How Digital Experience Monitoring (DEM) Can Help

 

In today's digital age, businesses need to continuously evolve and adapt to stay ahead of the competition. One area where this is particularly true is in the realm of application performance management (APM). While APM has been an essential tool for monitoring and optimizing application performance, it's no longer enough to ensure a seamless digital experience for customers. This is where Digital Experience Monitoring (DEM) and AIOps solutions come into play.

AIOps solutions, powered by artificial intelligence and machine learning algorithms, provide a more comprehensive approach to APM. With AIOps, businesses can monitor all aspects of their applications and infrastructure in real-time, including logs, metrics, and traces. This allows them to identify and resolve issues before they impact end-users, improving the overall digital experience.

Moreover, AIOps solutions like DEM offer additional features like user journey mapping and real user monitoring, which provide deeper insights into how customers interact with digital touchpoints. This data helps businesses identify areas of friction in the customer journey, optimize their digital channels, and deliver a seamless user experience.

Comparing DEM and APM

AIOps solutions are becoming increasingly important in the world of DevOps. They enable organizations to automate their processes and gain more insight into their operations. Comparing DEM (Digital Experience Monitoring) and APM (Application Performance Management) is essential to understanding which AIOps solution is best suited for your organization's needs.

DEM focuses on the customer experience, while APM focuses on performance metrics. Both solutions offer real-time insights into operations, but they have different use cases. DEM helps identify areas where customers might be having difficulty using an application or website, while APM can be used to pinpoint issues with applications and services that are causing performance issues. By comparing the two solutions, organizations can determine which one will provide them with the most comprehensive view of their operations.

How DEM augments APM?

Digital Experience Monitoring (DEM) is a key component of AIOps solutions, and when combined with Application Performance Management (APM), it can provide organizations with an unprecedented level of visibility into their digital infrastructure. DEM helps to track user experience across all devices, while APM provides insight into the performance of applications and services. When combined, these two solutions create a powerful tool that can help organizations identify problems before they become serious issues. By leveraging the insights from DEM and APM, organizations can improve user experience and ensure that their applications are running at optimal performance levels.

APM alone cannot help IT teams to boost service reliability. The same IT teams switch to DEM tools to find the reason for slowness. Performance testing with DEM tools reveals the slowdown can be attributed to excessive graphic elements within the application. For best performance, IT teams must prioritize technical optimization. That may mean reducing graphics on applications to improve customer experience. This showcases the power of DEM and APM as valuable tools for restoring service reliability.

APM tools are effective for quickly pinpointing issues, but DEM tools provide a more comprehensive view of the end-user experience. DEM and APM tools help organizations identify and resolve digital experience issues, leading to improved service reliability.

Conclusion:

As businesses continue to digitize and transform, they need to adopt more advanced solutions like AIOps to ensure optimal application performance and deliver exceptional digital experiences. AIOps solutions like DEM provide the necessary insights and capabilities to achieve this goal, helping businesses stay competitive in the ever-evolving digital landscape.

 

The Future of Healthcare: How AIOps Can Unlock the Full Potential of Healthcare Data

 

The healthcare sector is one of the most rapidly evolving fields, and AIOps in healthcare is playing a major role in revolutionizing patient care. AI and related technologies have the power to revolutionize the healthcare sector, impacting patient care and administrative processes within provider organizations, payers & pharmaceutical companies.

Studies and research have already shown that AI can be just as effective or even more capable than human clinicians when it comes to primary healthcare duties, such as diagnosing diseases. Right now, algorithms outperform human radiologists in detecting cancerous tumors and providing vital assistance to researchers when constructing cohorts for high-cost medical testing.

Why is the Majority of Medical Data Highly Unstructured?

The majority of medical data is highly unstructured and complex, making it difficult to process and analyze. This is due to the fact that medical data can come from a variety of sources such as patient records, lab results, medical images, and more. As a result, healthcare organizations are turning to AIOps solutions in order to make sense of this vast amount of data. AIOps solutions use artificial intelligence (AI) and machine learning (ML) to identify patterns in the data and provide insights that can be used by healthcare professionals. By leveraging AIOps in healthcare, organizations can gain access to valuable insights that would otherwise remain hidden within their unstructured data.

What is the Role of AIOps in Healthcare Structuring Data?

As per the 4th annual Optum Survey on AIOps in the healthcare sector, leading experts think AI can benefit patients by improving outcomes, curbing costs, and encouraging health equity. AI is revolutionizing healthcare by speeding up data exchange between payers, providers, and patients, leading to more efficient and accurate results.

Medical companies need to better manage unstructured data for AI to reach its full potential, allowing AIOps systems to improve training accuracy, reliability, and effectiveness. It is seen as a profitable tool in healthcare, with 80% of clinicians believing it can do more than just administrative tasks. It has the potential to increase data quality and patient reach, leading to higher profits. AIOps in healthcare can help reduce the time spent on manual data entry tasks, improve the accuracy of data analysis, and provide actionable insights to improve patient outcomes.

Why Timely, Accurate Information is Required in Healthcare Delivery, Management

The medical industry is facing a data issue that hinders personalized patient care. Unstructured data is not helping to create an accurate, comprehensive picture of people's health. With incomplete knowledge, decision-making can easily prove insignificant, thereby resulting in poor outcomes and high expenses.

AI is the key solution to having fast access to reliable data, which improves patient outcomes and helps physicians make accurate decisions. A research study involving respondents suggested that 96% of them believed easier access to primary data will help save lives. 95% of respondents highlighted data interoperability as essential to improving patient outcomes, with 86% believing efficient healthcare info exchange will lead to lower costs, quicker diagnosis, and accurate results.

Poor investment in AIOps solutions prevents organizations from taking advantage of AIOps in healthcare in the long run. Machine learning can reduce the time for patient care by accelerating clinical decision-making through NLP, which allows for unstructured data to be structured quickly and securely. Technology is enabling healthcare professionals and care managers to achieve their goals.

 

Growing Importance of Business Service Reliability | ZIF

Business services are a set of business activities delivered to an outside party, such as a customer or a partner. Successful delivery of business services depends on several IT services. An IT business service that would support “order to cash”, as an example could be “supply chain service”, that can be delivered by an application like SAP, with the customer of that service being an employee using the application to perform customer-facing services. A business service is not only an application the end-user sees, but also the entire chain that supports the delivery of the service, including physical and virtualized servers, databases, and many more.

A failure in any of these can affect the service – and therefore IT organizations have an up-to-date view of these components and of how they work together.

The technologies for Social Networking, Mobile Applications, Analytics, Cloud (SMAC), and Artificial Intelligence (AI) are redefining the business and their services. Their widespread usage is changing the business landscape, increasing reliability and availability.

Availability versus Reliability

Reliability is the measure of how long a business service performs its function, whereas on the another hand availability is the measure of the percentage of time a business service is operable.

Service reliability can be the follows:

• Probability of success
• Durability
• Dependability
• Quality over time
• Availability to perform

 

Recognizing the importance of reliability, Google initiated Site Reliability Engineering (SRE) practices with a mission to protect, provide for, and progress the software and systems behind all of Google’s public services — Google Search, Ads, Gmail, Android, YouTube, and App Engine.

 Zero Incident Framework (ZIF) enables proactive detection and remediation of the incident and is, available in two versions to evaluate and experience the power of AI-driven Business Service Reliability: 

• ·       ZIF Business Xpress
• ·       ZIF Business

r

r    Read this entire blog by the best digital transformation company in the USA - ZIF.AI here - https://zif.ai/growing-importance-of-business-service-reliability/

Addressing Web Application Performance Issues - Zero Incident Framework

It is important to make sure that the end-user gets a greater experience while using an application and therefore it is compulsory to monitor the performance of an application to provide higher satisfaction to them.

External factors

When the web applications face performance issues, here are some questions that need to be asked:

• ·       Application performance issue
• ·       Production environment
• ·       Release of the application stack
• ·       Hardware/software upgrades

 Actions

• Look at the number of incoming requests
• Identify how many requests are delaying
• Look at the web pages/methods/functions in the source code
• Identify whether any third-party links or APIs is making it slow
• 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
• Check the performance of the CPU, Memory, and Disk of the server
• Check the sibling processes which are consuming more Memory/CPU/Disk in all servers

 

Challenges 

People need to be well equipped with technologies across all layers to know what parameters to collect and how to collect.

Zero Incident Framework Application Performance Monitoring gives details of application performance management. The APM Engine has built-in AI features monitor the application across all layers, starting from an end-user, web application, to the underlying infrastructure.

The in-built 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

2.    Traces the end-to-end user transaction journey

3.    Monitors the performance of the 3rd party calls

4.    Monitors the End User Experience

Why choose ZIF APM?

Key Features and Benefits

1.    Provides a 360 insight into the underlying Web Server, API server, DB server related infrastructure metrics

2.    Captures performance issues and anomalies that the end-users face

3.    Offers deeper insights on the exceptions faced by the application

4.    Gives every detail about which method and function calls take more time or slow down the application

5.    Provides the details about 3rd party APIs or Database calls

6.    Analyzes unusual spikes through pattern matching, thus alerting providers

Read the complete blog by the ai for application monitoring tool, Zero Incident Framework - https://zif.ai/addressing-web-application-performance-issues/

Prediction for Business Service Assurance

Artificial Intelligence for IT Operations (AIOps) uses technologies like Big Data and Machine Learning (ML) to automate the resolution and identification of the common problems of Information Technology (IT).

Zero Incident Framework (ZIF) is an AIOps based TechOps platform which enables proactive detection and remediation of incidents which helps an enterprise to move towards a Zero Incident Enterprise.

Five modules of ZIF:

• -        Discover: To auto-discover all the IT assets and mission-critical workloads
• -        Monitor: End-to-end enterprise performance monitoring
• -        Analyze: Analyze and correlate alerts/events across tools
• -        Predict: Predictive techniques to prevent outages
• -        Remediate: Prescriptive remediation with minimal or no manual intervention

Predictive Analysis is ZIF’s USP. ZIF encompasses Supervised, Unsupervised and Reinforcement Learning algorithms for realization of various businesses use. They are:

                  Supervised Learning Algorithm:

·        Linear prediction

·        Seasonality based prediction

·        Recommended resolution

·        Virtual Supervisor

·        False Probability

·        Anomaly Deduction

·        Estimated Time to Complete

Unsupervised Learning Algorithm:

·        Sequence-based mining

·        Keyboard extraction

·        Sentiment Analysis (CSAT)

·        Data labeling

·        Log Analytics

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                        Reinforcement Learning Algorithms:

·        Log Rotation

·        Optimizing resource utilization

How does ZIF work:

-        

•       ZIF can receive and process all kinds of data through its ingestion capabilities
• -        Predicts anomalies by analyzing these data
• -        Anomalies can be presented as ‘Opportunity cards’ to eliminate any undesired incidents
• -        It brings out the paradigm shift since its proactive and reactive
• -        Predictions occur at multiple levels

Sub-Functions of the Predictive Model:

Functions:

-        Forecast capacity needs

·        Provides accurate resource utilization and usage predictions

·        Orchestrates the virtual infrastructure

-        Forecast Incident Volume:

·        Predicts to spike and exceed capacity

·        Predicts corrective actions

-        Detects potential failures:

·        Forecasts when capacity is about to exceed

·        Identifies problems even when they originate somewhere else

Benefits:

• -        Reduced manual effort
• -        Reduced errors
• -        Reduced cost of operations
• -        Optimized staffing
• -        Proactive IT Operations
• -        Drive towards zero outage
• -        Enhanced user experience

Predict module can categorize the opportunity cards into three swim lanes. They are:

• -        Warning swim lane: Opportunity Cards that have an “Expected Time of Impact” (ETI) beyond 60 minutes.
• -        Critical swim lane: Opportunity Cards that have an ETI within 60 minutes.
• -        Processed / Lost: Opportunity Cards that have been processed or lost without taking any action.

The above article has been taken from Zero Incident Framework, that is the best tool for predictive analytics using ai applications, and a leading cognitive process automation tools for business

Zero Incident Framework Corporate Brochure

Zero Incident Framework (ZIF) is a TechOps platform that is AI-based. ZIF helps in the remediation and proactive detection of incidents which in return helps an Organization to become a Zero Incident Enterprise.

Basic components of Zero Incident Framework (ZIF):

-        Discover

-        Monitor

-        Analyze

-        Predict

-        Remediate

Discover: Discovers all the critical mission workloads and IT assets.

• -        Auto-Discover Application
• -        Auto-Discover Users
• -        Real-Time Dynamic Topology

Monitor: Does the end-to-end performance monitoring for the enterprise.

• -        Allows visibility of the full stack
• -        Detects Anomalies
• -        User Experience Index, Application Health Index

Analyze: Analyzes and correlates alerts and events across tools.

• -        Ingest & Correlate heterogeneous Datasets
• -        Noise Nullification
• -        Accelerated RCA

Predict: Brings predictability to detect the potential outages.

• -        Forecasts the Capacity of Needs
• -        Forecast the Incident Volume
• -        Detects the Potential Failures

Remediate Prescriptive remediation with least or no manual intervention.

• -        Orchestration
• -        Auto-Remediation
• -        Virtual Engineer

ZIF for Service Desk:

• -        Enhances the productivity of the Service Desk by reducing the meantime to repair
• -        Takes user experience to the next level through the application of enhanced Sentiment Analysis and reduction of repeated incidents.
• -        Reduces the number of incidents as well as the cost of operations.

ZIF for Data Center:

• -        Reduces the false positives as well as provides a unified view of the health of the enterprise by ingesting and aggregating diverse data.
• -        Uses an Advanced Intelligent Incident Analytics to analyze the root cause faster.
• -        The predictive insights prevent the outage of expensive systems.

Outcome:

• -        Single Pane of Command
• -        Proactive IT Operations
• -        40% reduction of incidents
• -        60% reduction in Mean Time to Repair
• -        50% reduction in overall IT Operations costs
• -        Agile monitoring & elimination of Digital Dirt

Zero Incident Framework is the best tool for predictive analytics using AI applications, and for workflow automation software architecture. It ensures Zero IT downtime for your organization, and reduces manual efforts for the IT Personnel.

Machine Learning: Building Clustering Algorithms

Clustering is a widely-used Machine Learning (ML) technique. Clustering is an Unsupervised ML algorithm that is built to learn patterns from input data without any training, besides being able of processing data with high dimensions. This makes clustering the method of choice to solve a wide range and variety of ML problems. Machine Learning and Clustering has been best explained by the best digital service desk AI software – Zero Incident Framework (ZIF).

ZIF is an award-winning tool developed by GAVS Technologies for the management of AIOps, AI automated root cause analysis solution, AI data analytics monitoring tools and many more such applications. Some excerpts from the blog are provided herein -

What is Clustering and how does it work?

Clustering is finding groups of objects (data) such that objects in the same group will be similar (related) to one another and different from (unrelated to) objects in other groups.

Clustering works on the concept of Similarity/Dissimilarity between data points. The higher similarity between data points, the more likely these data points will belong to the same cluster and higher the dissimilarity between data points, the more likely these data points will be kept out of the same cluster.

This blog also encompasses how a clustering algorithm can be built, how a dissimilarity matrix is built, properties of a distance matrix, and how it is built.

Considerations for the selection of clustering algorithms:

Before the selection of a clustering algorithm, the following considerations need to be evaluated to identify the right clustering algorithms for the given problem. Some of them are -

1.      Partition criteria: Single Level vs hierarchical portioning

2.      Separation of clusters: Exclusive (one data point belongs to only one class) vs non-exclusive (one data point can belong to more than one class)

3.      Similarity measures: Distance-based vs Connectivity-based

Clustering is broadly used in two applications namely - As an ML tool to get insight into data, and as a pre-processing or intermediate step for other classes of algorithms. Read this blog here to know more.