The Selector Newsroom

November 21 2022- Intellyx, an industry analysis and advisory firm focused on digital transformation recently announced their fall 2022 Digital Innovator Award winners. Among the winners, Selector AI was chosen as the 2022 Digital Innovator Award. Read the full news announcement here.

https://www.forbes.com/sites/forbestechcouncil/2022/05/31/the-evolution-and-expansion-of-aiops-in-network-management/ Nitin Kumar Forbes Councils MemberForbes Technology Council Expected to be worth more than $40 billion by 2026, AIOps solutions are paving the way for improved IT operations in digitally-driven organizations. A major factor driving this is that traditional operations management models are no longer good enough to deal with a world where distributed work has been normalized. Instead, by leveraging big data, machine learning, artificial intelligence (AI) and other advanced technologies, companies can bring greater automation to enterprise IT infrastructure and service management as well as gain complete visibility of the network infrastructure challenges they face. Operations management used to deal with various IT tasks at a departmental level in the past. In fact, many solutions today still follow this nitty-gritty approach and perform system analysis in siloes. In doing so, however, business and technology leaders do not have the rich context needed to connect the dots, troubleshoot effectively and quickly empower operations teams with the insights required to focus their efforts in the most impactful areas. Outside of alarms and notifications, this approach means that very little advanced thinking and analysis is happening in traditional monitoring systems. If anything, these operations teams are overwhelmed by the velocity, variety and volume of data being generated. They cannot observe all metrics, anomalies, events and alerts using a legacy way of thinking, resulting in rapidly cascading challenges that bottleneck the infrastructure environment. A Sherlock For Network Challenges Of course, AIOps is not a new concept. First coined by Gartner, Inc. in 2017, AIOps platforms can “enable the concurrent use of multiple data sources, data collection methods, analytical (real-time and deep) technologies, and presentation technologies. Think of these as a collection of managed services that combine network analytics with machine learning and AI to provide IT teams with instant, actionable insights. The data explosion has made it impossible to program thousands of rules on a system to examine logs, set thresholds, warn of any issues and compare events. Let’s consider that the developments of the past two years have seen customers embrace digital services at a scale previously unimaginable, from online shopping and banking to video streaming, videoconferencing, work collaboration, networks and IT infrastructure. We also need to recognize how they have become more complex. All of this means that it is simply no longer humanly possible to set up, manage and monitor how the operations management environment deals with rules. The power of AIOps lies in collecting and analyzing the data generated by a growing ecosystem of IT devices. Whether this comes from edge computing and Internet of Things devices or smartphones and laptops used by remote workers, the secret sauce driving AIOps is normalizing the generated data to provide a uniform picture of the entire system. In turn, machine learning analyzes it and provides IT teams with the insights required to ensure the network infrastructure real estate runs smoothly. This is where the benefit of AIOps reveals itself, as it reinvents this arduous process and better equips organizations to deal with injecting automation into the optimization of their mission-critical network, communications and infrastructure processes. This ability to unify data from across the entire IT infrastructure of the organization becomes the critical step toward a broad-scale implementation of AI at the enterprise level. It is fueling enhanced productivity and, more importantly, manageability. Starting The Journey This looks good on paper, but how can it be implemented in a business? It all starts with data. AIOps cannot exist without data, and data without metadata is meaningless. Whether in the form of key-value pair labels or as tags, metadata is the context that data requires and is one of the most common pitfalls we see in AIOps initiatives. Metadata contextualizes network telemetry, logs, events, flows, routes, alerts, configuration changes, etc. It’s as important as the data itself, and without proper, holistic management of metadata, network and IT telemetry are just raw disconnected points. Once meta tags and metrics are correctly in place, the next step is collection. Organizations should focus on analysis that leverages metrics from the widest variety of available sources for a clear view of operations. From there, correlating data points from multiple sources can provide the critical operational insights needed to keep systems running. This is where AI and machine learning play a critical role in connecting the dots and should be a starting place for any AIOps initiative. Finally, organizations need to ensure that the resulting analytics and insights can be shared quickly. Operational insights are only valuable if they reach the teams that need them. Forging Ahead We can all argue that based on the efficiency it yields, AIOps is the future of IT operations. It’s the catalyst that enables enterprises to speed up time-to-resolution for incidents and predict issues before they happen. This predictability and insight are essential for modern business, and they can come from the data that the business is already producing. This next generation of monitoring delivers a paradigm shift in analytics. Even though the core modeling of data remains consistent, it comes down to embracing a more agile way of capturing those insights to improve operations management. That is what AIOps enables.

https://www.fastcompany.com/90740122/when-they-doubt-your-tech-can-deliver-lean-on-these-three-pillars-of-credibility BY KANNAN KOTHANDARAMAN 3 MINUTE READ Every company that sells technology promises magic. From the early days of email to the countless platforms and programs available today, technology has always promised something faster, easier, automated, or intelligent. Through decades of innovation and transformation, countless products have inevitably fallen short of their promise. Today, the market is crowded and prospects are skeptical when considering new solutions. As founder of an AIOps startup, I’m quite familiar with the promises AI and machine learning have made in the past. In business and pop culture, the magic of AI has promised to drive our cars, be our assistants, make medical diagnoses, and more. In reality, we’ve made huge advancements in the applications of AI and machine learning, but we’re a long way from HAL and the Terminator. Regardless of the technology you’re introducing, convincing customers your solution will meet their needs comes down to credibility. Those that get it right have three things in common. TEAM IS THE FOUNDATION The most essential element for success is the team. Before you can attempt to solve a problem, the team needs firsthand experience with it. Why does it matter to your customers? What’s been promised? What’s failed? What’s needed? In the beginning stages, your team’s experience is a foundation of credibility for prospective customers. To that end, it’s crucial to build a team that has both technical expertise in data science and engineering, and domain expertise in the product category. They don’t always come from the same talent pools. Top technical talent is in high demand and spread across a variety of industries. Don’t limit your search. Domain expertise, on the other hand, requires extensive experience in your vertical. Look for candidates with experience working on and managing teams that have been in the trenches of the problem you want to solve. TECH FROM THE GOUND UP Once you’ve identified the customer’s problem, find the white space. What are the gaps in the current solutions? Start small with the specific issues you address and focus on the architectural underpinning of the platform first. Broad applicability is crucial in technology, but it’s easy to overextend by trying to satisfy too many use cases. That’s not to say scalability is unimportant. It’s crucial. Your solution should accommodate a variety of implementation scenarios and allow customers to add on as needed, but that’s only possible with the groundwork in place. Resist the urge to rush to production. A strong technical foundation from the start will pay dividends long into the future. CREDIBILITY ISN’T BUILT IN A DAY Regardless of how amazing and groundbreaking your technology is, if implementation doesn’t go well, you’re dead in the water. How do you ensure a successful implementation? Think “land and expand.” Leverage the credibility you’ve established with your team and technology to land a proof of concept or small engagement. Once you’re in the door, remember your customer has tried to solve this problem before. Set realistic expectations of what your platform can deliver then aim to exceed them. It’s easy to over-promise in the initial conversations. Focus on demonstrating results with one or two outstanding implementations and the solution will upsell itself. Don’t be afraid of taking a co-creation approach with key customers either. In the early stages, it’s essential to gather and quickly take action on customer feedback. With the right platform architecture from the start, expanding the product in collaboration with the customer is an ideal way to build credibility and expand. No matter what you’re introducing, be aware that it’s been promised in the past. Hype cycles are exciting when they’re in your favor, but real customer credibility doesn’t come in waves. In the end, the right team, technology, and implementation is the only formula to prove the value of your product where others have fallen short. Kannan Kothandaraman is CEO and co-founder of Selector, the AIOps platform for operational intelligence.

Kannan Kothandaraman, Nitin Kumar and the Selector Team Three years ago, we set out to develop an operational analytics platform that would eliminate downtime and empower operations teams with tools to be more efficient and productive in their jobs. We wanted to make their lives easier, taking the complexity out of application and networking issues by providing a solution that combines intelligence and analytics but most importantly gives them actionable outcomes. We like to think about it in terms of all the iterations of search engines before Google came on the scene. The early “search” engines were very literal and required the user to wade through links and know exactly what they were looking for, whereas Google’s superpower is its ability to predict what you’re searching for and deliver more precise results, even leading you to the answers to questions you didn’t know to ask. This is the approach that our platform, Selector Analytics, takes to the AIOps space.  We came to this place having seen first-hand the shortcomings of monitoring solutions during our years working with some of the largest Service Providers, financial institutions, cloud providers, retailers and all manner of enterprises all over the world. It was through the lens of that extensive experience and the lessons learned from it, that we saw a huge opportunity to deliver the best AIOps platform on the market. A platform that other vendors in the space haven’t come close to matching. Like Google did for search, we take a sophisticated, complex environment and deliver simplicity.  Today, we are announcing our $28M Series-A led by Two Bear Capital, SineWave Ventures, and Atlantic Bridge. Comcast Ventures and Azure Capital Partners are also investors in the company. It brings the total funding raised by the company to $33M.  Even as we look to the future with great excitement, today is also a day to reflect on how far we’ve come and how we got here, a process that inevitably leads me to the three pillars of credibility that Nitin and I built Selector on, and what we’ve been working so hard at for the past three years as a team. We always come back to these pillars to validate our market proposition and technology offering, and enable us to hold true to our promises. The Team The Technology The Implementation The Team We have assembled a world-class team, from companies such as Juniper, VMWare, Cisco, Nutanix, AWS, and focused it on transforming the AIOps space, and that’s one of the main reasons Two Bear Capital, SineWave Ventures, Atlantic Bridge invested in us. Collectively, we have decades of experience and deep domain expertise in networking, applications, data science, AI and machine learning. Our leadership team has first-hand knowledge about the problems our customers are facing and the shortcomings and failures of the platforms and systems they’ve relied upon before. This has enabled us to build a solution that meets their needs in ways that no other vendor has been able to.  With this Series A funding, we’ll continue to build out our hand-picked team with individuals who bring different but complementary skills and experiences to the table. Together, we’ll push ourselves to innovate, encouraging each other to greater heights, while creating a supportive and collaborative culture.  The Technology Everybody promises magic with their AI/ML-powered solutions. Claiming to provide a “single pane of glass” that collates information across disparate sources into a single view is a common claim, and yet most simply don’t. It’s no wonder prospective customers are becoming skeptical. But Selector Analytics is not your garden variety AI/ML platform. We really are breaking the mold with our solution.  We identified a problem that needed solving, we found the white space, and we delivered. As a team, we noticed that the monitoring tools out there are beginning to be commoditized. Not only that but monitoring is no longer enough – we need to move to observability. And ultimately, we need to move to action and to take into account all the myriad data sources enterprises rely upon plus multi cloud environments. It’s a complex and sophisticated landscape that we bring simplicity to.  We also break down the silos of data, of work and between departments. Our technology is groundbreaking: by merging networking, AIOps and observability into a single platform. And we’re making our solution broadly applicable to not just the problems the customer is having now, but also those they will have in the future. Our team recognizes the broad applicability but also the ability to scale. This is critical to our current and future success.  The Implementation At Selector, we are driven relentlessly by our goal of delivering total customer satisfaction. So, it was incredibly important for us to create a Customer Success team right out of the gate. In real terms, this plays out as our customers having unprecedented access to our data scientists and our resources. And because we are “people intensive,” this means customers get the best and the brightest minds along with the technology. In other words, our customers get contextual collaboration and consultative support along with the platform. And what’s more is we have our customers up and running in days.  Nothing makes me prouder of our platform and our team than hearing positive feedback from our customers. Recently, the VP of Operations of a major wireless company told me, “What Selector has done is deliver an Operations Center (NOC) in a box, which has been a target for the last 20 years for operations teams everywhere”. This is the type of feedback we’re receiving from customers and prospects and we know it’s because of the power of our team combined with the best technology.  When we built the platform, instead of trying to be everything to everyone right away, we focused on its architectural underpinnings to ensure that it could be leveraged in a wide range of use cases, and deliver the scalability organizations need to grow and move forward with their multicloud infrastructures. Our flexible approach

Tuesday, March 15, 2022Selector, a start-up based in Santa Clara, California, announced $28 million in Series A funding for its network-aware AIOps, observability and analytics for multi-cloud environments. Selector Analytics provides hyper speed-to-value for operations with usable AIOps and zero-touch analytics across any and all data.  The company was founded in 2019 by former Juniper Networks executives, Kannan Kothandaraman and Nitin Kumar.  The funding round was led by Two Bear Capital, SineWave Ventures, and Atlantic Bridge. Comcast Ventures and Azure Capital Partners are also investors in the company. This brings total fundng to $33M.  “At Selector, we have assembled a world-class team looking to transform the AIOps space. By merging networking, AIOps, and observability into a single platform, Selector is approaching the space uniquely. Our mission is to eliminate downtime and empower operations with tools to be more efficient and productive in their jobs,” said Kannan Kothandaraman, Co-Founder and CEO, Selector. “Our customers are having amazing success with the platform and are expanding to new problems they once thought unsolvable.” “Existing monitoring solutions are single-domain, manual, and complex, making rapid event correlation and analysis tedious, frustrating, and often impossible,” said Nitin Kumar, Co-Founder and CTO of Selector. “Selector abstracts all of that complexity and enables automated remediation using elastic data ingestion, declarative transformations, and data-centric machine learning.” https://www.selector.ai/ https://www.convergedigest.com/2022/03/selector-raises-28-million-for-its-aiops.html