NetSpring raises $13M to fuel operational intelligence suite

With a platform focused on operational intelligence and $13 million in Series A funding, analytics startup NetSpring emerged from stealth on Tuesday.

The vendor, founded in late 2019 and based in Redwood City, Calif., is the brainchild of some of the same founders who started business intelligence vendor ThoughtSpot in 2012, including Vijay Ganesan, Priyendra Deshwal, Satyam Shekhar and Abhishek Rai.

Operational intelligence, according to Ganesan, NetSpring's CEO, is analytics on real-time data. While business intelligence analyzes historical data, operational intelligence analyzes data that is no more than a day old. The goal of operational intelligence is to enable agility amid constantly changing conditions.

Whether a retail company needs insights into its customer patterns as COVID-19 cases ebb and flow, or an energy company needs to monitor every inch of its grid, real-time data is needed to quickly act and react to any changes. And it will be the analytics vendors that can provide a real-time lens to view event data that will move the market forward.

With NetSpring having secured $13 million in new funding led by Dell Technologies Capital and the startup emerging from stealth, Ganesan recently discussed NetSpring's conviction that operational intelligence is the future of analytics.

In addition, he spoke about how NetSpring plans to use its capital funding, its product roadmap and even some of the analytics trends converging to make now the right time for a startup focused on operational intelligence.

NetSpring calls itself an operational intelligence platform. How do you define operational intelligence?

Vijay Ganesan

Vijay Ganesan: Operational intelligence is an emerging category in the analytics space. It's essentially defined as analytics on event data -- that's the simple way of saying it. The other way of saying it is that it's analytics platforms that help enterprises get insights from recent data that can impact business outcomes very quickly.

We think of the data spectrum being broken into two halves. There's the downstream half, which is the half with data warehouses and business intelligence and is reporting on historical data. That's what we did at ThoughtSpot, and what Looker and Tableau and other companies do. It's very important, and remains an important aspect of analytics. Operational intelligence is the upstream half. It's closer to where data is born. It's recent data, event data, raw transaction-level data where there is time-criticality of insights -- if you don't get insights from the data quickly, the value is lost. Operational intelligence is analytics on recent data.

In terms of timing, what's the divide between business intelligence and operational intelligence?

Ganesan: I look at a day as the boundary separating the two. If you're looking at seconds, minutes, hours, you're in the world of operational intelligence. If you are looking at a more strategic long-term horizon -- days, weeks, months -- you're in the world of business intelligence.

The term operational intelligence is not a very well-understood term the way business intelligence is understood. People have different meanings for it and we're trying to evangelize what it should mean. If you look at a company like Splunk, it is also an operational intelligence platform, but Splunk is focused more on IT infrastructure. We think of ourselves as doing the same thing as observability platforms, but doing it for business data. If your website goes down, someone will find out through a Datadog or another infrastructure monitoring tool, but if your business metrics are going south, who's tracking that? That's where we come in. It's business observability.

With its platform, NetSpring aims to enable organizations to be more agile -- how does operational intelligence accomplish that?

Ganesan: There are two aspect of agility -- there's efficiency and profitability. Efficiency is all about very quickly detecting anomalous patterns and being able to react to them. One of the trigger points for the idea for NetSpring was a Fortune 500 telecom company where they had millions of customers interacting with properties, and it would take them a week to find out that certain customers were having trouble adding a line or making a payment. If their website went down, someone would find out, but if a customer abandoned a shopping cart [they wouldn't know]. Being able to track these things quickly and alert folks and help discover root causes is part of the efficiency of the organization. And that has a bearing on profitability. If 500,000 customers are taking three hours to do something, that affects the bottom line.

So one aspect is around detecting problems. The other is around opportunity. Based on patterns of usage, there may be opportunities to upsell customers, for example. If you detect that a customer's activity is increasing and usage is increasing, that's a chance to upsell and make a profit.

As you emerge from stealth, who are some established vendors who also focus on operational intelligence?

Ganesan: This is still an emerging area, but there are three buckets of competitors in this landscape. One is the application performance management vendors, like Splunk, AppDynamics and Datadog. They've all made attempts at business analytics and are traditional, infrastructure-oriented monitoring and observability companies. Then, there is a class of product analytics/customer experience companies like Amplitude and other folks who are very focused on product implementation and event streams for user clicks. The third category is a bunch of startups focused in this real-time OLAP (online analytical processing) space, companies that are built on Apache Druid and Apache Pinot. They're coming at it from a data platform perspective. They're stream-processing systems, real-time slice-and-dice analytics platforms.

They're all converging toward this North Star of what we call operational intelligence, which is a combination of a special kind of a data platform and an application platform to be able to operationalize these use cases.

We are bringing the rigor of rich BI-style analytics, which we are calling state-oriented analytics, and we're mixing it with real-time monitoring. That's what customers need and what is our differentiation.

How will NetSpring be able to differentiate itself from those established vendors already dabbling in operational intelligence?

Ganesan: There are two key pieces of differentiation that we bring to the table. One is a complete platform. We have brought together the worlds of streaming, batch and storage into a converged platform. If you look at a data warehouse like Snowflake, it's batch and storage. If you look at streaming platforms, it's batch and stream. What you need for these types of use cases is an integrated streaming, batch and storage engine.

The second aspect is an integrated application and a data platform. A lot of folks are either an app platform or a data platform. One key aspect of our platform is what we call this convergence of event-oriented analytics and state-oriented analytics. If you look at the world of event-oriented systems, they're very simplistic from an analytics point of view. If you look at them through a BI-style lens, the kinds of analytics you can do is simplistic. We are bringing the rigor of rich BI-style analytics, which we are calling state-oriented analytics, and we're mixing it with real-time monitoring. That's what customers need and what is our differentiation.

What will the $13 million you raised in Series A funding enable you to do that you haven't been able to do so far?

Ganesan: There are two big areas where we will use this investment. One is in building up the go-to-market function. So far, our investment has mostly been on product development and engineering, so now we're bringing on a sales leader, a marketing leader, and building up the go-to-market machinery. The second part of it is around the SaaS machinery, what it takes to build an enterprise-class scalable SaaS service. That will include a freemium capability.

These are high-end, mission-critical systems that are tracking, for example, oil and gas data that is coming from sensors. This has to be available 24/7 and cannot go down for a second. It can't miss a single event. These are high-end systems that require a lot of muscle to run at scale when you have hundreds of customers, so building up the SaaS machinery for that is part of the investment.

You mentioned customers -- as you enter the market, how many customers do you have so far?

Ganesan: Right now we have less than a dozen customers -- it's still early. But we have some very large Fortune 500 companies that run production workloads, so we've proven that [our platform] is enterprise-class and can perform at scale.

ThoughtSpot's Series A funding was a little more than $10 million -- do you feel like NetSpring is similar to where ThoughtSpot was a decade or so ago when it was just getting started?

Ganesan: Every company's trajectory is different, but ours is similar to ThoughtSpot. We're broadly in the same area of data and we're selling to large enterprises. The trajectory tends to be similar, but at ThoughtSpot we went after a fairly well-established market. BI was very well-invested. Operational intelligence is a new market, so there are different challenges. Also, technology has moved along a lot in the last 10 years and it is a very different world out there. How you build and how fast you can build is very different than we were at ThoughtSpot.

Now that you have $13 million in capital funding, what does NetSpring's roadmap look like for the next year?

Ganesan: The number one initiative we're doing from a product perspective is what we're calling library templates. What we've built so far is a generic, broad platform that can service a variety of use cases, which is great because we're not building a use case-specific company; we're building a broad company. But the next phase is building use case-specific tailored templates that are very easy for people to get started with. If you are studying retention, conversion, churn around customer experience, you get these ready-made templates that you launch and then five minutes later you have the analysis going. Library templates is a big initiative that we think will help people get started very easily. You can enter the platform without having to learn a whole new platform, but instead come in and see familiar patterns you're already used to.

The second part is the cloud machinery. Being able to run equally well on multiple clouds is something that's becoming very important.

What are some use cases the library templates will address?

Ganesan: One of the initial ones is product and behavior analytics. Anybody that has a consumer-facing website, for example, has to have an understanding of how users are interacting with the system, where they're having issues and how to react to them. That is a massively growing area that's very underserved. A second one is for what is called data observability. Enterprises track key business metrics, and half the time when something is off with the metrics it's because of a data pipeline issue. It has nothing to do with the business; the business is fine. Observing patterns in your data pipelines and understanding problems with the pipeline, and then correlating those patterns with the business metrics that are impacted, is an area that is a huge problem. When you're talking about monitoring business metrics, you have to have that in place to understand that problems have to do with data pipelines.

These are things you can already do with our platform today, but you will be able to do much easier with the library templates.

Why is now the time to emerge with a company focused on operational intelligence?

Ganesan: If you look at a broad trend in the data and analytics space, we feel like there's a big shift happening, and it's only in conjunction with big shifts that big companies are born.

One big shift is the data lakehouse-style architecture. That's emerging, and we're big believers in that. Databricks is spearheading that movement, and we think that's where the world is moving. We also think the face of analytics is changing. We've been doing analytics the same way for the last 25 or 30 years. It's been reporting-oriented. Now, it's becoming more operational. And then, of course, there's the shift to the cloud. We think this is an inflection point in the world of data, so we feel the timing is right to create a very good company.

Editor's note: This Q&A has been edited for clarity and conciseness.