Gregg Ostrowski

Executive CTO | Cisco AppDynamics

"As application landscape is becoming increasingly complex with multi-cloud and hybrid environments, the enterprise maturity model needs to evolve from chaotic to reactive to proactive and predictive levels. Data is at the heart of this transition and Robotic Data Automation Fabric is paramount for this transformation. Cisco along with its portfolio of Appdynamics, Thousand Eyes and Intersight and along with top tier partners like CloudFabrix is well positioned to meet these challenges."

Meenakshi Srinivasan

Partner, Global DevSecOps Practice at IBM Consulting

"IT is no longer a supporting function; IT is at the center stage of the digital ecosystem and is increasingly driving business value. The client's solution stack is becoming extremely complex with On-Prem, Hybrid, IaaS, PaaS and SaaS deployment models and AIOps is paramount to move to an Intelligent AI driven IT solution. IBM has comprehensive AIOps framework to discover, observe, analyze, prescribe using an open technology stack. We boast a wide ecosystem of partner solution including CloudFabrix and remediation and visualization solutions using IBM Technology such as Instana and Turbonomics."

Jonathan Symonds

Chief Marketing Officer, MinIO

"Log Intelligence is considered the 'Digital Heartbeat' of an Autonomous Enterprise. However, with the modern, distributed edge to core web applications, the amount of data generated quickly becomes overwhelming for enterprises. This in turn impacts the actionability of that data. Together, MinIO and CloudFabrix's Log Intelligence solution offers a SEIM preprocessor which can enrich and correlate logs, significantly reducing TCO, improving MTTR and providing compliance with PII masking - all while keeping timestamped full fidelity copies which can be replayed on-demand."

Marco Spoel

Executive, T-Systems

"Building trust with AIOps starts with the data. We are a service delivery partner for regulated industries like Healthcare, Manufacturing and Automotive and public sector and the trust with predictions is absolutely paramount. Robotic Data Automation Fabric delivers the data supply chain to build this trust. Also, with AIOps becoming a buzzword for several vendors, it is important to measure KPI's for noise reduction, proactive automation for 'ZeroOps' and reducing TCO. It is equally important to work with existing ITSM tools and enable 'BYOL - Bring your own log tool,' when dealing with distributed environments at scale. CloudFabrix can move your company forward toward ZeroOps, giving you a foundation for hyper-automated Incident avoidance that works in the existing tools environment, providing unique value to Business and IT."

Girish Chandangoudar

Vice President, Happiest Minds

"As our customers are going through their Digital transformation, several of the applications are getting redesigned to be cloud native with IaaS, PaaS, serverless as their hybrid underlying platforms. They are thus losing the visibility they had with their monolithic static application stacks, not to mention the deluge of data. This is driving the need for AIOps and why we have decided to partner with CloudFabrix. Additionally building trust in AIOps boils down to defining KPI's and measuring the success against these KPI's whether they be improving MTTR (Mean Time to Resolution), Incident management, improving productivity and this begins with the data quality at hand."

Shailesh Manjrekar

Vice President AI and SaaS Marketing, CloudFabrix

AI has certainly become the hallmark of Digital Transformation, however there are 3 inhibitors – Data quality, operationalizing the pipelines for quick experimentation and consolidating data silos. CloudFabrix's RDAF platform is architected to mitigate these challenges with Data automation, No code/low code Databots and Data Fabric and applying them to Observability, AIOps and Automation. It was very encouraging to see our partners and customers agree on the inhibitors, the RDAF vision and execution with cfxCloud. We strongly believe RDAF as market category enables enterprises to embark on their Autonomous Enterprise journey, unifying Observability, AIOps and Automation.

Sean McDermott

CEO, Windward Consulting group

Starting to look at your data pipeline right and how do you automate your pipeline and this is kind of where CloudFabrix is really strong in identifying data pipelines and being able to integrate with those data, transform the data through automated means.So, one of the things that we talk a lot about to our customers about is really helping , really looking at technology to accelerate the third party vendors to accelerate the automation of accessing that data in transforming the data, making that data useful so that when you actually do start looking at modelling and things like that you're using good data.

Tejo Prayaga

Senior Director, Product management and Marketing- CloudFarix

We are allowing our customers to deploy AIOps data plane anywhere they want, whether it is in edge, or data center or in the cloud and separate the control traffic from the data traffic so that customer gets cloud management benefits, while still keeping the data transfers within the enterprise

Kris Inapurapu

Chief Business Offficer-MINIO

Log Reduction Routing and Replay is a core component of the CloudFabrix solution but here performance is a big driver so your ability to do analysis on these logs is a function of the fact that your ability to read and write at speed and we just talked about being able to deliver against that mandate that the Cloudfabrix requires in their solution.

Joe McKendrick

Principal, The Field CTO

AIOps is not a methodology intended to improve the delivery of AI to the business but it's about using AI to improve IT operations themselves.So, AIOps helps automate many of the tasks that define today's IT operations.

Jen Stirrup

CEO and Founder- Data Relish

When we are specifically looking at RDA we are trying to solve few problems here so that we can generate value from the data, now the dataops problem is really that there's no single tool for collecting all disparate data together all in one place and thats from storage to processing analysis, building insights based on AI, so solution is to think about AIOps and what it does by combining ML and AI algorithms in a very intlligent way that you can use it for your log data.

John Sipple

Google Cloud

The basic high level solution is that we break things down into cohort groups, when you have a fleet of devices,the one of the first things you want to do is to subdivide the device into peer groups or cohorts and then and from there you can build a model around that cohort and then use the anomaly detection just simply on that cohort, it does two things for you, number one it provides you with a way of scaling out so you split the entire fleet into cohorts that allows you to instantiate an anomaly objection process on each cohort individually gives you a natural scaling process but while you have a cohort you have many examples of this type of device that builds a richer statistical baseline which which gives you an understanding of what normal looks like then we can use an unsupervised anomaly objection algorithm which we have developed here at Google and we use the model explainability and then we wrap all that in in an MLOps pipeline on within the google cloud platform.

Manjeet Singh

Director of Products-ServiceNow

My take on autonomous enterprise is as you go into this journey, continue to do it more proactively slowly, you will reach a point where the scale will shift slowly from percentage of manual work to automation work if you are doing it right in your organization.

Shardul Vikram

SVP and Head of Intelligence and Incubation-SAP

Data Fabric is something that any enterprise which wants to do serious AI requires massive big data capabilities, more and more AI needs to work in real-time, which means that data needs to flow seamlessly, and nowadays, as most things are being deployed as microservices so the data discovery part should exist as well. Then there have to be layers of authorization that need to be built, like which service needs to access data. In the past, all of these protocols and requirements were codified within respective microservices. Still, with the advent of data fabric, you will see more and more being handled at a common framework at a central level than by each respective service, and that will simplify the way we develop and deploy.

Anand Oka

Data Scientist-Truveta

Each business has to become a distributed self-governing utility, and that's where the future is for that automation based on data and processes is obviously critical, so I see it like data automation at the bottom, there is process automation, and then you have digital transformation going from the front end to the back end, but it has to be two ways, theme one in the loop and then theme two which is like a full-scale autonomous control of the enterprise.

Aleksandar Lazarevic

VP of Advanced Analytics & Data Engineering-Black&Decker

When we talk about envelopes or AI ops data when we try to deploy certain machine learning algorithms that we are building on site and then try to deploy this on on the business side trying to actually track the value, trying to quantify the value over time and to see how these algorithms are creating the value for our organisation.

Jayanth Kolla

Health Care - Technocrat; Consultant; Entrepreneur; Board Member; Global Speaker- Daytoday

When we are talking about data at such a large scale, and when we are talking about government agencies being the custodians, we are talking about sort of a mismatch in terms of existing systems and their ability to handle right, and that’s where I see some of these technologies such as RDAF come into play wherein they enable these agencies to handle these large sets of data in a much more structured manner and make them available for innovation especially Cloudfabrix has a couple of these solutions that we have evaluated wherein the pipeline itself the data can be vectorized, structured and also even be AI-enabled.

Shahidul Mannan

Head of Data Engineering and Innovation, DAO-Mass General Brigham

When you mentioned robotic automation on data, that’s one of my vision and goal as well, which is how can we enable system-wide, more flexible access to data for innovation, and that’s why we are driving what we call self-service analytics where we want people to have the flexibility without coming to the technology teams and how they can be set enabled to access data and then to run and build their experiments around data science, and that involves this robotic automation which w involves integration of DevOps, AIOps and MLOps automation .

Shailesh Basani

Director-Adobe

We started to invest in AI in some parts of the organization.RPA is one area we are using significantly for the backend IT process. We have been using a lot of chatbots and things like that in some areas, but for the cloud services journey, I would say we are in the early stages of AIOps because there’s so much in this space, and one of the major things is that we have a very solid data strategy to make sure we can build on top of that and what we are looking for at this point of time when we look at AIOps is how we can make sure whatever we are doing can we do a performance baselining well and then from there can we go towards anomaly detection and then can the AIOps help us with automated root cause analysis and eventually go to predictive insights.

Ramesh Dhanapal

Data Acquisition and Governance-GILEAD

Robotic Data Optimization is going to be really helpful if we can automate through this process for a couple of reasons like atleast, we are trying to build a data marketplace where people can come and discover the data assets that we have across the enterprise whether is US or XUS sheet, we want to make it more global data assets but it can be only useful if we can give access to the data and build valuable insights out of it.

Piyush Gupta

Chief Technology Advisor, Senior Technology Leader-World Wide Technology,Inc

From a CFX standpoint, what I see the role is build versus buy. If you are going to building something, CFX can help accelerate that journey and at the same point helping in terms of ROI as well, many times there are existing investments, licenses, and tools where i have seen CFX can help with like cutting down some of those existing expenses and then using that to fund the new investments into the tool.

Sachin Anikar

Director Consulting at LTI - Larsen & Toubro Infotech

AIOps pushed the boundaries of enterprise, the inflection or having to go with the kind of boundaries of the enterprise were very well defined from infrastructure perspective it was data center and office space very kind of accessing, now its gone beyond and the experience of the end user comes into play much more than what it was two three year back, in addition to that it has accelerated the transformation towards digital part of the enterprise and by doing so it has got into distributed, cloud infrastructure and distributed enterprise framework.

Priya Kanduri

CTO - Vice President, Cyber Security services, Diversity & Inclusion Champion-Happiest Minds

CFX definitely helps us in the incident identification itself. Part two is where it’s identified, and we want to analyze and categorize it as a false positive or false negative. The third area is where we are trying to take the right action and go look into the past history, do we want to quarantine, do we stop a process, do you want to isolate a device, or block a URL.So there’s a lot of data gathering goes into that activity as well, and that’s another use case where CFX equivalent tools and technologies that we employ come in very handy.

Avijit Sen

Associate Partner - Global Go To Market Leader - DevSecOps and AIOps-IBM

Many companies are doing data ops, but CloudFabrix has an advantage because the basic foundation of AI is ultimately data, so if you look at how to reduce the noise and the differentiation in terms of how you can bring out the good data from a plethora of data, that’s where I think CloudFabrix has the biggest edge.

Satya Bajpai

Managing Director, Tech M&A-JMP

Two areas are extremely rich from my perspective for data and AIOps, which is effective automation that is sort of back-office in many ways or even the front office. One is information security having cyber security, which has always been a top priority, and it doesn't matter what use case you are solving, security has to be planned as early as possible in the data framework. Second is retail front-end applications because the amount of e-commerce happening has increased the importance of how you can take the data and deliver more insights which can result in more revenue and customer satisfaction.

Ashish Bansal

Director, Recommendations Systems-Twitch

The biggest challenge for us is making sure that we keep privacy in perfect view and privacy is very important, especially in a b2c operation that we are operating. The customers must trust that their data is being taken care of properly. Beyond that, I think different industries have different values from the amount of data they can go, and by the amount, I’m referring to the time scale. So, for some industries, the time scale is less important, but for the actions happening within the shorter times, the granularity of interactions is much more important. There the challenge is that the signal-to-noise ratio is not that great. Among the number of clicks and things, they did on the platform, identifying that important insight and making sure that you can build more resilient models and are resistant to these noisy factors and noisy interactions are highly important.

Venu Kalluri

VP Engineering-Dolby

When we are training AI algorithms, getting the data to train on is a challenge, and we have a lot of internal and external data. We look at our data sets for training. Then we look at what happens in the field and where we can get customer permission, so if some things go wrong, we can get customer permission to get those data to add it to the data sets that we have and retrain algorithms and come up with improvements and enhancements.

Satyam Priyadarshy

Managing Director - India center, Halliburton

There are a lot of challenges in terms of governance. I am actually being able to create value from the data because you cannot connect various things, but at the same time, what's progressing in the energy sector is people have realized that we need to democratize the data within the right governance framework and as a result learned over the last couple of years how to create data pipelines within the right governance framework so that you don't move the data but only look at the data that really needs to be connected to solve a particular problem operational challenge and actually generate value.

Nikhil Aggarwal

Managing Director at IBM-Promontory | IBM Industry Academy Member

At IBM, we have been thinking a lot about federated machine learning-How do we take data sets from different clients with their permission, and how do we synthesize this together and try to build out a bit of a more comprehensive machine learning model. The direction that we are getting from US regulators has been a spirit of innovation, and the idea here is through AIOps, you are able to generate augmented insights that will benefit all the participants of the consortium.

Sreekar Bhaviripudi

Managing Director, Morgan Stanley

Explainable AI is still in its green field yet because I think its coming more and more to light I think explainable AI is in particular more critical if you are using one of the representational machine learning techniques like deep neural networks where its more like a black box approach and in a regulatory environment whether it be financial services and decisions are being made using any of these models.