Category: Digital Transformation goals

AIOps Myth Busters

Posted on by ZIF

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

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

MYTH 1: AIOps mainly involves alert correlation and event management

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

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

MYTH 2: AIOps increases human effort

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

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

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

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

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

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

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

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

MYTH 6: AIOps platforms offer only warnings and no insights

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

MYTH 7: AIOps is suitable only for Operations

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

Augmented Analytics with SAP Analytics Cloud

Posted on by ZIF

Augmented Analytics

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

Here is the why and how.

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

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

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

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

Evolution of Analytics

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

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

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

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

SAP Analytics Cloud

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

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Augmented SAP Analytics Cloud

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

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

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

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Search to Insight – Query search in Natural Language

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

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

AIOps Artificial Intelligence for IT Operations

Smart Insights – Instant explanations

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

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

Smart Discovery – Easily reveal insights

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

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

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

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

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

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

Smart Predict – Answers the toughest questions

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

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

Smart Predict provides 3 different predictive scenario options for selection

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

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

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

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

References

MF Kashif

About the Author –

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

Is AR the Future of our Increasingly Digital World?

Posted on by ZIF

Imagine a device which, when used to look at something, throws up information on whatever you’re pointing at. Menus for restaurants, dates of establishment for institutes, and so on. These are the sort of possibilities afforded by Augmented Reality (AR).

What is AR?

AR is a science fiction idea that successfully made the transition to reality. The fundamental idea behind augmented reality is to add something extra to your experience of reality. So, if you are watching a movie or playing a game, AR adds to that experience in some way or form to turn the experience immersive and interactive. AR basically superimposes computer-generated information (audio, visual, haptic, etc.) on the real-world objects.

AR can be defined as a system that fulfills three basic features: a combination of real and virtual worlds, real-time interaction, and accurate 3D registration of virtual and real objects.

How does AR work?

A camera-equipped device is essential for an AR experience. Upon pointing the device at an object, computer vision technology is used to recognize it. The device then downloads information about the object from the cloud, in much the same way that a web browser loads a page via a URL. In this case, the information is presented in a 3-D experience.

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AR can provide a view of the real-time data flowing from products and allow users to control them by touchscreen, voice, or gesture. An operator using an AR headset to interact with an industrial robot might see superimposed data about the robot’s performance and gain access to its controls.

The size and orientation of objects viewed through the AR display adjusts/changes in real-time. New graphical or text information comes into view while other information passes out of view as the user moves about. In industrial settings, users in different roles, such as a machine operator and a maintenance technician, can look at the same object but be presented with different AR experiences that are tailored to their needs.

AR – a novel way of shopping

A 2018 Gartner report stated, “By 2020, 100 million consumers will shop in AR online and in-store.” The current global pandemic has put a damper on consumer sentiments worldwide and we may not see those numbers. But AR can help make the in-store shopping experience more secure by reducing the need to touch a lot of objects and surfaces.

Brands like American Apparel, Uniqlo, and Lacoste already have showrooms and fitting rooms that provide try-before-you-buy options in AR spaces. Smart mirror technologies that scan RFID tags also offer the ability to bring recommendations to the brick-and-mortar shopping experience.

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IKEA customers have access to an app that permits them to point their phones at spaces and see what different products would look like in their own homes.

In the current global climate, fashion and lifestyle brands stand to gain from technologies that handle facial recognition, adapt to local lighting conditions, and provide personalized recommendations.

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According to a BRP report, 48% consumers said they would be more inclined to buy from a retailer that provided AR experiences. Retailers may be able to attract more customers with an immersive and secure shopping experience in a post-Corona world.

35% of sales on Amazon are derived from its recommendation engine, which is powered by Machine Learning. Leveraging this in the real world also has immense commercial potential.

AR for Navigation Solutions

Map services from Google and Apple have already found mass acceptance, indoor navigation is next. Apps based on ARKit and ARCore can enable navigating inside spaces like airports, malls, hospitals, etc. Gatwick Airport has already deployed its own smartphone solution that provides routes to terminals and gates based on a user’s flight number.

In 2019, a beta version of AR walking directions feature was launched for Google Maps for all AR-compatible iOS and Android mobile devices. You could view information about your surroundings by pointing your phone’s camera towards it.

AR in Automotive Industry

AR can be used in a breadth of ways in the automotive industry. Starting with dashboard-mounted heads-up displays to interactive experiences in showrooms and more.

AR is also employed by some carmakers to help aid in car maintenance (Volkswagen’s Marta app) and car manufacturing and selling processes (Volvo’s project with Microsoft HoloLens).

The heads-up display is one of the most popular uses of AR in this industry. Not only can drivers get directions and alerts on hazards, but also information on landmarks and nearby locations.

Hyundai has been a leader in AR research that goes beyond the cockpit-style view of the motorist’s experience. They have reimagined maintenance manuals with AR and has apps to point their phones at their cars to get information. Mercedes has a similar app, but its version adds a chatbot to provide virtual assistance.

AR in Healthcare

Applications of AR is opening up new opportunities in the healthcare industry. It’s expected that the global market will reach a value of $1.5B. By enabling healthcare workers with real-time data and patient information, AR can aid in more accurate diagnoses and more precise surgeries.

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AR can also bring huge value to practicing medicine and education by allowing students and trainee physicians to better visualize health issues and scenarios that they one day will be treating. The benefit that AR can bring to the healthcare industry can be ground-breaking and we are just witnessing the beginning of what is to come from AR in the field of medicine.

AR-powered Solutions for Enterprises

Smart glasses are quickly gaining popularity. Military, medical and enterprise solutions, however, are beginning to prove the value of combining AR with headsets and smart glasses.

Microsoft HoloLens 2 was likely the most anticipated product in this space in 2019. The company hopes to roll out its technology to great fanfare by demonstrating improvements in raw processing power, battery life, and wear ability. The U.S. Army has awarded a $480 contract to Microsoft, and they are also working with the industrial IoT firm PTC to streamline the development of both augmented and mixed reality products.

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Walmart and Tyson are testing programs that will transition traditional training methods into mixed reality (MR) settings. This will bring about new ways to learn about compliance and safety issues by looking around mixed-reality environments and identifying problems in a way that’s practical and engaging. Integration with other recent workplace training trends, especially gamification, may compound the returns that AR and MR solutions generate. Per ABI Research, AR-based training in enterprise will be a $6 billion industry by 2022.

Improvements in prototyping, testing, troubleshooting, and quality control are expected to emerge from this trend, too, as workers will be able to make on-the-fly comparisons of real-world items against available documentation and specifications. Jobs that call for workers’ hands to be free will also benefit significantly from AR headsets and glasses.

Augmented reality is the next ‘BIG THING’, it will absolutely revolutionize almost every aspect of life. Everything from medicine to education to construction to entertainment. AR application has already started to appear on the world’s laptops, tablets, and smartphones.

References

https://www.sciencedirect.com/topics/computer-science/augmented-reality

https://www.mantralabsglobal.com/blog/disruptive-augmented-reality-use-cases/

https://www.vxchnge.com/blog/augmented-reality-statistics

About the Author –

Kalpana Vijayakumar

Kalpana is a database developer. She strongly believes that “It’s not that we use technology, we live technology.”
Outside of her professional role, Kalpana is passionate about travelling and watching movies.

Algorithmic Alert Correlation

Posted on by ZIF

Today’s always-on businesses and 24×7 uptime demands have necessitated IT monitoring to go into overdrive. While constant monitoring is a good thing, the downside is that the flood of alerts generated can quickly get overwhelming. Constantly having to deal with thousands of alerts each day causes alert fatigue, and impacts the overall efficiency of the monitoring process.

Hence, chalking out an optimal strategy for alert generation & management becomes critical. Pattern-based thresholding is an important first step, since it tunes thresholds continuously, to adapt to what ‘normal’ is, for the real-time environment. Threshold accuracy eliminates false positives and prevents alerts from getting fired incorrectly. Selective alert suppression during routine IT Ops maintenance activities like backups, patches, or upgrades, is another. While there are many other strategies to keep alert numbers under control, a key process in alert management is the grouping of alerts, known as alert correlation. It groups similar alerts under one actionable incident, thereby reducing the number of alerts to be handled individually.

But, how is alert ‘similarity’ determined? One way to do this is through similarity definitions, in the context of that IT landscape. A definition, for instance, would group together alerts generated from applications on the same host, or connectivity issues from the same data center. This implies that similarity definitions depend on the physical and logical relationships in the environment – in other words – the topology map. Topology mappers detect dependencies between applications, processes, networks, infrastructure, etc., and construct an enterprise blueprint that is used for alert correlation.

But what about related alerts generated by entities that are neither physically nor logically linked? To give a hypothetical example, let’s say application A accesses a server S which is responding slowly, and so A triggers alert A1. This slow communication of A with S eats up host bandwidth, and hence affects another application B in the same host. Due to this, if a third application C from another host calls B, alert A2 is fired by C due to the delayed response from B.  Now, although we see the link between alerts A1 & A2, they are neither physically nor logically related, so how can they be correlated? In reality, such situations could imply thousands of individual alerts that cannot be combined.

Algorithmic Alert Correlation

This is one of the many challenges in IT operations that we have been trying to solve at GAVS. The correlation engine of our AIOps Platform ZIF uses algorithmic alert correlation to find a solution for this problem. We are working on two unsupervised machine learning algorithms that are fundamentally different in their approach – one based on pattern recognition and the other based on spatial clustering. Both algorithms can function with or without a topology map, and work around what is supplied and available. The pattern learning algorithm derives associations based on learnings from historic patterns of alert relationships. The spatial clustering algorithm works on the principle of similarity based on multiple features of alerts, including problem similarity derived by applying Natural Language Processing (NLP), and relationships, among several others. Tuning parameters enable customization of algorithmic behavior to meet specific demands, without requiring modifications to the core algorithms. Time is also another important dimension factored into these algorithms, since the clustering of alerts generated over an extended period of time will not give meaningful results.

Traditional alert correlation has not been able to scale up to handle the volume and complexity of alerts generated by the modern-day hybrid and dynamic IT infrastructure. We have reached a point where our ITOps needs have surpassed the limits of human capabilities, and so, supplementing our intelligence with Artificial Intelligence and Machine Learning has now become indispensable.

About the Authors –

Padmapriya Sridhar

Priya is part of the Marketing team at GAVS. She is passionate about Technology, Indian Classical Arts, Travel, and Yoga. She aspires to become a Yoga Instructor someday!

Gireesh Sreedhar KP

Gireesh is a part of the projects run in collaboration with IIT Madras for developing AI solutions and algorithms. His interest includes Data Science, Machine Learning, Financial markets, and Geo-politics. He believes that he is competing against himself to become better than who he was yesterday. He aspires to become a well-recognized subject matter expert in the field of Artificial Intelligence.

Cloud Adoption, Challenges, and Solution Through Monitoring, AI & Automation

Posted on by ZIF

Cloud Adoption

Cloud computing is the delivery of computing services including Servers, Database, Storage, Networking & others over the internet. Public, Private & Hybrid clouds are different ways of deploying cloud computing.  

  • In public cloud, the cloud resources are owned by 3rd party cloud service provider
  • A private cloud consists of computing resources exclusively by one business or organization
  • Hybrid provides the best of both worlds, combines on-premises infrastructure, private cloud with public cloud

Microsoft, Google, Amazon, Oracle, IBM, and others are providing cloud platform to users to host and experience practical business solution. The worldwide public cloud services market is forecast to grow 17% in 2020 to total $266.4 billion and $354.6 billion in 2022, up from $227.8 billion in 2019, per Gartner, Inc.

There are various types of Instances, workloads & options available as part of cloud ecosystem, i.e. IaaS, PaaS, SaaS, Multi-cloud, Serverless.

Challenges

When very large, large and medium enterprise decides to move their IT environment from on-premise to cloud, they try to move some/most of their on-premises into cloud and keep the rest under their control on-premise. There are various factors that impact the decision, to name a few,

  1. ROI vs Cost of Cloud Instance, Operation cost
  2. Architecture dependency of the application, i.e. whether it is monolithic or multi-tier or polyglot or hybrid cloud
  3. Requirement and need for elasticity and scalability
  4. Availability of right solution from the cloud provider
  5. Security of some key data

After crossing all, once the IT environment is cloud-enabled, the challenge comes in ensuring the monitoring of the Cloud-enabled IT environment. Here are some of the business and IT challenges

1. How to ensure the various workloads & Instances are working as expected?

While the cloud provider may give high availability & up time depending on the tier we choose, it is important that our IT team monitors the environment, as in the case of IaaS and to some extent in PaaS as well.

2. How to ensure the Instances are optimally used in terms of compute and storage?

Cloud providers give most of the metrics around the Instances, though it may not provide all metrics that we may need to make decision in all scenarios.

The disadvantage with this model is, cost, latency & not straight forward, e.g. the LOG analytics which comes in Azure involves cost for every MB/GB of data that is stored and the latency in getting the right metrics at right time, if there is latency/delay, you may not get a right result

3. How to ensure the Application or the components of a single solution that are spread across on-premise and Cloud environment is working as expected?

Some cloud providers give tools for integrating the metrics from on-premise to cloud environment to have a shared view.

The disadvantage with this model is, it is not possible to bring in all sorts of data together to get the insights straight. That is, observability is always a question. The ownership of getting the observability lies with the IT team who handles the data.

4. How to ensure the Multi-Cloud + On-Premise environment is effectively monitored & utilized to ensure the best End-user experience?

Multi-Cloud environment – With rapid growing Microservices Architecture & Container based cloud enabled model, it is quite natural that the Enterprise may choose the best from different cloud providers like Azure, AWS, Google & others.

There is little support from cloud provider on this space. In fact, some cloud providers do not even support this scenario.

5. How to get a single panel of view for troubleshooting & root cause analysis?

Especially when problem occurs in Application, Database, Middle Tier, Network & 3rd party layers that are spread across multi-cluster, multi-cloud, elastic environment, it is very important to get a Unified view of entire environment.

ZIF (Zero Incident FrameworkTM), provides a single platform for Cloud Monitoring.

ZIF has Discovery, Monitoring, Prediction & Remediate that seamlessly fits for a cloud enabled solution. ZIF provides the unified dashboard with insights across all layers of IT infrastructure that is distributed across On-premise host, Cloud Instance & Containers.

Core features & benefits of ZIF for Cloud Monitoring are,

1. Discovery & Topology

  • Discovers and provides dynamic mapping of resources across all layers.
  • Provides real-time mapping of applications and its dependent layers irrespective of whether the components live on-premise, or on cloud or containerized in cloud.
  • Dynamically built topology of all layers which helps in taking effective decisions.

2. Observability across Multi-Cloud, Hybrid-Cloud & On-Premise tiers

  • It is not just about collecting metrics; it is very important to analyze the monitored data and provide meaningful insights.
  • When the IT infrastructure is spread across multiple cloud platform like Azure, AWS, Google Cloud, and others, it is important to get a unified view of your entire environment along with the on-premise servers.
  • Health of each layers are represented in topology format, this helps to understand the impact and take necessary actions.

3. Prediction driven decision for resource optimization

  • Prediction engine analyses the metrics of cloud resources and predicts the resource usage. This helps the resource owner to make proactive action rather than being reactive.
  • Provides meaningful insights and alerts in terms of the surge in the load, the growth in number of VMs, containers, and the usage of resource across other workloads.
  • Authorize the Elasticity & Scalability through real-time metrics.

4. Container & Microservice support

  • Understand the resource utilization of your containers that are hosted in Cloud & On-Premise.
  • Know the bottlenecks around the Microservices and tune your environment for the spikes in load.
  • Provides full support for monitoring applications distributed across your local host & containers in cloud in a multi-cluster setup.

5. Root cause analysis made simple

  • Quick root cause analysis by analysing various causes captured by ZIF Monitor instead of going through layer by layer. This saves time to focus on problem-solving and arresting instead of spending effort on identifying the root cause.
  • Provides insights across your workload including the impact due to 3rd party layers as well.

6. Automation

  • Irrespective of whether the workload and instance is on-premise or on Azure or AWS or other provider, the ZIF automation module can automate the basics to complex activities

7. Ensure End User Experience

  • Helps to improve the end-user experience who gets served by the workload from cloud.
  • The ZIF tracing helps to trace each & every request of each & every user, thereby it is quite natural for ZIF to unearth the performance bottleneck across all layers, which in turn helps to address the problem and thereby improve the User Experience

Cloud and Container Platform Support

ZIF Seamlessly integrates with following Cloud & Container environments,

  • Microsoft Azure
  • AWS
  • Google Cloud
  • Grafana Cloud
  • Docker
  • Kubernetes

About the Author –

Suresh Kumar Ramasamy-Picture

Suresh Kumar Ramasamy


Suresh heads the Monitor component of ZIF at GAVS. He has 20 years of experience in Native Applications, Web, Cloud, and Hybrid platforms from Engineering to Product Management. He has designed & hosted the monitoring solutions. He has been instrumental in conglomerating components to structure the Environment Performance Management suite of ZIF Monitor.

Suresh enjoys playing badminton with his children. He is passionate about gardening, especially medicinal plants.

Is Your Investment in TRUE AI?

Posted on by ZIF

Yes, AIOps the messiah of ITOps is here to stay! The Executive decision now is on the who and how, rather than when. With a plethora of products in the market offering varying shades of AIOps capabilities, choosing the right vendor is critical, to say the least.

Exclusively AI-based Ops?

Simply put, AIOps platforms leverage Big Data & AI technologies to enhance IT operations. Gartner defines Acquire, Aggregate, Analyze & Act as the four stages of AIOps. These four fall under the purview of Monitoring tools, AIOps Platforms & Action Platforms. However, there is no Industry-recognized mandatory feature list to be supported, for a Platform to be classified as AIOps. Due to this ambiguity in what an AIOps Platform needs to Deliver, huge investments made in rosy AIOps promises can lead to sub-optimal ROI, disillusionment or even derailed projects. Some Points to Ponder…

  • Quality in, Quality out. The value delivered from an AIOps investment is heavily dependent on what data goes into the system. How sure can we be that IT Asset or Device monitoring data provided by the Customer is not outdated, inaccurate or patchy? How sure can we be that we have full visibility of the entire IT landscape? With Shadow IT becoming a tacitly approved aspect of modern Enterprises, are we seeing all devices, applications and users? Doesn’t this imply that only an AIOps Platform providing Application Discovery & Topology Mapping, Monitoring features would be able to deliver accurate insights?
  • There is a very thin line between Also AI and Purely AI. Behind the scenes, most AIOps Platforms are reliant on CMDB or similar tools, which makes Insights like Event Correlation, Noise Reduction etc., rule-based. Where is the AI here?
  • In Gartner’s Market Guide, apart from support features for the different data types, Automated Pattern Discovery is the only other Capability taken into account for the Capabilities of AIOps Vendors matrix. With Gartner being one of the most trusted Technology Research and Advisory companies, it is natural for decision makers to zero-in on one of these listed vendors. What is not immediately evident is that there is so much more to AIOps than just this, and with so much at stake, companies need to do their homework and take informed decisions before finalizing their vendor.
  • Most AIOps vendors ingest, provide access to & store heterogenous data for analysis, and provide actionable Insights and RCA; at which point the IT team takes over. This is a huge leap forward, since it helps IT work through the data clutter and significantly reduces MTTR. But, due to the absence of comprehensive Predictive, Prescriptive & Remediation features, these are not end-to-end AIOps Platforms.
  • At the bleeding edge of the Capability Spectrum is Auto-Remediation based on Predictive & Prescriptive insights. A Comprehensive end-to-end AIOps Platform would need to provide a Virtual Engineer for Auto-Remediation. But, this is a grey area not fully catered to by AIOps vendors.  

The big question now is, if an AIOps Platform requires human intervention or multiple external tools to take care of different missing aspects, can it rightfully claim to be true end-to-end AIOps?

So, what do we do?

Time for you to sit back and relax! Introducing ZIF- One Solution for all your ITOps ills!

We have you completely covered with the full suite of tools that an IT infrastructure team would need. We deliver the entire AIOps Capability spectrum and beyond.

ZIF (Zero Incident Framework™) is an AIOps based TechOps platform that enables proactive Detection and Remediation of incidents helping organizations drive towards a Zero Incident Enterprise™.

The Key Differentiator is that ZIF is a Pure-play AI Platform powered by Unsupervised Pattern-based Machine Learning Algorithms. This is what sets us a Class Apart.

  • Rightly aligns with the Gartner AIOps strategy. ZIF is based on and goes beyond the AIOps framework
  • Huge Investments in developing various patented AI Machine Learning algorithms, Auto-Discovery modules, Agent & Agentless Application Monitoring tools, Network sniffers, Process Automation, Remediation & Orchestration capabilities to form Zero Incident Framework™
  • Powered entirely by Unsupervised Pattern-based Machine Learning Algorithms, ZIF needs no further human intervention and is completely Self-Reliant
  • Unsupervised ML empowers ZIF to learn autonomously, glean Predictive & Prescriptive Intelligence and even uncover Latent Insights
  • The 5 Modules can work together cohesively or as independent stand-alone components
  • Can be Integrated with existing Monitoring and ITSM tools, as required
  • Applies LEAN IT Principle and is on an ambitious journey towards FRICTIONLESS IT.

Realizing a Zero Incident EnterpriseTM

Optimizing ITOps for Digital Transformation

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The key focus of Digital Transformation is removing procedural bottlenecks and bending the curve on productivity. As Chief Insights Officer, Forbes Media says, Digital Transformation is now “essential for corporate survival”.

Emerging technologies are enabling dramatic innovations in IT infrastructure and operations. It is no longer just about hardware, software, data centers, the cloud or the service desk; it is about backing business strategies. So, here are some reasons why companies should think about redesigning their IT services to embrace digital disruption.

DevOps for Agility

As companies move away from the traditional Waterfall model of software development and adopt Agile methodologies, IT infrastructure and operations also need to become agile and malleable. Agility has become indispensible to stay competitive in this era of dynamism and constant change. What started off as a set of software development methodologies has now permeated all aspects of an organization, ITOps being one of them. Development, QA and IT teams need to come out of their silos and work in tandem for constant productive collaboration, in what is termed DevOps.

Shorter development & deployment cycles have necessitated overall ITOps efficiency and among other things, IT enviroment provisioning to be on-demand and self-service. Provisioning needs to be automated and built into the CI/CD pipeline.  

Downtime Mitigation

With agility being the org-wide mantra, predictable IT uptime becomes a mandate. Outages incur a very high cost and adversely affect the pace of innovation. The average cost of unplanned application downtime for Fortune 1000 companies is anywhere between $1.25 billion to $2.5 billion, says a report by DevOps.com. It further goes on to say that, infrastructure failure can cost the bottom line $100,000/hr and the cost of critical application failure is $500,000 to $1 million/hr.

ITOps must stay ahead of the game by eliminating outdated legacy systems, tools, technologies and workflows. End-to-end automation is key. IT needs to modernize its stack by zeroing-in on tools for Discovery of the complete IT landscape, Monitoring of devices, Analytics for noise reduction and event correlation, AI-based tools for RCA, incident Prediction and Auto-Remediation. All of this intelligent automation will help proactive response rather than a reactive response after the fact, when the damage has already been done.

Moving away from the shadows

Shadow IT, the use of technology outside the IT purview, is becoming a tacitly approved aspect of most modern enterprises. It is a result of proliferation of technology and the cloud offering easy access to applications and storage. Users of Shadow IT systems bypass the IT approval and provisioning process to use unauthorized technology, without the consent of the IT department. There are huge security and compliance risks waiting to happen if this sprawling syndrome is not reined in. To bring Shadow IT under control, the IT dept must first know about it. This is where automated Discovery tools bring in a lot of value by automating the process of application discovery and topology mapping.

Moving towards Hybrid IT

Hybrid IT means the use of an optimal, cost-effective mix of public & private clouds and on-premise systems that enable an infrastructure that is dynamic, on-demand, scalable, and composable. IT spend on datacenters is seeing a downward trend. Most organizations are thinking beyond traditional datacentres to options in the cloud. Colocation is an important consideration since it delivers better availability, energy and time savings, scalability and reduces the impact of network latency. Organizations are only keeping mission-critical processes that require close monitoring & control, on-premise.

Edge computing

Gartner defines edge computing as solutions that facilitate data processing at or near the source of data generation. With huge volumes of data being churned out at rapid rates, for instance by monitoring or IoT devices, it is highly inefficient to stream all this data to a centralized datacenter or cloud for processing. Organizations now understand the value in a decentralized approach to address modern digital infrastructure needs. Edge computing serves as the decentralized extension of the datacenter/cloud and addresses the need for localized computing power.

CyberSecurity

Cyber attacks are on the rise and securing networks and protecting data is posing big challenges. With Hybrid IT, IoT, Edge computing etc, extension of the IT footprint beyond secure enterprise boundaries has increased the number of attack target points manifold. IT teams need to be well versed with the nuances of security set-up in different cloud vendor environments. There is a lot of ambiguity in ownership of data integrity, in the wake of data being spread across on-premise, cloud environments, shared workstations and virtual machines. With Hybrid IT deployments, a comprehensive security plan regardless of the data’s location has gained paramount importance.

Upskilling IT Teams

With blurring lines between Dev and IT, there is increasing demand for IT professionals equipped with a broad range of cross-functional skills in addition to core IT competencies. With constant emergence of new technologies, there is usually not much clarity on the exact skillsets required by the IT team in an organization. More than expertise in one specific area, IT teams need to be open to continuous learning to adapt to changing IT environments, to close the skills gap and support their organization’s Digital Transformation goals.

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AIOps – IT Infrastructure Services for the Digital Age

Posted on by ZIF

The IT infrastructure services landscape is undergoing a significant shift, driven by digitalization. As focus shifts from cost efficiency to digital enablement, organizations need to re-imagine the IT infrastructure services model to deliver the necessary back-end agility, flexibility, and fluidity. Automation, analytics, and Artificial Intelligence (AI) – comprising the “codifying elements” for driving AIOps – help drive this desired level of adaptability within IT infrastructure services. Automation, analytics, and AI – which together comprise the “codifying elements” for driving AIOps– help drive the desired level of adaptiveness within IT infrastructure services. Intelligent automation, leveraging analytics and ML, embeds powerful, real-time business and user context and autonomy into IT infrastructure services. Intelligent automation has made inroads in enterprises in the last two to three years, backed by a rapid proliferation and maturation of solutions in the market.

Artificial Intelligence Operations (AIOps) . Everest Group 2018 Report . IT Infrastructure

Benefits of codification of IT infrastructure services

Progressive leverage of analytics and AI, to drive an AIOps strategy, enables the introduction of a broader and more complex set of operational use cases into IT infrastructure services automation. As adoption levels scale and processes become orchestrated, the benefits potentially expand beyond cost savings to offer exponential value around user experience enrichment, services agility and availability, and operations resilience. Intelligent automation helps maximize value from IT infrastructure services by:

  1. Improving the end-user experience through contextual and personalized support
  2. Driving faster resolution of known/identified incidents leveraging existing knowledge, intelligent diagnosis, and reusable, automated workflows
  3. Avoiding potential incidents and improving business systems performance through contextual learning (i.e., based on relationships among systems), proactive health monitoring and anomaly detection, and preemptive healing

Although the benefits of intelligent automation are manifold, enterprises are yet to realize commensurate advantage from investments in infrastructure services codification. Siloed adoption, lack of well-defined change management processes, and poor governance are some of the key barriers to achieving the expected value.  The design should involve an optimal level of human effort/intervention targeted primarily at training, governing, and enhancing the system, rather than executing routine, voluminous tasks.  A phased adoption of automation, analytics, and AI within IT infrastructure services has the potential to offer exponential business value. However, to realize the full potential of codification, enterprises need to embrace a lean operating model, underpinned by a technology-agnostic platform. The platform should embed the codifying elements within a tightly integrated infrastructure services ecosystem with end-to-end workflow orchestration and resolution.

The market today has a wide choice of AIOps solutions, but the onus is on enterprises to select the right set of tools / technologies that align with their overall codification strategy.

Click here to read the complete whitepaper by Everest Group

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