common centralized office to contractual office spaces as per employee role and function, to better allocate their operational budget. The survey found that 72% of the workers would like to work remotely at least 2 days a week. This showcases the need for a hybrid workspace in the long run. 

Maintaining & Sustaining Productivity

During the transition, keeping a check on the efficiency of remote workers was prime. The absence of these checks would jeopardize the delivery, resulting in a severe impact on customer satisfaction and retention.

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This number however, could be far less if the scale of the survey was higher. This in turn signifies that productivity is not uniform and requires course corrective action to maintain the delivery. An initial approach from an employee’s standpoint would result in higher results. The measures to help remote workers be more productive were found to be as follows.

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Many employees point out that greater flexibility of working hours and better equipment would help increase work productivity.

Most of the productivity hindrances can be solved by effective employee management. How a particular manager supervises their team members has a direct correlation towards their productivity and satisfaction to the project delivery. 

Theory X & Theory Y

Theory X and Theory Y were introduced by Douglas McGregor in his book, “The Human Side of Enterprise”. He talks about two styles of management in his research – Authoritarian (Theory X) and Participative (Theory Y). The theory heavily believes that Employee Beliefs directly influence their behavior in the organization. The approach that is taken by the organization will have a significant impact on the ability to manage team members. 

For theory X, McGregor speculates that “Without active intervention by management, people would be passive, even resistant to organizational needs. They must therefore be persuaded, rewarded, punished, controlled and their activities must be directed”

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Work under this style of management tends to be repetitive and motivation is done based on a carrot and stick approach. Performance Appraisals and remuneration are directly correlated to tangible results and are often used to control staff and keep tabs on them. Organizations with several tiers of managers and supervisors tend to use this style. Here authority is rarely delegated, and control remains firmly centralized. 

Even though this style of management may seem outdated, big organizations find it unavoidable to adopt due to the sheer number of employees on the payroll and tight delivery deadlines.

When it comes to Theory Y, McGregor firmly believes that objectives should be arranged so that individuals can achieve their own goals and happily accomplish the organization’s goal at the same time.

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Organizations that follow this style of management would have an optimistic and positive approach to people and problems. Here the team management is decentralized and participative.

Working under such organizational styles bestow greater responsibilities on employees and managers encourage them to develop skills and suggest areas of improvement. Appraisals in Theory Y organizations encourage open communication rather than to exercise control. This style of management has been popular these days as it results in employees wanting to have a meaningful career and looking forward to things beyond money.

Balancing X over Y

Even though McGregor suggests that Theory Y is better than Theory X. There are instances where managers would need to balance the styles depending upon how the team function even post the implementation of certain management strategies. This is very important from a remote working context as the time for intervention would be too late before it impacts the delivery. Even though Theory Y comprises creativity and discussion in its DNA, it has its limitations in terms of consistency and uniformity. An environment with varying rules and practices could be detrimental to the quality and operational standards of an organization. Hence maintaining a balance is important.

When we look at a typical cycle of Theory X, we can find that the foundational beliefs result in controlling practices, appearing in employee resistance which in turn delivers poor results. The results again cause the entire cycle to repeat, making the work monotonous and pointless. 

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Upon the identification of resources that require course correction and supervision, understanding the root cause and subsequently adjusting your management style to solve the problem would be more beneficial in the long run. Theory X must only be used in dire circumstances requiring a course correction. The balance where we need to maintain is on how far we can establish control to not result in resistance which in turn wouldn’t impact the end goal.

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Theory X and Theory Y can be directly correlated to Maslow’s hierarchy of Needs. The reason why Theory Y is superior to Theory X is that it focuses on the higher needs of the employee than their foundational needs. The theory Y managers gravitate towards making a connection with their team members on a personal level by creating a healthier atmosphere in the workplace. Theory Y brings in a pseudo-democratic environment, where employees can design, construct and publish their work in accordance with their personal and organizational goals.

When it comes to Theory X and Theory Y, striking a balance will not be perfect. The American Psychologist Bruce J Avolio, in his paper titled “Promoting more integrative strategies for leadership theory-building,” speculates, “Managers who choose the Theory Y approach have a hands-off style of management. An organization with this style of management encourages participation and values an individual’s thoughts and goals. However, because there is no optimal way for a manager to choose between adopting either Theory X or Theory Y, it is likely that a manager will need to adopt both approaches depending on the evolving circumstances and levels of internal and external locus of control throughout the workplace”.

The New Normal 3.0

As circumstances keep changing by the day, organizations need to adapt to the rate at which the market is changing to envision new working models that take human interactions into account as well. The crises of 2020 made organizations build up their workforce capabilities that are critical for growth. Organizations must relook at their workforce by reskilling them in different areas of digital expertise as well as emotional, cognitive, and adaptive skills to push forward in our changing world.

Ashish Joseph

About the Author –

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.

AIOps Myth Busters

The explosion of technology & data is impacting every aspect of business. While modern technologies have enabled transformational digitalization of enterprises, they have also infused tremendous complexities in infrastructure & applications. We have reached a point where effective management of IT assets mandates supplementing human capabilities with Artificial Intelligence & Machine Learning (AI/ML).      

AIOps is the application of Artificial Intelligence (AI) to IT operations (Ops). AIOps leverages AI/ML technologies to optimize, automate, and supercharge all aspects of IT Operations. Gartner predicts that the use of AIOps and digital experience monitoring tools for monitoring applications and infrastructure would increase by 30% in 2023. In this blog, we hope to debunk some common misconceptions about AIOps.

MYTH 1: AIOps mainly involves alert correlation and event management

AIOps can deliver enormous value to enterprises that harness the wide range of use cases it comes with. While alert correlation & management are key, AIOps can add a lot of value in areas like monitoring, user experience enhancement, and automation.  

AIOps monitoring cuts across infrastructure layers & silos in real-time, focusing on metrics that impact business outcomes and user experience. It sifts through monitoring data clutter to intelligently eliminate noise, uncover patterns, and detect anomalies. Monitoring the right UX metrics eliminates blind spots and provides actionable insights to improve user experience. AIOps can go beyond traditional monitoring to complete observability, by observing patterns in the IT environment, and externalizing the internal state of systems/services/applications. AIOps can also automate remediation of issues through automated workflows & standard operating procedures.

MYTH 2: AIOps increases human effort

Forbes says data scientists spend around 80% of their time preparing and managing data for analysis. This leaves them with little time for productive work! With data pouring in from monitoring tools, quite often ITOps teams find themselves facing alert fatigue and even missing critical alerts.

AIOps can effectively process the deluge of monitoring data by AI-led multi-layered correlation across silos to nullify noise and eliminate duplicates & false positives. The heavy lifting and exhausting work of ingesting, analyzing, weeding out noise, correlating meaningful alerts, finding the probable root causes, and fixing them, can all be accomplished by AIOps. In short, AIOps augments human capabilities and frees up their bandwidth for more strategic work.

MYTH 3: It is hard to ‘sell’ AIOps to businesses

While most enterprises acknowledge the immense potential for AI in ITOps, there are some concerns that are holding back widespread adoption. The trust factor with AI systems, the lack of understanding of the inner workings of AI/ML algorithms, prohibitive costs, and complexities of implementation are some contributing factors. While AIOps can cater to the full spectrum of ITOps needs, enterprises can start small & focus on one aspect at a time like say alert correlation or application performance monitoring, and then move forward one step at a time to leverage the power of AI for more use cases. Finding the right balance between adoption and disruption can lead to a successful transition.  

MYTH 4: AIOps doesn’t work in complex environments!

With Machine Learning and Big Data technologies at its core, AIOps is built to thrive in complex environments. The USP of AIOps is its ability to effortlessly sift through & garner insights from huge volumes of data, and perform complex, repetitive tasks without fatigue. AIOps systems constantly learn & adapt from analysis of data & patterns in complex environments. Through this self-learning, they can discover the components of the IT ecosystem, and the complex network of underlying physical & logical relationships between them – laying the foundation for effective ITOps.   

MYTH 5: AIOps is only useful for implementing changes across IT teams

An AIOps implementation has an impact across all business processes, and not just on IT infrastructure or software delivery. Isolated processes can be transformed into synchronized organizational procedures. The ability to work with colossal amounts of data; perform highly repetitive tasks to perfection; collate past & current data to provide rich inferences; learn from patterns to predict future events; prescribe remedies based on learnings; automate & self-heal; are all intrinsic features that can be leveraged across the organization. When businesses acknowledge these capabilities of AIOps and intelligently identify the right target areas within their organizations, it will give a tremendous boost to quality of business offerings, while drastically reducing costs.

MYTH 6: AIOps platforms offer only warnings and no insights

With its ability to analyze and contextualize large volumes of data, AIOps can help in extracting relevant insights and making data-driven decisions. With continuous analysis of data, events & patterns in the IT environment – both current & historic – AIOps acquires in-depth knowledge about the functioning of the various components of the IT ecosystem. Leveraging this information, it detects anomalies, predicts potential issues, forecasts spikes and lulls in resource utilization, and even prescribes appropriate remedies. All of this insight gives the IT team lead time to fix issues before they strike and enables resource optimization. Also, these insights gain increasing precision with time, as AI models mature with training on more & more data.

MYTH 7: AIOps is suitable only for Operations

AIOps is a new generation of shared services that has a considerable impact on all aspects of application development and support. With AIOps integrated into the dev pipeline, development teams can code, test, release, and monitor software more efficiently. With continuous monitoring of the development process, problems can be identified early, issues fixed, and changes rolled back as appropriate. AIOps can promote better collaboration between development & ops teams, and proactive identification & resolution of defects through AI-led predictive & prescriptive insights. This way AIOps enables a shift left in the development process, smarter resource management, and significantly improves software quality & time to market.  

Augmented Analytics with SAP Analytics Cloud

Augmented Analytics

In 2017, Gartner coined the term ‘augmented analytics’ and claimed it would be the future of data analytics. They predicted it would be a dominant driver of new purchases of analytics and business intelligence as well as data science and machine learning platforms, and of embedded analytics.

Here is the why and how.

Most organizations depend on data to back up its decision-making and strategy. Organizations collect data on all accounts of processes and events; thus, analyzing and effectively managing the breadth of this data is challenging yet significant for mining it for business insights.

Traditional business intelligence tools have given way to a new generation of business intelligence tools – Augmented Analytics technology.

Augmented Analytics is an approach of data analytics that employs machine learning (ML) and natural language processing (NLP) to automate and improve data access and data quality, uncover hidden patterns and correlations in data, pinpoint what’s driving results, predict future results and suggest actions to maximize or minimize desirable or undesirable outcomes.

Augmented Analytics is designed to conduct analyses and generate business insights automatically with little to no supervision and can be used without needing the assistance of a business analyst or data scientist. However, the focus of Augmented Analytics stays in its assistive role, where technology does not replace humans but supports them.

Evolution of Analytics

Business Intelligence (BI) and Analytics has evolved, increasing the demand for decision making through data analytics. It drives to unfold from traditional mirror reporting into self-service Business Intelligence and analytics.

Despite the advances in self-service analytics with agile discovery, many businesses demand assistance to uncover insights in data.

The next generation of BI and analytics products are augmented with artificial intelligence (AI) including ML, which automates complex analytics processes, and NLP makes it easier for users without knowledge of data science or query languages to obtain insights.

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Augmented analytics offer starting-point suggestions and guidance to the users. It also empowers businesses to leverage more of their data to make better decisions when compared to the traditional and self-service Business Intelligence.

SAP Analytics Cloud

SAP Analytics Cloud (SAC) is an analytical solution that features all the analytics functionalities like business intelligence, augmented analytics, predictive analytics, enterprise planning, and application building in one intuitive user interface. It is empowered with ML and built-in AI that helps discover in-depth insights, simplify access to critical information and enable adequate decision-making.

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Augmented SAP Analytics Cloud

Augmented analytics capabilities offered by SAP Analytics Cloud empowers business intelligence to reap the benefits of AI and ML.

SAP Analytics Cloud facilitates users to interact with the system using natural language to gather automatic insights, where Predictive Scenarios offer an accessible way into Predictive Analytics using the past data to foresee the future.

Let’s look at the Analytics features, and capabilities offered by SAP Analytics Cloud

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Search to Insight – Query search in Natural Language

The Search to Insight feature enables query search through natural language through conversational AI and NLP. No knowledge of query languages like SQL, R, or Python is required. Asking questions just like in a search engine or digital personal assistant fetches insightful answers represented by visualization or numeric values tailored based on the question type.

Search to Insight provides auto-complete suggestions to match words or phrases in questions for measures and dimensions in the data and includes auto spell-check.

AIOps Artificial Intelligence for IT Operations

Smart Insights – Instant explanations

The Smart Insights feature facilitates digging deeper into the data points. It analyzes the underlying dataset and runs various statistical algorithms to offer insights based on the current user context.

It helps to understand top contributors of specific data points without having to manually pivot or slice and dice the data. When a data point is selected, ML calculations run on information that is of the same nature as the selected data point. For example, if the selected data point is ‘Total Revenue’, the top contributors are based on ‘Total Revenue’. It analyzes the dimension in the selected data and looks for members in these dimensions that influence the selected value.

Smart Discovery – Easily reveal insights

The Smart Discovery feature identifies hidden patterns and statically relevant relationships in the data to discover how business factors influence performance. It helps to understand the business drivers behind the core KPIs.

Based on the selection of measure or dimension, smart discovery automatically generates interactive story pages as below –

Overview: It explains the data distribution, summary of trends, and the detected patterns for the target dimension or measure.

Key Influencers:  It explains the influence of the dimensions for the value of the target measures in the context of the selected model using classification and regression techniques, where the classification techniques are used to identify dimensions that segregate results into different groups of results and the regression techniques identify relationships between data points to predict future results.

Unexpected Values: It displays the details about outliers, where the actual values differ greatly from what the predictive model would expect. If an actual value diverges from the regression line it is categorized as unexpected.

Simulation: The simulation facilitates the ‘what-if’ analysis, users can change the values of the measures and dimensions to see the predicted change positively, negatively, or neutrally in the target measure.

Smart Predict – Answers the toughest questions

Smart Predict feature predicts the likelihood of different outcomes based on the historical data using techniques such as data mining, statistics, machine learning, and artificial intelligence.

Smart Predict, also referred as Predictive forecasting, considers different values, trends, cycles, and/or fluctuations in the data to make predictions that can be leveraged to aid business planning processes.

Smart Predict provides 3 different predictive scenario options for selection

Classification: It can be used to generate predictions for a binary event. For example, whether individual customers would be likely to buy the target product or not.

Time Series: It can be used to forecast values over a set period. For example, forecasting the sales of product by month or week, using historical data.

Regression: It can be used to predict values and explore key values behind them. For example, predicting the price of an imported product based on projected duties or shipping charges.

In the modern world of business Intelligence, SAP Analytics cloud’s ML technology augments the analytic process which assists from insights to actions and enables avoiding the agenda-driven and biased decision making by revealing the accurate patterns which drives the business.

References

MF Kashif

About the Author –

Kashif is a SAP Business objects consultant and a business analytics enthusiast. He believes that the “Ultimate goal is not about winning, but to reach within the depth of capabilities and to compete against yourself to be better than what you are today.”

Customize Business Outcomes with ZIFTM

Zero Incident Framework™ (ZIF) is the only AIOps platform that is powered with true machine learning algorithms with the capability to self-learn and adapt to today’s modern IT infrastructure.

ZIF’s goal has always been to deliver the right business outcomes for the stakeholders. Return on investment can be measured based on the outcomes the platform has delivered. Users get to choose what business outcomes are expected from the platform and the respective features are deployed in the enterprise to deliver the chosen outcome.

Single Pane of Action – Unified View across IT Enterprise

The biggest challenge IT Operations teams have been trying to tackle over the years is to get a bird’s eye view on what is happening across their IT landscape. The more complex the enterprise becomes the harder it becomes for the IT Operations team to understand what is happening across their enterprise. ZIF solves this issue with ease.

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The capability to ingest data from any source monitoring or ITSM tool has helped IT organizations to have a real-time view of what is happening across their landscape. Enormous time can be saved by the IT engineers with ZIF’s unified view, who would otherwise be traversing between multiple monitoring tools.

ZIF can integrate with 100+ tools to ingest (static/dynamic) data in real-time via ZIF Universal Connector. This is a low code component of ZIF and dataflows within the connector can also be templatized for reuse. 

AIOps based Analytics Platform

Intelligence – Reduction in MTTR – Correlation of Alerts/Events

Approximately 80% of the time is lost by IT engineers in identifying the problem statement for an incident. This has been costing billions of dollars for enterprises. ZIF, with the help of Artificial Intelligence, can reduce the mean time to identify the probable root cause of the incident within seconds. The high-performance correlation engine that runs under the hood of the platform process millions of patterns that the platform has learned from the historical data and correlates the sequences that are happening in real-time and creates cases. These cases are then assigned to IT engineers with the probable root cause for them to fix the issue. This increases the productivity of the IT engineers resulting in better revenue for organizations.

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Intelligence – Predictive Analytics

AIOps platforms are incomplete without the Predictive Analytics capability. ZIF has adopted unsupervised machine learning algorithms to perform predictive analytics on the utilization data that is ingested into the platform. These algorithms can learn trends and understand the symptoms of an incident by analyzing tons of data that the platform had consumed over a period. Based on the analysis, the platform generates opportunity cards that help IT engineers take proactive measures on the forecasted incident. These opportunity cards are generated a minimum of 60 minutes in advance which gives the engineers a lead time to fix an issue before it strikes the landscape.

Visibility – Auto-Discovery of IT Assets & Applications

ZIF agentless discovery is a seamless discovery component, that helps in identifying all the IP assets that are available in an enterprise. Just not discovering the assets, but the component also plots a physical topology & logical map for better consumption of the IT engineers. This gives a very detailed view of every asset in the IT landscape. The logical topology gives in-depth insights into the workload metrics that can be utilized for deep analytics.

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Visibility – Cloud Monitoring

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In today’s digital transformation journey, cloud is inevitable. To have a better control over the cloud orchestrated application, enterprises must depend on the monitoring tools provided by cloud providers. The lack of insights often leads to the unavailability of applications for end-users. More than monitoring, insights that help enterprises take better-informed decisions are the need of the hour.

ZIF’s cloud monitoring components can monitor any cloud instance. Data that are generated from the provider provided monitoring tools are ingested into ZIF to further analyze the data. ZIF can connect to Azure, AWS & Google Cloud to derive data-driven insights.

Optimization – Remediation – Autonomous IT Operations

ZIF does not stop by just providing insights. The platform deploys the right automation bot to remediate the incident.

ZIF has 250+ automation bots that can be deployed to fast-track the resolution process by a minimum of 90%. Faster resolutions result in increased uptime of applications and better revenue for the enterprise.

Sample ZIF bots:

  • Service Restart / VM Restart
  • Disk Space Clean-up
  • IIS Monitoring App Pool
  • Dynamic Resource Allocation
  • Process Monitoring & Remediation
  • DL & Security Group Management
  • Windows Event Log Monitoring
  • Automated phishing control based on threat score
  • Service request automation like password reset, DL mapping, etc.
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For more information on ZIF, please visit www.zif.ai

About the Author –

Anoop Aravindakshan

An evangelist of Zero Incident FrameworkTM, Anoop has been a part of the product engineering team for long and has recently forayed into product marketing. He has over 14 years of experience in Information Technology across various verticals, which include Banking, Healthcare, Aerospace, Manufacturing, CRM, Gaming, and Mobile.

Addressing Web Application Performance Issues

With the use of hybrid technologies and distributed components, the applications are becoming increasingly complex. Irrespective of the complexity, it is quite important to ensure the end-user gets an excellent experience in using the application. Hence, it is mandatory to monitor the performance of an application to provide greater satisfaction to the end-user.

External factors

When the web applications face performance issues, here are some questions you need to ask:

  • Does the application always face performance issues or just during a specific period?
  • Whether a particular user or group of users face the issue or is the problem omnipresent for all the users?
  • Are you treating your production environment as real production environment or have you loaded it with applications, services, and background processes running without any proper consideration?
  • Was there any recent release to any of the application stack like Web, Middle Tier, API, DB, etc., and how was the performance before this release?
  • Have there been any hardware or software upgrades recently?

Action items on the ground

Answering the above set of questions would have brought you closer to the root cause. If not, given below are some steps you can do to troubleshoot the performance issue:

  • Look at the number of incoming requests, is the application facing unusual load?
  • Identify how many requests are delaying more than a usual level, say more than 5000 milliseconds to serve a request, or a web page.
  • Is the load getting generated by a specific or group of users – is someone trying to create intentional load?
  • Look at the web pages/methods/functions in the source code which are taking more time. Check the logs of the web server, this can be identified provided the application does that level of custom logging.
  • Identify whether any 3rd party links or APIs which are being used in the application is causing slowness.
  • Check whether the database queries are taking more time.
  • Identify whether the problem is related to a certain browser.
  • Check if the server side or client side is facing any uncaught exceptions which are impacting the performance.
  • Check the performance of the CPU, Memory, and Disk of the server(s) in which the application is hosted.
  • Check the sibling processes which are consuming more Memory/CPU/Disk in all servers and take appropriate action depending on whether those background processes need to be in that server or can be moved somewhere or can be removed totally.
  • Look at the web server performance to fine tune the Cache, Session time out, Pool size, and Queue-length.
  • Check for deadlock, buffer hit ratio, IO Busy, etc. to fine tune the performance.

Challenges 

  • Doing all these steps exactly when there is a performance issue may not be practically all the time. By the time you collect some of these, you may lose important data for the rest of the items unless the history data is collected and stored for reference.
  • Even if the data is collected, correlating them to arrive at the exact root cause is not an easy task
  • You need to be tech savvy across all layers to know what parameters to collect and how to collect.

And the list of challenges goes on…

Think of an ideal situation where you have metrics of all these action items described above, right in front of you. Is there such magic bullet available? Yes, Zero Incident FrameworkTM Application Performance Monitoring (ZIF APM), it gives you the above details at your fingertips, thereby makes troubleshooting a simple task.

ZIF APM has more to offer than other regular APM. The APM Engine has built-in AI features. It monitors the application across all layers, starting from end-user, web application, web server, API layers, databases, underlying infrastructure that includes the OS and performance factors, irrespective of whether these layers are hosted on cloud or on-premise or both. It also applies the AI for monitoring, mapping, tracing and analyze the pattern to provide the Observability and Insights. Given below is a typical representation of distributed application and its components. And the rest of the section covers, how ZIF APM provides such deep level of insights.

ZIF APM

Once the APM Engine is installed/run on portfolio servers, the build-in AI engine does the following automatically: 

  1. Monitors the performance of the application (Web) layer, Service Layer, API, and Middle tier and Maps the insights from User <–> Web <–> API <–> Database for each and every applications – No need to manually link Application 1 in Web Server A with API1 in Middle Tier B and so on.
  2. Traces the end-to-end user transaction journey for all transactions with Unique ID.
  3. Monitors the performance of the 3rd party calls (e.g. web service, API calls, etc.), no need to map them.
  4. Monitors the End User Experience through RUM (Real User Monitoring) without any end-user agent.

<A reference screenshot of how APM maps the user transaction journey across different nodes. The screenshot also gives the Method level performance insights>

Why choose ZIF APM? Key Features and Benefits

  1. All-in-One – Provides the complete insight of the underlying Web Server, API server, DB server related infrastructure metrics like CPU, Memory, Disk, and others.
  2. End-user experience (RUM) – Captures performance issues and anomalies faced by end-user at the browser side.
  3. Anomalies detection – Offers deeper insights on the exceptions faced by the application including the line number in the source code where the issue has occurred.
  4. Code-level insights – Gives details about which method and function calls within the source code is taking more time or slowing down the application.
  5. 3rd Party and DB Layer visibility – Provides the details about 3rd party APIs or Database calls and Queries which are delaying the web application response.
  6. AHI – Application Health Index is a scorecard based on A) End User Experience, B) Application Anomalies, C) Server Performance and D) Database performance factors that are applicable in the given environment or application. Weightage and number of components A, B, C, D are variables. For instance, if ‘Web server performance’ or ‘Network Performance’ needs to be brought in as new variable ‘E’, then accordingly the weightage will be adjusted/calculated against 100%.
  7. Pattern Analysis – Analyzes unusual spikes through pattern matching and alerts are provided.
  8. GTrace – Provides the transaction journey of the user transaction and the layers it is passing through and where the transaction slows down, by capturing the performance of each transaction of all users.
  9. JVM and CLR – Provides the Performance of the underlying operating system, Web server, and run time (JVM, CLR).
  10. LOG Monitoring – Provides deeper insight on the application logs.
  11. Problem isolation– ZIF APM helps in problem isolation by comparing the performance with another user in the same location at the same time.

Visit www.zif.ai for more details.

About the Author –

Suresh Kumar Ramasamy

Suresh heads the Monitor component of ZIF at GAVS. He has 20 years of experience in Native Applications, Web, Cloud, and Hybrid platforms from Engineering to Product Management. He has designed & hosted the monitoring solutions. He has been instrumental in conglomerating components to structure the Environment Performance Management suite of ZIF Monitor. Suresh enjoys playing badminton with his children. He is passionate about gardening, especially medicinal plants.

Ensure Service Availability and Reliability with ZIF

To survive in the current climate, most enterprises have already embarked on their digital transformation journeys. This is leading to uncertainty in the way applications and services supporting the applications are being monitored and managed. Inadequate information is leading to downtime in service availability for end-users eventually resulting in unhappy users and revenue loss.

Zero Incident Framework™ has been architected to address the IT Ops issues of today and tomorrow.

Leveraging the power of Artificial Intelligence on telemetry data ingested in real-time, ZIF can provide insights and resolve forecasted issues – resulting in the availability of application service when end-user wants the service at the right time.

Business Value delivered to customers from ZIF

  • Minimum 40% reduction in capital expenses and a minimum 50% reduction in IT operational cost
  • Faster resolution by 60% (MTTR)
  • Service availability of 99.99%
  • ZIF bots to increase productivity by a minimum of 80%
  • Increased user experience measured by metrics (UEI) User Experience Index
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ICEBERG STATE IN ITOps

Many IT operations are in an ‘ICEBERG’ state even today. Do not be surprised if your organization is also one of them. Issues and incidents that surfaces to the top are the ones that are known to the team. But the unknown issues are not uncovered.

Therefore, enterprises have started to embark on artificial intelligence to help them identify and track the unknown issues within the complex IT landscape.

OBSERVABILITY USING ZIF

ZIF, architected and developed on the premise of observability, not only helps with visibility but also enables discovering deeper insights, thus freeing up more time for more strategic initiatives. This becomes critical to the overall success of Site Reliability Engineering (SRE) in enterprises.

Externalizing the internal state of systems, services, and application to the maximum, helps in complete observability.

Monitoring Vs. Observability?

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Pillars of Observability – Events | Metrics | Traces

Ensure SERVICE RELIABILITY

“Reliability is defined as the probability that an application, system, or service will perform its intended function adequately for a specified period or will operate in a defined environment without failure.”

ZIF has mastered the art of predicting device, application & service failure, or performance degradation. This unique proposition from ZIF gives IT engineers the edge on service reliability of all applications, systems, or services that they are responsible for. ZIF’s auto-remediation bots can resolve predicted issues to make sure the intended function performs as and when expected by users.

SERVICE AVAILABILITY

Availability is measured as the percentage of time your service or system or application is available.

A small variation in availability percentage will have to be addressed on priority. A 99.999% availability allows only 5.26 minutes of downtime a year, whereas 99% availability allows downtime of 3.65 days a year.

ZIF helps IT engineers achieve the agreed-upon availability of application or system by learning the usage of the system and application from the metrics that are collected from the environment. Collecting the right metrics helps in getting the right availability. With the help of unsupervised algorithms, patterns are learned which helps in discovering when the application or system is required the most and then predicting any potential downtime. With above 95% accuracy in prediction, ZIF can achieve 99.99% availability for application and devices which allows 52.56 minutes downtime a year.

ZIF’s goal has always been to deliver the right business outcomes for the stakeholders. Users have the privilege to choose what business outcomes are expected from the platform and the respective features are deployed in the enterprise to deliver the chosen outcome.

About the Author

Anoop Aravindakshan

An evangelist of Zero Incident FrameworkTM, Anoop has been a part of the product engineering team for long and has recently forayed into product marketing. He has over 14 years of experience in Information Technology across various verticals, which include Banking, Healthcare, Aerospace, Manufacturing, CRM, Gaming, and Mobile.