Bishnu Nayak, Author at FixStream

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Posts By : Bishnu Nayak

Not All AIOps Solutions are Created Equal

As our market evolves, a number of AIOps solutions have entered the market. It is hard to discern the differences between the offerings. This article helps you understand the core features that you should look for when evaluating these types of solutions. This article was previous published in DZone.

As enterprises accelerate the digital transformation of their business, they’ve increased their dependency on always-on, high-performing business processes. As such, it’s critical that these mission-critical applications perform optimally and are always available to users. To improve system availability and aid troubleshooting, organizations have turned to AIOps (Artificial Intelligence for IT Operations), a technology based on AI and Machine Learning, to automate the identification and remediation of numerous IT issues and automate day to day IT operations activities.

AIOps is helping organizations cope with the challenges of digital business transformation, siloed IT operations and the exponential growth of operational data such as logs, alerts, network faults and performance data generated by the typical digital enterprise. AIOps can use that data to understand dependencies between IT entities across domains, observe the health of critical IT assets, understand business impacts and improve visibility into root cause of system outages and slowdowns.

Harness the Power of Big Data with AIOps

Using big data analytics and machine learning algorithms, AIOps solutions can ingest and aggregate multiple streams of metrics, events and logs to filter through noise and uncover problems. They can provide IT departments with valuable insights into complex cloud-based and virtualized environments, system problems, and business-impacting issues. But the success of the AIOps platform depends on its access to cleanse the disparate data gathered from all across the hybrid IT ecosystem, using various data lineage and relationships. Without the complete set of data and the data relationships, the AIOps system can’t analyze and learn from it, and its success is limited.

Why is this? At its core, AIOps is data-driven, so it requires access to all relevant operations data, including unstructured machine data such as logs, metrics, streaming data, API outputs, and device data, and structured data such as databases. In order to eliminate false positives and accurately identify cause vs impacts, anomalies across related entities, AIOps solutions must utilize relationships across the entities in the machine learning algorithms.   

AIOps technology learns from the input data source to identify trends and patterns to provide an early warning whenever it discovers anomalies or reoccurrence of a known pattern indicating business impacting incident. By correlating and analyzing data from multiple enterprise application and infrastructure domains, the right AIOps solution can reveal trends and patterns within the “noise” of millions of system incident reports, highlighting potential risks and performance issues. It can also uncover patterns to show what has occurred, a boon to system diagnostics and predictive analytics.

Not all AIOps are created equal

AIOps is a multi-layered platform whose capabilities should include efficient data collection at big data scale, correlation across the collected data, machine learning, analytics and visualization. Most AIOps vendors focus on capabilities of data ingestion, and machine learning for noise reduction and root-cause failure analysis. However, when shopping for an AIOps platform, it helps to remember that AIOps solutions come with varying functionality and ability to manage data sources.                

Cross-Domain Data correlation provides valuable insight

Successful AIOps platforms need to be able to collect data from the entire multi-vendor and multi-domain environment, including network and storage solutions, containers, and public cloud. So, it’s important to select an AIOps platform that can ingest, correlate, and provide access to a broad range of historical and streaming data types. This will enable a broader analysis of trends and issues within the distributed hybrid IT ecosystem and avoid blind spots.

The AIOps solution’s value lies in its ability to ingest and correlate data across the siloed hybrid environment, helping organizations deal with the high volume, variety and velocity of data generated by today’s complex IT operations. Within the data, the AIOps platform can uncover patterns, which can then be used historically to identify the root causes of specific system issues in real time, or proactively to predict potential problems.

Data correlation capabilities have other benefits as well, such as revealing application dependencies and what specific resources each application requires. The AIOps platform may also be able to unite the power of machine learning and big data with domain knowledge to identify a multitude of data relationships and interdependencies. This insight can help IT managers better allocate resources, plan migrations, and purchase only what their cloud-based applications really need.

Enhance digital business operations with AIOps

Today’s modular, dynamic and distributed IT systems require a new multi-perspective approach to fully understand how they’re operating. One approach is to implement AIOps solutions capable of ingesting, correlating, and analyzing data from a number of sources and across IT siloes. This ability to consolidate and analyze system information gives IT teams the ability to more quickly diagnose system issues and resolve proactively before it impacts business

Business applications are the lifeblood of every digital enterprise. With more mission-critical applications based in the cloud, managers need better tools to understand and track how those applications are performing. Done right, an AIOps implementation can lead to a decreased mean time to remediation and better proactive problem solving. By increasing system availability and responsiveness, AIOps can enhance your digital business operations and improve profitability.

Avoid “IT Firefighting” With AIOps

By Bishnu Nayak — CTO, FixStream

Originally Posted on DZONE

In today’s 24×7 IT environments, nothing is more important than avoiding system outages and slowdowns that impact business. Without ready access to the desired applications, frustrated workers and customers are unable to complete their transactions. Business grinds to a halt, revenue is lost, and corporate reputation is damaged.

But manually detecting and diagnosing system glitches across a multi-layered, siloed infrastructure is time-consuming and cumbersome. Outdated domain-centric tools leave IT specialists unable to proactively troubleshoot and repair system issues. Your IT team can end up “fighting fires” rather than working on the important projects that add value to the business.

Solution: Predictive Analytics for Continuous Oversight of IT Operations

Predictive analytics, an emerging category of big data analytics, can help organizations predict future outcomes based on historical data. When reviewing the data, analysts can detect trends and patterns that may highlight risks, correlations, or current as well as future conditions.

Already used for applications such as inventory forecasts and customer service, predictive analytics can uncover abnormal trends, detect threats, and forecast issues before they impact operations and create emergencies. Examples include:

Multi-variate anomaly detection can identify anomalies in applications behaving abnormally. For example, utilization spikes on Monday are normal, while a similar surge on Sunday may indicate a security threat.

  • Capacity prediction — Don’t pay for unused servers or be caught short-footed by unanticipated server demand. Use analytics to forecast and optimize system resources usage, while minimizing your operational footprint.
  • Incident prediction — Predictive analytics, enhanced by data mining, can help analysts interpret the structured and unstructured data recorded in tickets. The results can be used to highlight and fix potential failures.

AIOps: Fixing IT Problems Before They Happen

Powered by Machine Learning, AIOps has advanced analytical capabilities to help IT organizations forecast and avoid system issues. Using its Artificial Intelligence capabilities, AIOps can be taught to observe and recognize patterns and anomalies over time. Then it can automatically analyze massive amounts of digital data, correlate leading indicators, and use historical behavior to help predict what could happen next. By delivering contextual operational insight, AIOps can help your team predict and prevent business outages before they cause actual problems.

AIOps can be taught to examine data trends and provide an early warning whenever it discovers possible issues. The application can detect trends and patterns within the “noise” of millions of system incident reports, highlighting potential risks and performance issues.

When system outages occur, the predictive insights from AIOps can speed up root-cause analysis and remediation. By quickly and accurately diagnosing the root cause, problems are fixed faster, often reducing downtime from hours to just minutes. The result is optimized system availability and enhanced business operations.


Depending on the platform, AIOps can quickly predict business application issues across an enterprise’s entire IT stack. Using its end-to-end view across all domains, AIOps helps customers rapidly identify issues and predict outages, so they can resolve problems proactively and avoid IT firefighting.

FixStream 2019 Predictions: Digital Transformation Powered by AIOps

By Bishnu Nayak, Chief Technology Officer, FixStream

Original Posted in

Digital Transformation Powered by AIOps

In a recent press release, industry analyst firm IDC made ten predictions for 2019 which center on the ongoing digitization of the global economy. Underpinned by 3rd Platform technologies such cloud, mobile, and big data analytics, IDC reminds us that digital innovation is transforming industries and regions, and it’s imperative that organizations adapt. As Frank Gens, senior vice president and chief analyst at IDC, pointed out in the press release. “The ability to accelerate digital innovation volume and pace will be the most critical new benchmark for organizations competing in the digital economy”.

The need to keep pace with this digital acceleration means that yesterday’s toolsets and practices-which depend on human skills and manual processes-are no longer effective. Today’s interconnected hybrid cloud IT environments are too complex and siloed, not to mention mission critical, for traditional approaches. Users have very high expectations of system performance, and uptime of critical business applications, so IT Operations need new tools and approaches.

Let’s look at one example to illustrate the challenge. ITSM functions such as change management, asset management, and incident management heavily depend on the accuracy and richness of the data maintained in the underlying CMDB. Yet, when modern data centers leverage emerging technologies such as virtualization, containers, micro services, and/or hybrid cloud to deliver digital services, traditional CMDB’s struggle to keep up. Changes across the infrastructure, such as the addition or deletion of VM’s or containers, or new micro service deployments, may or may not be recorded. Often, the CMDB can’t manage the frequency of changes, or maintain the dependencies and relationships across CIs in an accurate, complete and timely fashion.

Artificial Intelligence for IT Operations (AIOps), the next generation of IT operations analytics, can address this issue and more. AIOps solutions can more efficiently and accurately implement changes and handle incidents that impact critical business services. For example, AIOps can discover interdependencies and relationships of CI’s across the infrastructure and business applications, ensuring the CMDB stays accurate and current. This in turn enriches and optimizes the ITSM functions required for successful delivery and operations of digital services.

Business impacts from new changes can be easily determined and forecasted using AiOps solution enabling enterprises to quickly roll out updates such as new technology, system patches, upgrades/maintenance updates, or new vendor applications. Better yet, AIOps event correlation can quickly identify the root cause of applications negatively impacted by system change done incorrectly.

Industry analysts also see the valuable role that AIOps plays in digital transformation. A Sept. 2018 blog by EMA Vice President Dennis Drogseth reviews the results of a study the firm conducted on a range of AIOps topics. In the survey of 300 ITOps professionals, 87% revealed they are already using an AIOps platform, with another 13% in the planning or testing stage. 54% of respondents identified the CMDB as ‘extremely important’ to their analytic strategy. Drogseth explains that investment in AIOps solution can reinvigorate their CMDB/CMS and application/infrastructure dependency mapping technology areas.

Studies like this make us feel confident in predicting that, in 2019, more and more organizations will adopt AIOps to accelerate their digital business transformation.

Faster Problem Resolution with AI-Enabled Predictive Analytics

Outdated Technology and IT Complexity Hampers Issue Resolution

The exponential growth of real-time business transactions, coupled with dynamic hybrid and software-based technologies, make even routine IT operations difficult when using outdated domain-centric tools. As application environments become more complex and distributed, and change more frequently, there are more failure indicators generated than what can be investigated using traditional approaches.

Detecting and diagnosing system problems manually is time-consuming and cumbersome. Inadequate IT operations management tools complicate the identification, analysis and resolution of system issues, as IT specialists struggle to establish a clear, complete view of what’s happening across the various technologies. As a result, operations teams often lack the insight needed to proactively monitor, troubleshoot and manage system environments. This hampers IT’s ability to detect and resolve incidents when a system disruption occurs.

AIOps – Fixing IT Problems Before They Happen

Powered by machine learning, AIOps has advanced analytical capabilities to help IT organizations forecast and resolve system issues. Using its artificial intelligence capabilities, AIOps can be taught to observe and recognize patterns and anomalies over time. Then it will be able to automatically analyze massive amounts of digital data, correlate leading indicators, and use historical behavior to help predict what could happen next. Power of analytics combined with correlation across IT domains delivers contextual operational insight. The insight gained from reviewing the results can help your team predict and prevent business outages, even before they cause actual problems.

AIOps can be taught to examine data trends and provide an early warning whenever it discovers possible issues. For example, the application can detect trends and patterns within the “noise” of millions of system incident reports, helping analysts uncover potential risks and performance issues. By automatically applying proactive and predictive insights, AIOps can help IT organizations diagnose and resolve system threats and performance risks, forestalling a damaging outage. This increases system uptime and performance, driving better outcomes for the business.

When system outages do occur, the predictive insights from AIOps can speed up root-cause analysis and remediation. By quickly and accurately diagnosing the root cause, your team can find and fix problems faster, often reducing downtime from hours to just minutes. The result is optimized system availability and enhanced business operations.


AIOps predictive analytics can provide up-to-date insight into your organization’s entire application and IT infrastructure resources, which can lead to a better understanding of the interdependencies between system components. This provides CIOs with valuable knowledge to better manage the increasing complexity and dynamic nature of their sprawling hybrid IT architectures.

The FixStream AIOps platform can quickly predict business application issues across an enterprise’s entire hybrid IT stack. Using its end-to-end view across all domains, the AIOps platform helps customers rapidly identify issues and predict outages, so they can save time and solve problems faster. For more information on how FixStream’s AIOps Platform can predict failure points across hybrid IT stacks, read our press release.

Diagram: Hybrid IT Infrastructure, Applications, Business Transactions
FixStream Launches Industry’s First Visual Artificial Intelligence Platform To Predict Business Application Issues Across Hybrid IT

AIOps Platform Combines Auto-Discovery, Correlation and Visualization with Machine Learning to Predict Failure Points Across Hybrid IT Stacks

SAN JOSE, CA —May 15, 2018 FixStream, a pioneer in solutions for Artificial Intelligence for IT operations (AIOps), today introduced an advanced version of its product, the industry’s first AIOps platform to quickly predict business application issues across an enterprise’s entire hybrid IT stack. With new Machine Learning (ML) algorithms, advanced multi-layer correlation from business transactions to application services and infrastructure, FixStream can now rapidly identify issues that can significantly impact business in minutes instead of hours.  The new FixStream AIOps platform can quickly detect patterns to predict and prevent future business outages, modernizing IT operations management while increasing revenues, customer satisfaction, and business agility.

With the rapid pace of digital transformation and the need for on-demand access 24/7, companies rely on their applications to do business more than ever before.  When an application goes down even for just a few minutes, IT can take days or weeks to resolve issues, costing millions of dollars in revenue. It is very challenging for IT teams to pinpoint and resolve these issues due to the increasing growth of real-time business transactions, and the complex nature of today’s dynamic hybrid and software-defined technologies. Today’s domain-centric tools and technologies make routine IT operations cumbersome and time-consuming, putting companies’ business at risk.

“We are modernizing IT, giving it a seat at the executive table,” said Sameer Padhye, Founder and CEO of FixStream. “FixStream’s multi-layer correlation, visualization and machine learning capabilities across business KPI’s, applications and infrastructure is a gamechanger, reducing the complexity of how enterprises identify issues across their IT infrastructure and saving millions in revenue by predicting outages in the future.”

New Machine Learning (ML) Capabilities to Detect Patterns and Prevent Business Outages
FixStream applies a proprietary machine learning algorithm to contextually correlated data to automatically discover patterns, so IT operations can predict and visualize future outages. As a result of deep learning insights, IT teams can significantly reduce troubleshooting efforts from weeks, months, or days down to minutes. New ML capabilities include:

  • Dynamic Thresholding and Multivariate Anomaly Detection – IT staff can now identify a sequence or group of anomaly events for transactions, applications and infrastructure entities that may impact a business application (i.e.-unplanned events such as DDOS attack or better plan for Black Friday)
  • Sequential Pattern Analysis for Incident Prediction – IT staff can now identify and visualize in seconds the sequence of correlated events that have impacted a specific application or business process and identify patterns with probabilities to predict future incidents (i.e.- predict eCommerce transactions such as ordering new service or paying bills will stop working after one hour due to a detected alert to prevent the incident from happening).
  • Disk/Network Bandwidth Predictive Analytics – IT teams can now analyze historical trends around the utilization of an infrastructure resource and predict when the infrastructure entity will run out of capacity. Now IT operations can proactively plan to add more capacity before an event negatively impacts the business.

“FixStream’s end-to-end view across all domains – business-critical applications and infrastructure – and its ability to accelerate IT problems resolution is unlike any operations platform I have seen before, capable of reducing MTTR by four times,” said Mike Fischer, CDO at Hertz and former SVP, Global IT Operations at First Advantage. “As networks get more dynamic, finding out why outages are happening is more challenging for IT executives. The FixStream AIOps platform will give them unprecedented insights into their companies’ mission-critical business processes to rapidly identify issues and predict outages, saving crucial time and allowing them to solve problems faster.”

“FixStream’s approach to infrastructure management is unique in that it addresses the user experience holistically – from the application to the infrastructure that supports it,” said Mike Jude, Research Manager, Big Data Analytics, Stratecast, Frost & Sullivan. “The application of AI technologies to preemptively address user impacting issues before the user sees them is innovative, and combining such capabilities with an end-to-end management approach means that FixStream is likely to predict more faults before they can occur than other infrastructure management tools.”

About FixStream
FixStream is the Artificial Intelligence company for IT Operations. With FixStream’s industry-first multi-layer correlation, visualization and prediction of business application issues across an enterprise’s entire hybrid IT stack, IT operations can now get real-time insights and powerful analytics to prevent outages and increase revenues.

For additional information, visit, or connect with FixStream on LinkedIn and Twitter.

Jennifer Chau, 408-394.4478

Flying High With AI How Machine Learning Powers AIOps, Correlation & Analytics

Flying High With AI
How Machine Learning Powers AIOps, Correlation & Analytics

By Bishnu Nayak

Digital disrupters have accelerated the pace of business. They’ve prompted digital transformation across organizations and industries. That has led to more departments within more businesses adopting more connected applications.

That has created greater reliance on underlying enterprise networks on which these critical business applications run. The infrastructure is becoming more distributed, heterogeneous, intelligent, open, and virtualized to support the growth, agility, and scale of the business applications.

Application architecture is changing to adopt newer technologies such as containers. That allows deployment of the application entities in multiple data center and cloud environments, which can scale dynamically. (In October, DockerCon Europe reported that 24 billion containers have been downloaded.)

Business network elements frequently come from a wide variety of hardware and software suppliers. And these networks are only becoming more diverse given the movement by business networking professionals to avoid vendor lock-in, embrace open architectures, and use best-of-breed solutions.

The changes in the dynamic application environment happen very abruptly. So it’s impossible to track the changes and correlate the events using legacy techniques. Manual processing of massive amounts of data – which is dynamic across the stacks – to identify patterns, anomaly scenarios, and predict capacity requirements is almost impossible. That, in turn, poses tremendous business risks and hinders business innovation

So, what’s the solution?

A solution that combines the power of machine learning with the ability to auto-discover and correlate entities across critical layers of digital business – business, application, and infrastructure.

Artificial intelligence and machine learning are not a replacement for people in this scenario. Rather, they help humans perform day to day IT operational tasks such as troubleshooting, capacity management, migration, and planning. There are companies that use AI in event driven architecture, if you are interested in finding out more you might be interested in visiting somewhere like

“Most recent advances in AI have been achieved by applying machine learning to very large data sets,”

notes McKinsey & Co. “Machine-learning algorithms detect patterns and learn how to make predictions and recommendations by processing data and experiences, rather than by receiving explicit programming instruction. The algorithms also adapt in response to new data and experiences to improve efficacy over time.”

Machine learning can correlate and analyze data, (using something similar to data lake) from multiple enterprise application and infrastructure domains, dealing with the volume, velocity, and varieties of data generated. It can uncover patterns to show what has occurred. It can use current conditions and past learning to spot exceptions and predict the future. Machine learning can even offer suggestions on what to do in various scenarios.

AIOps platforms leverage machine learning to deliver AI capabilities for IT operations. Here are some interesting use cases.

  • Multivariate anomaly detection can identify anomaly scenarios across various dependent entities. Such anomalies may signal that a planned or unplanned business event has taken place. For example, a multivariate anomaly group may represent an unplanned event like a DDoS cyberattack or a planned business effort such as Black Friday event.
  • A time-series sequential pattern detection algorithm can predict business outages triggered by events anywhere in the stack business functions are deployed.
  • It’s also possible to use AI and machine learning to predict when you’ll run out of capacity. For example, it could signal a potential lack of storage disk volume and excessive network bandwidth use of a router. Such information helps IT experts do proactive capacity planning to better meet business needs.

Machine learning automates IT operations and can notify operations teams of potential business outages before they happen. It also can detect security issues, identify infrastructure performance bottlenecks, and recommend capacity augmentation and optimization.

IT teams can then set systems to trigger actions for remediation. Executing remediation scripts or integrating with other orchestration and automation tools to take actions minimizes human tasks.

Proactively detecting issues and fixing such issues enables business continuity and assures customer satisfaction. In the age of digital transformation, such capabilities and AIOps solutions are an absolute must.

With machine learning, IT staff can continually and completely look for traffic exceptions. So IT experts can be far more effective in preventing and quickly responding to cyberattacks with the use of pentesting and vulnerability management. So businesses can stay up and running, and stay out of the headlines.

These are just a few reasons why AI and machine learning have become key components of digital transformation. And that’s only going to accelerate moving forward.

“During the next few years, the technologies associated with this [digital transformation] wave — including artificial intelligence, cloud computing, online interface design, the Internet of Things, Industry 4.0, cyberwarfare, robotics, and data analytics — will advance and amplify one another’s impact,”

note PwC analysts Leslie H. Moeller, Nicholas Hodson, and Martina Sangin.

Forrester Research says more than half of organizations already have implemented some form of an AI project. And it says another 20 percent are planning AI projects in the near future.

Your business and its IT staff should be thinking about how you can benefit from AI and machine learning too.

If you’re still on the fence, think of it like this. Machine learning is to network operations as air traffic control is to airline operations.

There are about 5,000 airplanes in the sky every hour in the U.S.
So you can’t use manual processes to track planes as they move around. It would be near impossible, and just plain dangerous.

So we use air traffic control to manage the chaos. The air traffic control system helps experts keep track of all the traffic (airplanes) among the different domains (various airports and airlines).

By bringing together the various data points and presenting a complete view of what’s happening, air traffic control helps avoid crashes and enables smoother traffic flow.

Machine learning likewise enables data correlation and analytics. That way, IT experts can keep the network and its applications running safely and on time. And that allows organizations to deliver better and safer customer experiences, make better use of their human and technological resources, and keep their applications and businesses moving forward.

That’s why artificial intelligence and machine learning are key technology enablers of the FixStream AIOps solution. They’re the AI in AIOps.

On behalf of FixStream and the entire crew, I’d like to thank you for joining us on this trip. We look forward to seeing you on board again in the near future. Have a nice day. (Sorry, I couldn’t resist!)

In my next blog, I’ll talk about automation.

Bishnu Nayak is the CTO for FixStream.

How AIOps Can Ensure SAP ERP Performance, Availability

How AIOps Can Ensure SAP ERP Performance, Availability

By Bishnu Nayak

All major businesses these days use enterprise resource planning (ERP) platforms such as CarryCulum. You probably use one yourself.

As you probably already know, ERP software helps organizations more efficiently handle billing, customer management, human resources matters, ordering, provisioning, supply chain management, and more. ERP software also can allow for better decision-making and increased agility.

Clearly, the standard ERP system has a lot of functionality. And businesses rely on that functionality to keep their organizations up-and-running and the wheels of industry turning.

A Complex Situation

Because these platforms do so much, they are quite complex and consume lots of resources. ERP systems employ an array of software running on various infrastructure in many data centers.

So there are lots of moving pieces, and those piece parts can be widely distributed. That creates a lot of opportunity for problems that can adversely impact ERP performance and customer SLAs.

For example, a problem with a server or a network switch or router can cause a kink in a company’s invoicing or order booking process. Whilst the issue with the router may be solvable quickly by accessing the login info (such as the Netgear here listed), this kind of thing can interfere with an organization providing its customers with their bills.

You don’t need me to tell you that is a big problem.

The Cost of Delay & Outages

This kind of a situation can confuse and frustrate customers. Worse yet, it can mean late or lost payments, creating cash flow issues for the company providing the product or service.

But that’s just one example of the kind of thing that can go wrong. There are plenty of real-life examples of how ERP- and IT-related outages hurt businesses.

A few years back HSBC had a problem with a software update. As a result, thousands of the bank’s customers couldn’t cash their paychecks. Worse yet, this occurred just before a holiday weekend.

More recently, the failure of a power system in a British Airways data center resulted in canceled flights affecting more than 75,000 passengers. The airlines ended up paying $68 million in passenger reimbursement costs. And its parent company experienced a 2.8 percent stock price drop.

According to Gartner, the average cost of IT downtime is $5,600 per minute – or $300,000 an hour.

So businesses clearly want to avoid these kinds of scenarios. But it can be challenging to identify and address the sources of such issues. That challenge is even more daunting when it involves a distributed, multi-application ERP.

That’s why FixStream introduced AIOps in SAP. This solution provides multilayer correlation for SAP ERP users like you.

Ensuring Availability & Performance

AIOps in SAP allows you to quickly understand problems so you can take action to correct them before it impacts your customers and your business. That helps your business to increase customer satisfaction, better retain customers, protect your reputation, attract new customers, and grow your customer spend.

The top layer of FixStream’s AIOps in SAP focuses on business process KPI and SLA. It knows how many orders should be processed in an hour. It also understands how long it should take for an order to be processed from beginning to end.

The application layer is the second tier of AIOps in SAP. It addresses every application in SAP (or Oracle, the other major ERP supplier). That could be a server, a database, or a business process defined within the application tier.

The third layer is the infrastructure layer. That’s made up of physical and virtual compute, network, and storage resources.

FixStream’s AIOps correlates data from those three layers and applies an algorithm on top of it. This data correlation and analysis enables AIOps in SAP to address a variety of use cases.

The Use Cases

For example, AIOps in SAP flags when an SLA violation occurs because processing of customer orders is stuck. FixStream’s AIOps then sets out to identify the source of the trouble via data correlation and analysis.

AIOps in SAP also can predict when things are headed for trouble. It does that by identifying repeating patterns and dependencies.

For example, FixStream’s AIOps can understand from past data that Black Friday is a high-volume order day. And it can see that when you have more than X number of orders, your order processing getter slower. This kind of information enables your business to address that potential problem – by allocating more resources to enable fast ordering during Black Friday – so your ordering process continues to move forward at the desired pace.

FixStream also addresses capacity management. So if, for example, the number of orders your ERP processes has been ramping up monthly over the past nine months, AIOps in SAP reveals that. FixStream also provides you with a holistic view of what you have and what your business needs in terms of compute power, network resources, and storage. That means you won’t be caught off-guard by changing resource requirements.

There’s one more really important thing that AIOps in SAP addresses: IT compliance.

FixStream automates IT compliance efforts, which today are typically done using manual processes. We enable that via FixStream’s data explorer capability.

Our data explorer provides automated, up-to-date reports of all domains. That reduces the time it takes for IT personnel to ensure compliance.

As a result, IT teams can more quickly and easily see if the organization is running the latest software release, has the needed patches in place, and the like. That beats today’s manually-intensive audits, which require more IT team resources and can take one or two months to complete. So IT team can focus on value-added efforts and spend less time just keeping the lights on.

ERP Assurance & IT Resource Optimization

Forrester has reported that 69 percent of IT budgets go to maintenance and operations. The other 31 percent go to new projects – with just 14 percent of that going to investments for customer-facing and new business opportunities.

But there’s a widespread acknowledgement that digital transformation is changing the role of IT and how IT dollars should be allocated.

So IT teams need sophisticated – but easy-to-use – tools so they can ensure the performance and availability of key applications. That includes the applications supported by ERP systems, on which their employers and customers rely.

FixStream AIOps facilitates digital transformation by automating the discovery and mapping of critical processes like order-to-cash to the underlying application and infrastructure entities. By doing so, it enables IT operations to analyze mission-critical business processes, reduce mean time to repair, and predict occurrence of issues across any portion of their hybrid IT stack.

It provides customizable, single-pane-of-glass views of SAP ERP businesses processes and their operational health. It groups applications into logical business processes like O2C, P2P, SCM, and eCommerce. It provides auto-discovery of SAP ERP business processes and applications across hybrid IT environments. And it visualizes data to help people easily see what’s happening.

And, as noted earlier, it does automated correlation of SAP ERP business KPIs to application and system errors; allows for faster root cause identification of SAP ERP issues; and detects anomalies and patterns to allow businesses to address issues before they affect business operations and customers.

In my next blog I’ll tackle the subject of migration.

FixStream Accelerates the Delivery of Business Workloads in a Nutanix Enterprise Cloud Environment

As originally published on

This guest post was authored by Bishnu Nayak, FixStream Inc. CTO

In September 2016, I met and presented FixStream’s Algorithmic IT Operations (AIOps) platform to Nutanix’s Alliance and Alliance Engineering team. They noticed very quickly that FixStream addresses an important need for their customers – delivering an operational analytics platform that ensures service assurance of critical business workloads deployed on the Nutanix Enterprise Cloud infrastructure.

Built on cutting-edge Big Data technologies, FixStream’s correlation, analytics and visualization platform provides application-centric visibility in a hybrid IT environment by correlating across end-user transactions, applications and infrastructure layers. Its platform adds tremendous value in customers’ heterogeneous environment where Nutanix Enterprise Cloud is deployed, along with other multi-vendor infrastructure technologies.

We decided to work with Nutanix as our first HCI partner for several reasons – first we were impressed with their architectural approach to a hyperconverged solution; built ground up while keeping ecosystem innovation in mind. Second Nutanix presence in IT is very complementary to FixStream’s strategy in that both Nutanix and FixStream’s solutions enable customer to accelerate migration to a hybrid cloud environment.

Last (but not least) we have always been intrigued by the partner-centricity embedded in the company culture and vision. it’s pervasive in how Nutanix onboards, validates and market with its industry peers and partners.

Per Gartner, algorithmic IT operations platforms enable I&O leaders to meet the proactive, personal and dynamic demands of digital business by transforming the very nature of IT operations work via unprecedented, automated insight

“AIOps platforms utilize Big Data, modern machine learning, and other advanced analytics technologies to directly and indirectly enhance IT operations (monitoring, automation, and service desk) functions with proactive, personal, and dynamic insight.” (Gartner – “Innovation Insight for Algorithmic IT Operations Platforms”– Colin Fletcher, Refreshed: 26 April 2017 | Published: 24 March 2016)

Soon after our initial meeting, we entered into an official partnership agreement with Nutanix and with their support, developed a solution that was released in FixStream 6.0. The Nutanix alliance team has been extremely collaborative working with our R&D and engineering teams, ensuring the successful delivery of the desired capabilities.

FixStream delivers much-needed visibility and analytics for enterprises to successfully deploy critical business applications into Nutanix-powered (AHV or VMware hypervisor) environments and troubleshoot, plan and maintain going forward.

The FixStream platform:

  • Auto-discovers compute, storage, network entities from the Nutanix Enterprise Cloud environment by using Nutanix REST APIs and other FixStream native data collection techniques
  • Provides topology analytics across the entire customer network and visually represents how Nutanix Enterprise Cloud appliances connect with the end to end enterprise network, including virtual and physical networks, links, and interfaces
  • Auto-discovers application services such as app servers, databases, and web servers, running on Nutanix managed VMs, network flows, and available paths in/out into other entities in the network
  • Auto-discovers application dependencies and delivers application maps by connecting the underlying physical, virtual and logical entities in network, compute and storage with dynamic computation of network and storage path for application flows. It’s like Google Maps for critical enterprise business applications
  • Collects performance metrics, alerts and faults from all entities in the map and algorithmically correlate the events using time series analytics to identify patterns and anomalies. It then provides proactive and predictive remedial actions. For example: a business application’s performance slows down every Monday at peak hour. At the same time, the VM where the application service is running runs at high CPU, and the pattern repeats every week. FixStream identifies the trend and provides a remedial recommendation to proactively provision more CPU to the VM
  • Stores collected data contextually in a linearly scalable backend search database, allowing users to automate IT compliance and reporting functions

FixStream for the Nutanix Enterprise Cloud environment enables significant ROI for enterprises customers. According to the Digital Enterprise Journal (DEJ – 2017), 80% of companies surveyed experienced an average MTTR of 4.2 hours. The cost of a minute of service outage was $72,000 if revenue was lost. FixStream can reduce MTTR to minutes, therefore allowing companies to potentially save millions of dollars.

As technology transforms, it requires millions of data points to be manually correlated for daily operational activities such as planning, migration, workload placement, troubleshooting, and change management. This makes the activities extremely expensive, risky for business, and prone to errors.

FixStream automates these tasks by correlating these data points across transactions, applications and infrastructure.

The FixStream solution for the Nutanix environment delivers the following key business values:

  • Accelerates migration to Nutanix – With the end-to-end visualization, correlation and dependency mapping capabilities of FixStream, enterprises can now migrate from older infrastructure technologies to Nutanix HCI technology significantly reducing unforeseen business risks. The FixStream topology map, application dependency map, and data explorer capabilities provide the analytics required to plan and execute the transformation activities while lowering cost via FTE reduction and assuring performance of business services.
  • Automates troubleshooting of business outages and reduces MTTR from hours to minutes – FixStream’s application map and time series event correlation pinpoints the exact root cause of the problem in the Nutanix Enterprise Cloud infrastructure, as well as connectivity to larger corporate network connected to Nutanix real-time. This reduces the MTTR from hours to minutes. The FixStream platform proactively identifies performance bottlenecks and notifies the operations team to take corrective actions.
  • Resource Optimization and Application Workload Management – Through its performance Heatmap, FixStream provides analytics to operations team to realign resource allocation for optimization and cost reduction. Additionally, the Heatmap provides insights on available resources for new workload placement based on specific requirements for memory, disk and CPU. Automates IT Compliance and Reporting – Cross-domain, cross-vendor data across network, storage, compute and application are discovered by FixStream and stored in the backend ElasticSearch database. This allows allows users to query data for hierarchical output as needed for compliance analysis and reporting. The automation of this quarterly IT compliance activity significantly reduces costs for enterprises.

We are very excited to jointly launch this solution in the market and help Nutanix customers realize the benefits of FixStream’s innovative AIOps platform capabilities. For more details on this solution, please visit our website.

Disclaimer: The views expressed in this blog are those of the author and not those of Nutanix, Inc. or any of its other employees or affiliates. This blog may contain links to external websites that are not part of Nutanix does not control these sites and disclaims all responsibility for the content or accuracy of any external site. Our decision to link to an external site should not be considered an endorsement of any content on such site.

© 2017 Nutanix, Inc. All rights reserved. Nutanix is a trademark of Nutanix, Inc., registered in the United States and other countries. All other brand names mentioned herein are for identification purposes only and may be the trademarks of their respective holder(s).

Open Data Ingestion and API Driven Architecture by FixStream

Open Data Ingestion and API Driven Architecture by FixStream

By Bishnu Nayak

In my last blog, I discussed the need for auto-discovery in hybrid IT environments and the business values that enables for enterprises. In this blog, I will focus on an open API data ingestion architecture strategy that further leverages the power of auto-discovery and correlation to unify the heterogeneous IT operations landscape.

Enterprise IT environments are becoming increasing complex with hybrid IT deployment models with virtualization, cloud, containers, and software defined-architectures. New technology adoption introduces newer management and operations tools. The number of tools used to manage the hybrid environment is increasing across various operations domains such as infrastructure monitoring, ticketing, orchestration, automation, application management, security, etc.

In fact, more than 35 percent of IT professionals surveyed said there are too many tools and dashboards. They say this disjointed situation makes them slower to respond to critical issues and identify sources of trouble.

Nemertes Research’s John Burke says the more toolsets, the tougher it is to use them effectively.

That said, the fact is many of these tools are important to IT. And they’re not going away any time soon, at least not all of them.

So what’s the solution? The solution is to bring these tools together to a unified operations view via open API data ingestion and a single-pane-of-glass approach.

That’s exactly what FixStream has done with its Artificial Intelligence for IT Operations, or AIOps, platform.


FixStream AIOps platform is built on an open API data ingestion architecture. The open API data ingestion layer is built on a robust set of APIs used for communication with different domain specific tools. APIs are at the heart of the open API data ingestion architecture, which allows for data collection from the following categories of tools in a standard way:

  • Application Performance Management (APM) solutions like AppDynamics and New Relic
  • IT Operations Management (ITOM) software like Nagios and SolarWinds
  • IT Service Management (ITSM) systems like BMC Remedy and ServiceNow
  • Security Information and Event Management (SIEM) offers from Splunk and others

The ability to do open API data ingestion from an array of data sources is critical. It allows for an accurate, real-time view of all the moving parts in hybrid IT environments. That includes all applications, all business transactions, and all infrastructure.

 FixStream has enabled this via the introduction of connectors and Southbound APIs. We have put together some prepackaged connectors for the most popular tools in use today. Additionally, our APIs enable FixStream’s industry colleagues and customers to build their own connectors.

 As you know, APIs are bridges that connect software together. Forrester Research has called the API the poster child of digital transformation. That’s because APIs enable different systems to work together for more efficient operations and better outcomes.

For our customers, FixStream’s APIs and connectors add up to ease of use, elimination of siloes, great efficiency, and more intelligence. They enable users to get more value out of AIOps and from their existing tools too. The value of disparate, domain-specific data that exist in domain-centric tools is significantly enriched when ingested into the FixStream AIOps platform, powered by its multi-domain, multi-vendor and multi-layer correlation.

Getting an end-to-end view of your applications and network resources requires a lot of things to come together. And our APIs – and related connectors – help make that happen, and build value in the process.

Our open API approach for ecosystem enablement is in many ways similar to the disruption that happened in the smartphone mobile app ecosystem.  Smartphones deliver powerful open platforms such as iOS or Android for developers all over the world. That enables those developers to deliver powerful applications that run on the smartphone O/S.

FixStream’s open API data ingestion is pivotal to build a community and ecosystem for enablement of data ingestion from various sources. That enables our partners, customers, third-party vendors and developers to build connectors to ingest data to FixStream to leverage the power of FixStream AIOps.

I am very excited about the opportunities and benefits it will enable for our partners and customers to drive business value.

How Best to Collect Unstructured Data from Hybrid IT

How Best to Collect Unstructured Data from Hybrid IT

Auto-Discovery Provides a Simple, Scalable, Automated Approach

By Bishnu Nayak

We live in a world that is massively distributed, disparate and diverse.

There are a lot of different people speaking various languages, and different cultures. If you can interact with these individuals in their language, you can learn a lot. And the broader knowledge and insights can significantly benefit people as well as businesses across the world.

Enterprise networks are worlds of their own. And in some ways, they mirror this larger disparate world.

Enterprise hybrid IT data centers contain lots of entities across network, compute, and storage supplied by different technology vendors. The vendors that supply these technologies have their own cultures, their own syntax and languages on how they manage and interact with other entities in the IT environment.

But if you can interact with these distributed entities in a normalized way and understand how they relate to one another, you can derive a deeper understanding into the end-to-end environment. That understanding helps enterprises manage their IT environments optimally and profitably.

My point is that we live in a diverse world. And when we collect information about different entities across the world from different sources, we gain greater understanding. That can help improve human lives. I mention this because FixStream AIOps platform helps business to improve their IT operations by understanding their application and IT infrastructure resources.

FixStream AIOps technology can

  • collect data from disparate IT entities and siloed systems
  • correlate and analyze the flood of data from different sources
  • and present that information in a way that makes it quick and easy for businesses to understand and gain value from it.

Sameer Padhye has blogged about the data correlation and visualization aspects of FixStream’s AIOps solution. (I should note here that AIOps stands for Artificial Intelligence platform for IT Operations.)

But  before data is correlated and visualized, it needs to be collected from millions of disparate datasources. And FixStream uses its smart auto-discovery solution for optimal data collection.

So, I’m going hit the rewind button with this blog and address the first step in the process.

Auto-discovery in this context describes the process of automatically fetching lots of data from many disparate sources. FixStream can do that because it knows how to communicate with the various infrastructure and application entities. Data is collected by FixStream data collectors from all kinds of entities –switches, routers, load balancers, firewalls, servers, storage devices, hypervisors, VMs, application entities – both physical, virtual as well as logical.

FixStream is vendor agnostic. So, it doesn’t matter if the entity comes from Cisco, HP, IBM, Nutanix, Juniper, VMware, or some other supplier.

FixStream knows how to normalize and make sense of the massive amount of data it collects using a semantic model. And it can do that regardless of the physical location of those entities across hybrid enterprise network.

That’s really useful, especially considering that complex IT environments typically lack a real-time inventory of assets. FixStream addresses that gap. Our hybrid cloud discovery capability provides a reliable and up-to-date inventory of enterprise compute, network, storage, and application environments.

Here’s how it works. Data collectors scan IP addresses in network subnets or user input boundaries to learn about infrastructure. FixStream can then identify the make and model of an and uses the vendor-specific command library to learn how it’s configured, and other topology-related data such as dynamic table, interfaces, MAC addresses, routing information, and VLANs, etc.

That allows FixStream to provide a topology map that illustrates the relationships among all IT entities. FixStream also does automatic and dynamic discovery and mapping of applications and their infrastructure dependencies.

The contextual maps are then correlated with alerts, faults, log events, and tickets ingested from different sources via the FixStream Open API ingestion layer.

This discovery, correlation and mapping delivers tremendous operational and business value to our customers. For example, if you’re doing maintenance on a device, you can see what else is connected to it. If you are doing a migration, you can easily understand the dependent systems that can be impacted. This end- to-end correlated view allows enterprises to do faster root cause analysis, lowers business risk, and eases migration challenges.

Many of our competitors lack such capabilities. Some have them, but their approaches to auto-discovery are less than optimal. To be frank, they tend to be quite basic.

By comparison, FixStream auto-discovery allows for a very rich data experience. Our approach is extremely granular with derivation and analysis of relationship data such as hierarchies, links, and relationships across all the IT entities.

For example, the platform derives the parent-child relationships between hypervisors and all VMs hosted in it. That shows the links between the VM to hypervisor to the TOR switch they are connected with. And the FixStream AIOps solution derives all available network paths between all compute, storage, and network entities and correlates them to application flows (Flow2Path analytics). That helps IT teams with troubleshooting, maintenance, and to make more effective use of their resources.

You don’t know what you don’t know. So FixStream auto-discovery solution helps you to know the unknowns.

Some FixStream customers have uncovered assets they didn’t even realize they had. It was a nice surprise. But the fact that some businesses lose track of their IT assets isn’t particularly surprising. It’s easy to do.

FixStream discovery process enriches the value of ITSM systems by feeding the discovered data to CMDBs of ITSM systems. Traditional CMDB discovery and update process lacks the knowledge of relationships between compute, storage, network and application layer and FixStream provides rich relationship data across all layers to CMDB, further enhancing the ITSM processes such as incident, change, config and asset management.

FixStream cloud discovery makes it just as easy to identify the underutilized resources and repurposes them for business services that need additional capacity. As a result, businesses don’t have infrastructure assets sitting idle. And they can use their limited budgets on things they really need.

Knowing what’s in your network is also important from a compliance and security standpoint. If you don’t know what’s in your network, you can’t keep tabs on what’s happening with it.

It’s also important to note that FixStream’s auto-discovery approach is agentless, so it’s not intrusive.

With agentless auto-discovery there’s no need to deploy agents on the devices from which data is collected. That means businesses don’t have to install agents on thousands or tens of thousands of devices. And they don’t have to worry about implementing different agent versions to address various vendor solutions.

All of the above illustrates how FixStream makes data collection simple and scalable. Even in hybrid cloud, multi-cloud, multi-domain, and multi-vendor environments.

Not only that, but we make sure you’re collecting the right kind of data. Once we do that, FixStream uses AIOps to aggregate, correlate, analyze, and visualize your data. All that adds up to new value for your business.

FixStream continues to add value to AIOps ecosystem through its data collector and open API architecture. Our open architecture means new collectors can be built in a one to three months release cycle. And third parties can now build collectors based on the FixStream open architecture too.

(In my next blog, I’ll talk more about the importance of APIs and open data ingestion.)

But for now, let me leave you with this quote from author and speaker Deepak Chopra.

“Success comes when people act together. Failure tends to happen alone.”

The same could be said of data in the IT operations management, planning, and troubleshooting realm.

IT environments today are made up of many different and disparate entities. The cloud and virtualized technologies like containers, microservices, virtual machines, and network functions have added to the chaos.

Businesses, which are increasingly reliant on connected applications, need to get a handle on all this. They can do that by correlating, analyzing, visualizing, and acting on an array of data.

That’s the only way organizations can avoid failure, optimize networks, and ensure application – and business – success. And auto-discovery is the first step in that process.

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