Enterprise startups have several viable exit strategies: Some will go public, but most successful outcomes will be via acquisition, often by one of the highly acquisitive large competitors like Salesforce, Microsoft, Amazon, Oracle, SAP, Adobe or Cisco.

From rivals to “spin-ins,” Cisco has a particularly rich history of buying its way to global success. It has remained quite active, acquiring more than 30 startups over the last four years for a total of 229 over the life of the company. The most recent was Epsagon earlier this month, with five more in its most recent quarter (Q4 FY2021): Slido, Sedona Systems, Kenna Security, Involvio and Socio. It even announced three of them in the same week.

What’s the secret sauce? How it is going faster than ever? For startups that encounter a company like Cisco, what do you need to know if you have talks that go places with it? We spoke to the company CFO, senior vice president of corporate development, and the general manager and executive vice president of security and collaboration to help us understand how all of the pieces fit together, why they acquire so many companies and what startups can learn from their process.

Cisco, as you would expect, has developed a rigorous methodology over the years to identify startups that could fit its vision. That involves product, of course, but also team and price, all coming together to make a successful deal. From targeting to negotiating to closing to incorporating the company into the corporate fold, a startup can expect a well-tested process.

Even with all this experience, chances are it won’t work perfectly every time. But since Cisco started doing M&A nine years into its history with the purchase of LAN switcher Crescendo Communications in 1993 — leading to its massive switching business today — the approach clearly works well enough that they keep doing it.

It starts with cash

If you want to be an acquisitive company, chances are you have a fair amount of cash on hand. That is certainly the case with Cisco, which currently has more than $24.5 billion in cash and equivalents, albeit down from $46 billion in 2017.

CFO Scott Herren says that the company’s cash position gives it the flexibility to make strategic acquisitions when it sees opportunities.

“We generate free cash flow net of our capex in round numbers in the $14 billion a year range, so it’s a fair amount of free cash flow. The dividend consumes about $6 billion a year,” Herren said. “We do share buybacks to offset our equity grant programs, but that still leaves us with a fair amount of cash that we generate year on year.”

He sees acquisitions as a way to drive top-line company growth while helping to push the company’s overall strategic goals. “As I think about where our acquisition strategy fits into the overall company strategy, it’s really finding the innovation we need and finding the companies that fit nicely and that marry to our strategy,” he said.

“And then let’s talk about the deal … and does it make sense or is there a … seller price point that we can meet and is it clearly something that I think will continue to be a core part of our strategy as a company in terms of finding innovation and driving top-line growth there,” he said.

The company says examples of acquisitions that both drove innovation and top-line growth include Duo Security in 2018, ThousandEyes in 2020 and Acacia Communications this year. Each offers some component that helps drive Cisco’s strategy — security, observability and next-generation internet infrastructure — while contributing to growth. Indeed, one of the big reasons for all these acquisitions could be about maintaining growth.

Playing the match game

Cisco is at its core still a networking equipment company, but it has been looking to expand its markets and diversify outside its core networking roots for years by moving into areas like communications and security. Consider that along the way it has spent billions on companies like WebEx, which it bought in 2007 for $3.2 billion, or AppDynamics, which it bought in 2017 for $3.7 billion just before it was going to IPO. It has also made more modest purchases (by comparison at least), such as MindMeld for $125 million and countless deals that were too small to require them to report the purchase price.

Derek Idemoto, SVP for corporate development and Cisco investments, has been with the company for 100 of those acquisitions and has been involved in helping scout companies of interest. His team begins the process of identifying possible targets and where they fall within a number of categories, such as whether it allows them to enter new markets (as WebEx did), extend their markets (as with Duo Security), or acqui-hire top technical talent and get some cool tech, as they did when they purchased BabbleLabs last year.

New York City based startup NS1 got its start providing organizations with managed DNS services to help accelerate application delivery and reliability. With its new NetBox Cloud service that is being announced in preview today, NS1 is expanding its services into a new area beyond DNS. 

It can often be a challenging task for a network administrator in an enterprise to understand where all the networking infrastructure is and how it’s all supposed to be connected.  That’s a job for an emerging class of enterprise technology known as Infrastructure Resource Management (IRM) that NS1 is now jumping into. TechCrunch profiled NS1 in a wide-ranging EC-1 series last month. The company provides DNS as a service, for some of the biggest sites on the internet. DNS, or domain name system is about connecting IP addresses to domain names and NS1 has technology that helps organizations to intelligently optimize application traffic delivery. 

With its new NetBox Cloud service, NS1 is providing a managed service for NetBox which is a popular open source IRM tool that was initially built by developer Jeremy Stretch, while he was working at cloud provider DigitalOcean. Stretch joined NS1 as a distinguished engineer in April of this year, with NS1 now supporting the open source project.

Stretch recounted that at one point during his tenure at DigitalOcean he was using Microsoft Excel spreadsheets to track IP address management. Using a spreadsheet to track IP addresses doesn’t scale, so Stretch coded the initial version of NetBox in 2015 to address that need. Over the last several years, NetBox has expanded with additional capabilities that will now also help users of NS1’s NetBox Cloud service.

Stretch explained that Netbox’s role is primarily in modelling network infrastructure in an approach that provides what he referred to as a “source of truth” for network infrastructure. The basic idea is to enable organizations to model their desired state of their networks and then from that point they can draw in monitoring to verify that the operational state is the same as the desired state. 

“So the idea of this source of truth is that it is the actual documented authoritative record of what is supposed to be configured on the network,” Stretch said.

NetBox has continued to grow over the years as a popular open source tool, but it hasn’t been particularly accessible to enterprises that required commercial support to get started, or that wanted a managed service. The goal with the new service is to make it easier for organizations of any size to get started with NetBox to better manage their networks.

NS1 co-founder and CEO Kris Beevers told TechCrunch that while Stretch has done a solid job of building the NetBox open source community, there hasn’t been a commercial service for NetBox. Beevers said that while NetBox has had broad adoption as an open source effort, in his view there are a lot of enterprises that will want commercial support and a managed service.

One key theme that Beevers reiterated time and again in the Extra Crunch EC-1 series is that NS1 is very experimental as a business, and that same theme holds true for NetBox. The primary objective for the initial beta release of the NetBox Cloud is all about figuring out exactly who is trying to adopt the technology and learning what challenges commercial users will face. Fundamentally, Beevers said that NS1 will be actively iterating on NetBox Cloud to make sure it addresses the things that enterprises care about.

“From the NS1 point of view, this is just such a compelling open source product and community and we want to drive barriers to adoption as low as we possibly can,” Beevers said.

NS1 was founded in 2013 and has raised $118.4 million in funding, including a $40 million Series D which the company closed in July 2020.

Like other areas of healthcare, the dental industry is steadily embracing technology. But while much of it is in the orthodontic realm, other startups, like Adra, are bringing artificial intelligence into a dentist’s day-to-day workflow, particularly in finding cavities, of what will be a $435.08 billion global dental services market this year.

The Singapore-based company was founded in 2021, but was an idea that started last year. Co-founder Hamed Fesharaki has been a dentist for over a decade and owns two clinics in Singapore.

He said dentists learn to read X-rays in dental school, but it can take a few years to get good at it. Dentists also often have just minutes to read them as they hop between patients.

As a result, dentists end up misdiagnosing cavities up to 40% of the time, co-founder Yasaman Nematbakhsh said. Her background is in imaging, where she developed an artificial intelligence machine identifying hard-to-see cancers, something Fesharaki thought could also be applied to dental medicine.

Providing the perspective of a more experienced dentist, Adra’s intent is to make every dentist “a super dentist,” Fesharaki told TechCrunch. Its software detects cavities and other dental problems on dental X-rays faster and 25% more accurately, so that clinics can use that time to better serve patients and increase revenue.

Example of Adra’s software. Image Credits: Adra

“We are coming from the eye of an experienced dentist to help illustrate the problems by turning the X-rays into images to better understand what to look for,” he added. “Ultimately, the dentist has the final say, but we bring the experience element to help them compare and give them suggestions.”

By quickly pointing out the problem and the extent of it, dentists can decide in what way they want to treat it — for example, do a filling, a fluoride treatment or wait.

Along with third co-founder Shifeng Chen, the company is finishing up its time in Y Combinator’s summer cohort and has raised $250,000 so far. Fesharaki intends to do more formalized seed fundraising and wants to bring on more engineers to tackle user experience and add more features.

The company has a few clinics doing pilots and wants to attract more as it moves toward a U.S. Food and Drug Administration clearance. Fesharaki expects it to take six to nine months to receive the clearance, and then Adra will be able to hit the market in late 2022 or early 2023.

As more companies provide more API-first services, Moesif has developed a way for those companies to learn how their customers are utilizing them.

The San Francisco-based startup is adding to its capital raise Monday with the announcement of a $12 million Series A round led by David Sacks and Arra Malekzadeh of Craft Ventures. Existing investor Merus Capital, which led Moesif’s $3.5 million seed round in 2019, also participated in the round, bringing the company’s total raise to $15.5 million, Moesif co-founder and CEO Derric Gilling told TechCrunch.

Gilling and Xing Wang founded Moesif in 2017 and went through the Alchemist Accelerator in 2018.

Companies seeking data around API usage and workflow traditionally had to build that capability in-house on top of a tech like Snowflake, Gilling said. One of the problems with that was if someone wanted a report, the process was ad hoc, meaning they would file a ticket and wait until a team had time to run the report. In addition, companies find it difficult to accurately bill customers on usage or manage when someone exceeds the rate limits.

“We started to see people build on top of our platform and pull data on APIs, and they started asking us how to directly serve customers, like making them aware if they are hitting a rate limit,” Gilling added. “We started to build new functionality and a way to customize the look and feel of the platform.”

Moesif provides self-service analytics that can be accessed daily and features to scale analytics in a more cost-effective manner. Customers use it to monitor features to better understand when there are issues with the API, and there are additional capabilities to understand who is using the API, how often and who may be likely to stop using a product based on how they are using it.

The company is also now seeing its revenue grow over 20% month over month this year and adoption by more diverse use cases and larger companies. At the time of the seed round, the company was just getting started with analytics and user trials, Gilling said. Today, it boasts a customer list that includes UPS, Tomorrow.io, Symbl.ai and Deloitte.

The company has also gone from a team of two to nine employees, and Gilling expects to use the new funding to bolster that roster across engineering, sales, developer relations and customer success.

He is also focusing on being a thought leader in the space and is pushing go-to-market and building out a new set of features to monetize APIs and improve its dashboard to better differentiate Moesif from competitors, which he said focus more on server health versus customer usage.

As part of the investment, Craft Ventures’ Malekzadeh is joining Moesif’s board. She was introduced to Gilling by another portfolio company and felt Moesif fit into Crafts’ thesis on SaaS companies.

Malekzadeh’s particular interest is in developer tools, and while in her previous position working at a startup developing APIs, she felt firsthand the pain point of not being able to know how those APIs were being used, how much customers should be billed and “was always bugging the product and engineering teams for reports.”

Moesif didn’t exist at the time she worked at the startup, and instead, her company had to build it own tools that turned out to be clunky, while at the same time recruiting top engineers that didn’t want to take up their time with building something that wasn’t the company’s core product.

“The two founders are highly technical, but they provided great content on their website that helped me learn about them,” Malekzadeh added. “One of the interesting things about them is that even though they are technical, they speak the same language as a business user, which makes them special as a developer-first company. Just the growth in their revenue was super impressive, and their customer references were glowing.”

The Pareto principle, also known as the 80-20 rule, asserts that 80% of consequences come from 20% of causes, rendering the remainder way less impactful.

Those working with data may have heard a different rendition of the 80-20 rule: A data scientist spends 80% of their time at work cleaning up messy data as opposed to doing actual analysis or generating insights. Imagine a 30-minute drive expanded to two-and-a-half hours by traffic jams, and you’ll get the picture.

While most data scientists spend more than 20% of their time at work on actual analysis, they still have to waste countless hours turning a trove of messy data into a tidy dataset ready for analysis. This process can include removing duplicate data, making sure all entries are formatted correctly and doing other preparatory work.

On average, this workflow stage takes up about 45% of the total time, a recent Anaconda survey found. An earlier poll by CrowdFlower put the estimate at 60%, and many other surveys cite figures in this range.

None of this is to say data preparation is not important. “Garbage in, garbage out” is a well-known rule in computer science circles, and it applies to data science, too. In the best-case scenario, the script will just return an error, warning that it cannot calculate the average spending per client, because the entry for customer #1527 is formatted as text, not as a numeral. In the worst case, the company will act on insights that have little to do with reality.

The real question to ask here is whether re-formatting the data for customer #1527 is really the best way to use the time of a well-paid expert. The average data scientist is paid between $95,000 and $120,000 per year, according to various estimates. Having the employee on such pay focus on mind-numbing, non-expert tasks is a waste both of their time and the company’s money. Besides, real-world data has a lifespan, and if a dataset for a time-sensitive project takes too long to collect and process, it can be outdated before any analysis is done.

What’s more, companies’ quests for data often include wasting the time of non-data-focused personnel, with employees asked to help fetch or produce data instead of working on their regular responsibilities. More than half of the data being collected by companies is often not used at all, suggesting that the time of everyone involved in the collection has been wasted to produce nothing but operational delay and the associated losses.

The data that has been collected, on the other hand, is often only used by a designated data science team that is too overworked to go through everything that is available.

All for data, and data for all

The issues outlined here all play into the fact that save for the data pioneers like Google and Facebook, companies are still wrapping their heads around how to re-imagine themselves for the data-driven era. Data is pulled into huge databases and data scientists are left with a lot of cleaning to do, while others, whose time was wasted on helping fetch the data, do not benefit from it too often.

The truth is, we are still early when it comes to data transformation. The success of tech giants that put data at the core of their business models set off a spark that is only starting to take off. And even though the results are mixed for now, this is a sign that companies have yet to master thinking with data.

Data holds much value, and businesses are very much aware of it, as showcased by the appetite for AI experts in non-tech companies. Companies just have to do it right, and one of the key tasks in this respect is to start focusing on people as much as we do on AIs.

Data can enhance the operations of virtually any component within the organizational structure of any business. As tempting as it may be to think of a future where there is a machine learning model for every business process, we do not need to tread that far right now. The goal for any company looking to tap data today comes down to getting it from point A to point B. Point A is the part in the workflow where data is being collected, and point B is the person who needs this data for decision-making.

Importantly, point B does not have to be a data scientist. It could be a manager trying to figure out the optimal workflow design, an engineer looking for flaws in a manufacturing process or a UI designer doing A/B testing on a specific feature. All of these people must have the data they need at hand all the time, ready to be processed for insights.

People can thrive with data just as well as models, especially if the company invests in them and makes sure to equip them with basic analysis skills. In this approach, accessibility must be the name of the game.

Skeptics may claim that big data is nothing but an overused corporate buzzword, but advanced analytics capacities can enhance the bottom line for any company as long as it comes with a clear plan and appropriate expectations. The first step is to focus on making data accessible and easy to use and not on hauling in as much data as possible.

In other words, an all-around data culture is just as important for an enterprise as the data infrastructure.