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! 

Top 6 Things AIOps Can Do for Your IT Performance

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.

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! 

Empowering VMware Landscapes with AIOps

VMware has been at the forefront of everything good in the modern IT infrastructure landscape for a very long time. After it came up with solutions like VMware Server and Workstation around the early 2000s, its reputation got tremendously enhanced amongst businesses looking to upgrade IT infrastructure. VMware has been able to expand its offering since then by moving to public and private cloud. It has also brought sophisticated automation and management tools to simplify IT processes within organizations.

The technology world is not static, it is consistently changing to provide better IT solutions that are in line with the growing and diverse demands of organizations across the world. The newest wave doing the rounds revolves around IT operations and providing support to business services that are dependent on those IT environments. AIOps platforms find their origin, primarily from the world that VMware has created – a world that is built on IT infrastructure that is capable of modifying itself according to needs and is defined by software. This world created by VMware consists of components that are changing and moving at a rapid pace. In order to keep up with these changes, newer approaches to operating environments are required. AIOps solutions are emerging as the ideal way to run IT operations with no reliance on static service models or fragile systems. AIOps framework promises optimal utilization of skills and effort targeted at delivering maximum value.

In order to make the most of AIOps tools, it is important that they be used in ways that can complement the existing VMware infrastructure strategy. Here are a few of those:

Software-defined is the way to go

Even though SDx is not properly distributed, it is still here and making its mark. However, the uneven distribution of SDx is a problem. There is still a need to manage physical network infrastructure along with some aspects of VMware SDN. In order to ensure that you get the most out of VMware NFV/SDN, it is important to conduct a thorough overview combining all these aspects. By investing in an AIOps solution, you will have a unified view of the different infrastructure types. This will help you in not only identifying problems faster but also aligning IT operation resources to deal with them so that they don’t interfere with the service that you provide to your users, which is the ultimate objective of choosing to invest in any IT solution.

Integrated service-related view across the infrastructure

Not too many IT organizations out there can afford to use only one technology across the board. Every organization has to deal with many things that they have done prior to switching to AIOps. IT-related decisions made in the past could have a strong bearing on how easy or difficult the transition is. There is not just the management of virtual network and compute amongst others, organizations have their work cut out with the management of the physical aspects of these things as well. If that’s not enough, there is a public cloud and applications to manage as well.

Having an overview of the performance and availability of services that are dependent on all these different types of infrastructure is very important. Having said that, this unified view should be independent of time-consuming manual work associated with entering service definitions at every point of change. Also, whenever it is updated, it should do so with respect to the speed of infrastructure. Whether or not your IT infrastructure can support software-defined frameworks depends a lot on its minimum or no reliance on static models.  AIOps can get isolated data sources into a unified overview of services allowing IT operations teams to make the most of their time and focus only on the important things.

Automation is the key

You have to detect issues early if you want to reduce incident duration – that’s a fact. But there is no point in detecting issues early if you are not able to resolve them faster. AIOps tools connect with third-party automation tools as well as those that come with VMware to provide operators a variety of authorized actions to diagnose and resolve issues. So there are no different automation tools and actions for different people, which enables everyone to make the most of only the best tools. What this leads to is helping the IT operations teams to deliver desired outcomes, such as faster service restoration.

No-risk virtual desktops

There is no denying the benefits of having virtual desktops. However, there are disadvantages of taking the virtual route as well. With virtual desktops, you can have a chain of failure points, out of which any can have a huge impact on the service delivered to end-users. The risk comes from the different VDI chain links that are owned by different teams. This could prove harmful and cause outages, especially if support teams don’t go beyond their area of specialization and don’t communicate with other support teams either. The outages will be there for a longer period of time in these cases. AIOps can detect developing issues early and provide a background of the entire problem throughout the VDI chain. This can help different support teams to collaborate with each other and provide a resolution faster, consequently saving end-users from any disruption.

Collaboration across service teams

VMware admins have little problem in getting a clear overview of the infrastructure that they are working on. However, it is a struggle when it comes to visibility and collaboration across different teams. The problem with this lack of collaboration is the non-resolution of issues. When issues are raised, they only move from one team to another while their status remains unresolved. AIOps can improve the issue resolution rate and bring down issue resolution time considerably. It does this by associating events with their respective data source, aligning the issue to the team that holds expertise in troubleshooting that particular type of issue. AIOps also facilitates collaboration between different teams to fast-track issue resolution.

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.

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.

Anomaly Detection in AIOps

Before we get into anomalies, let us understand what is AIOps and what is its role in IT Operations. Artificial Intelligence for IT operations is nothing but monitoring and analyzing larger volumes of data generated by IT Platforms using Artificial Intelligence and Machine Learning. These help enterprises in event correlation and root cause analysis to enable faster resolution. Anomalies or issues are probably inevitable, and this is where we need enough experience and talent to take it to closure.

Let us simplify the significance of anomalies and how they can be identified, flagged, and resolved.

What are anomalies?

Anomalies are instances when performance metrics deviate from normal, expected behavior. There are several ways in which this occur. However, we’ll be focusing on identifying such anomalies using thresholds.

How are they flagged?

With current monitoring systems, anomalies are flagged based on static thresholds. They are constant values that provide the upper limits of a normal behavior. For example, CPU usage is considered anomalous when the value is set to be above 85%. When anomalies are detected, alerts are sent out to the operations team to inspect.

Why is it important?

Monitoring the health of servers are necessary to ensure the efficient allocation of resources. Unexpected spikes or drop in performance such as CPU usage might be the sign of a resource constraint. These problems need to be addressed by the operations team timely, failing to do so may result in applications associated with the servers failing.

So, what are thresholds, how are they significant?

Thresholds are the limits of acceptable performance. Any value that breaches the threshold are indicated in the form of alerts and hence subjected to a cautionary resolution at the earliest. It is to be noted that thresholds are set only at the tool level, hence that way if something is breached, an alert will be generated. These thresholds, if manual, can be adjusted accordingly based on the demand.

There are 2 types of thresholds;

  1. Static monitoring thresholds: These thresholds are fixed values indicating the limits of acceptable performance.
  2. Dynamic monitoring thresholds: These thresholds are dynamic in nature. This is what an intelligent IT monitoring tool does. They learn the normal range for both a high and low threshold, at each point in a day, week, month and so on. For instance, a dynamic system will know that a high CPU utilization is normal during backup, and the same is abnormal on utilizations occurring in other days.

Are there no disadvantages in the threshold way of identifying alerts?

This is definitely not the case. Like most things in life, it has its fair share of problems. Routing from philosophy back to our article, there are disadvantages in the Static Threshold way of doing things, although the ones with a dynamic threshold are minimal. We should also understand that with the appropriate domain knowledge, there are many ways to overcome these.

Consider this scenario. Imagine a CPU threshold set at 85%. We know anything that breaches this, is anomalies generated in the form of alerts. Now consider the same threshold percentage as normal behavior in a Virtual Machine (VM). This time, the monitoring tool will generate alerts continuously until it reaches a value below the threshold. If this is left unattended, it will be a mess as there might be a lot of false alerts which in turn may cause the team to fail to identify the actual issue. It will be a chain of false positives that occur. This can disrupt the entire IT platform and cause an unnecessary workload for the team. Once an IT platform is down, it leads to downtime and loss for our clients.

As mentioned, there are ways to overcome this with domain knowledge. Every organization have their own trade secrets to prevent it from happening. With the right knowledge, this behaviour can be modified and swiftly resolved.

What do we do now? Should anomalies be resolved?

Of course, anomalies should be resolved at the earliest to prevent the platform from being jeopardized. There are a lot of methods and machine learning techniques to get over this. Before we get into it, we know that there are two major machine learning techniques – Supervised Learning and Unsupervised Learning. There are many articles on the internet one can go through to have an idea of these techniques. Likewise, there are a variety of factors that could be categorized into these. However, in this article, we’ll discuss an unsupervised learning technique – Isolation Forest amongst others.

Isolation Forest

The algorithm isolates observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature.

The way that the algorithm constructs the separation is by first creating isolation trees or random decision trees. Then, the score is calculated as the path length to isolate the observation. The following example shows how easy it is to separate an anomaly-based observation:  

predictive analytics models

In the above image, the blue points denote the anomalous points whereas the brown ones denote the normal points.

Anomaly detection allows you to detect abnormal patterns and take appropriate actions. One can use anomaly-detection tools to monitor any data source and identify unusual behaviors quickly. It is a good practice to research methods to determine the best organizational fit. One way of doing this is to ideally check with the clients, understand their requirements, tune algorithms, and hit the sweet spot in developing an everlasting relationship between organizations and clients.

Zero Incident FrameworkTM, as the name suggests, focuses on trending organization towards zero incidents. With knowledge we’ve accumulated over the years, Anomaly Detection is made as robust as possible resulting in exponential outcomes.

References

About the Author –

Vimalraj Subash

Vimalraj is a seasoned Data Scientist working with vast data sets to break down information, gather relevant points, and solve advanced business problems. He has over 8 years of experience in the Analytics domain, and currently a lead consultant at GAVS.

Empowering Digital Healthcare Transformation with ZIFTM

The Modern-Day Healthcare

The healthcare industry is one of the biggest revenue generation sectors for the economy. In 2020, the healthcare industry generated close to $2.5 trillion dollars in the US. This has been made possible due to multiple revenue generation streams that encompass the development and commercialization of products and services that aid in maintaining and restoring health.

The modern healthcare industry has three essential sectors – services, products, and finance, which in turn can be further branched to various interdisciplinary groups of professionals that meet the health needs of their respective customers.

For any industry to scale and cover more customers, being digital is the best solution. Stepping into the digital space brings various tools and functionalities that can improve the effectivity and efficiency of the products and services offered in the Healthcare Industry.

The key component of any Digital Healthcare Transformation is it’s Patient-Focused Healthcare Approach. The transformation must aid healthcare providers in better streamlining the operations, understanding what the patients need and in turn build loyalty, trust and a stellar user experience.

Healthcare Transformation Trends

Innovation is the foundation for all Transformation initiatives. The vision of rationalizing work, optimizing systems, improving delivery results, eliminating human error, reducing costs, and improving the overall customer experiences are the levers that churn the wheel. With the advent of VR, wearable medical devices, telemedicine, and 5G using AI-enabled systems have significantly changed the traditional way that consumers use healthcare products and services.

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The industry has shifted its focus in making intelligent and scalable systems that can process complex functionalities as well as deliver customer experience at its finest.  With the integration of AI and omnichannel platforms, organizations can better understand their customers, address service and product gaps to better capitalize the market to achieve higher growth. Hence, transformation is the key to pushing forward in unprecedented and unpredictable times in order to achieve organizational vision and goals.

Sacrosanct System Reliability

The healthcare industry is a very sensitive sector that requires careful attention to its customers. A mishap in the service can result in a life-and-death situation. Healthcare organizations aim to learn lessons from failures and incidents to make sure that they never happen again.

Maintaining and ensuring safe, efficient, and effective systems is the foundation for creating reliable systems in the Healthcare industry. Hence, innovation and transformation disrupt the existing process ecosystems and evolve them to bring in more value.

The challenge that organizations face is in their implementation and value realization with respect to cost savings, productivity enhancements, and overall revenue. The prime aspect of system reliability signifies the level of performance over time. When we consider healthcare, looking at defects alone does not differentiate reliability from the functional quality of the system. Reliability should be measured to its failure-free operation over time. Systems should be designed and implemented to focus on failure-free operation.

Measuring system operations over time can be depicted as a bathtub curve. While measuring performance, initial failure tends to arise from defects and situational factors. Eventually, the efficiency improves, and the curve flattens out to depict useful life until the wear-out phase starts from design and other situational factors.

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While understanding the bathtub curve of system operations over time, we can infer that system design majorly contributes to the initial defects and system longevity. Hence, organizations must strive to build systems that can last a tenure from which the invested capital can be gained back, and the additional returns can be used for its future modernization goals.

Towards the end of the day, system reliability revolves around the following factors:

  1. Process failure prevention
  2. Identification and Mitigation of failure
  3. Process redesign for critical failure avoidance

Reliability and Stability should seriously be considered whenever healthcare systems are being implemented. This is because the industry is facing quality-related challenges. Healthcare organizations are not delivering safe, reliable, and proof-based care. Thus, it is important for professionals to be empowered with tools and modern-day functionalities that would reduce the error and risk involved in their service delivery. These modern-day tools’ reliability must be sacrosanct to ensure that stellar customer experience and patient care are given.

Organizations purely focused on cost savings as a standalone goal can lead to unpredictable outcomes. It is imperative that an organization realize robust and reliability-centered processes that define clears roles and accountability to its employees, in order to have a sustainable form of operation.

When all these factors come together, the value realizations for the organization as well as its customer base are immense. These systems can contribute towards better ROI, improved profitability, enhanced competitive advantage, and an evolved customer brand perception.

These enhanced systems improve the customer loyalty and the overall brand value.

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Device Monitoring with ZIFTM

Ever since the pandemic hit, healthcare organizations have concentrated towards remote patient health monitoring, telemedicine, and operations to expedite vaccine deliveries. These healthcare organizations have invested heavily in systems that connect all the data required for day-to-day operations into one place for consolidated analysis and decision making.

For the effective functioning of these consolidated systems, each of the devices that are connected to the main system needs to be functioning to its optimal capacity. If there is a deviation in device performance and the root cause is not identified promptly, this can have adverse effects on the service delivery as well as the patient’s health.

These incidents can be addressed with ZIFTM’s OEM Device monitoring capabilities. ZIFTM can be used to provide a visual dashboard of all operational devices and monitor their health to set thresholds for maintenance, incident detection, and problem resolutions. The integration can also create a consolidated view for all logs and vital data that can be later used for processing to give predictive information for actionable insights. The end goal that ZIFTM aims to achieve here is to pivot organizations towards a proactive approach to servicing and support for the devices that are operational. This connectivity and monitoring of devices across their portfolio can substantially bring in measurable changes in its cost savings, service efficiency, and effectivity.

Prediction & Reliability Enhancement

With healthcare systems and digital services expanding across different organizations, predicting their reliability, efficiency and effectivity are important. When we look at reliability prediction, the core function is to evaluate systems and predict or estimate their failure rate.

In the current scenario, organizations are performing reliability and prediction analysis manually. Each of the resources analyzes the system to its component level and monitors its performance. This process has a high susceptibility to manual errors and data discrepancies. With ZIFTM, the integrated systems can be analyzed and modeled based on various characteristics that contribute to its systemic operation and failure. ZIFTM analyzes the system down to its component level to model and estimates each of its parameters that contribute to the system’s reliability.

The ZIFTM Empowerment

Players in the Healthcare Industry must understand that Digital Transformation is the way forward to keep up with the emerging trends and tend to its growing customer needs. The challenge comes in selecting the right technology that is worth investing and reaping its benefits within the expected time period.

As healthcare service empowerment leaders in the industry, GAVS is committed to align with our healthcare customers’ goals and bring in customized solutions that help them attain their vision. When it comes to supporting reliable systems and making them self-resilient, the Zero Incident FrameworkTM can bring in measurable value realizations upon its implementation.

ZIFTM is an AIOps platform that is crafted for predictive and autonomous IT operations that support day-to-day business processes. Our flagship AIOps platform empowers businesses to Discover, Monitor, Analyze, Predict and Remediate threats and incidents faced during operations. ZIFTM is one unified platform that can transform IT operations that ensure service assurance and reliability.   

ZIFTM transforms how organizations view and handle incidents. IT war rooms become more proactive when it comes to fire fighting. Upon implementation, customers can get end-to-end visibility of enterprise applications and infrastructure dependencies to better understand areas needing optimization and monitoring.   The Low code/No code implementation with various avenues for integration, provides our customers a unified and real-time view of on-premise and cloud layers of their application systems. This enables and empowers them to track performance, reduce incidents and improve the overall MTTR for service request and application incidents.

Zero is Truly, The New Normal.

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Experience and Explore the power of AI led Automation that can empower and ensure System Reliability and Resilience.

Schedule a Demo today and let us show you how ZIFTM can transform your business ecosystem.

www.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. 

applications of predictive analytics in business

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.