Egnyte introduces new features to help deal with security/governance during pandemic

The pandemic has put stress on companies dealing with a workforce that is mostly — and sometimes suddenly — working from home. That has led to rising needs for security and governance tooling, something that Egnyte is looking to meet with new features aimed at helping companies cope with file management during the pandemic.

Egnyte is an enterprise file storage and sharing (EFSS) company, though it has added security services and other tools over the years.

“It’s no surprise that there’s been a rapid shift to remote work, which has I believe led to mass adoption of multiple applications running on multiple clouds, and tied to that has been a nonlinear reaction of exponential growth in data security and governance concerns,” Vineet Jain, co-founder and CEO at Egnyte, explained.

There’s a lot of data at stake.

Egnyte’s announcements today are in part a reaction to the changes that COVID has brought, a mix of net-new features and capabilities that were on its road map, but accelerated to meet the needs of the changing technology landscape.

What’s new?

The company is introducing a new feature called Smart Cache to make sure that content (wherever it lives) that an individual user accesses most will be ready whenever they need it.

“Smart Cache uses machine learning to predict the content most likely to be accessed at any given site, so administrators don’t have to anticipate usage patterns. The elegance of the solution lies in that it is invisible to the end users,” Jain said. The end result of this capability could be lower storage and bandwidth costs, because the system can make this content available in an automated way only when it’s needed.

Another new feature is email scanning and governance. As Jain points out, email is often a company’s largest data store, but it’s also a conduit for phishing attacks and malware. So Egnyte is introducing an email governance tool that keeps an eye on this content, scanning it for known malware and ransomware and blocking files from being put into distribution when it identifies something that could be harmful.

As companies move more files around it’s important that security and governance policies travel with the document, so that policies can be enforced on the file wherever it goes. This was true before COVID-19, but has only become more true as more folks work from home.

Finally, Egnyte is using machine learning for auto-classification of documents to apply policies to documents without humans having to touch them. By identifying the document type automatically, whether it has personally identifying information or it’s a budget or planning document, Egnyte can help customers auto-classify and apply policies about viewing and sharing to protect sensitive materials.

Egnyte is reacting to the market needs as it makes changes to the platform. While the pandemic has pushed this along, these are features that companies with documents spread out across various locations can benefit from regardless of the times.

The company is over $100 million ARR today, and grew 22% in the first half of 2020. Whether the company can accelerate that growth rate in H2 2020 is not yet clear. Regardless, Egnyte is a budding IPO candidate for 2021 if market conditions hold.

Juniper Networks acquires Boston-area AI SD-WAN startup 128 Technology for $450M

Today Juniper Networks announced it was acquiring smart wide area networking startup 128 Technology for $450 million.

This marks the second AI-fueled networking company Juniper has acquired in the last year and a half after purchasing Mist Systems in March 2019 for $405 million. With 128 Technology, the company gets more AI SD-WAN technology. SD-WAN is short for software-defined wide area networks, which means networks that cover a wide geographical area such as satellite offices, rather than a network in a defined space.

Today, instead of having simply software-defined networking, the newer systems use artificial intelligence to help automate session and policy details as needed, rather than dealing with static policies, which might not fit every situation perfectly.

Writing in a company blog post announcing the deal, executive vice president and chief product officer Manoj Leelanivas sees 128 Technology adding great flexibility to the portfolio as it tries to transition from legacy networking approaches to modern ones driven by AI, especially in conjunction with the Mist purchase.

“Combining 128 Technology’s groundbreaking software with Juniper SD-WAN, WAN Assurance and Marvis Virtual Network Assistant (driven by Mist AI) gives customers the clearest and quickest path to full AI-driven WAN operations — from initial configuration to ongoing AIOps, including customizable service levels (down to the individual user), simple policy enforcement, proactive anomaly detection, fault isolation with recommended corrective actions, self-driving network operations and AI-driven support,” Leelanivas wrote in the blog post.

128 Technologies was founded in 2014 and raised over $97 million, according to Crunchbase data. Its most recent round was a $30 million Series D investment in September 2019 led by G20 Ventures and The Perkins Fund.

In addition to the $450 million, Juniper has asked 128 Technology to issue retention stock bonuses to encourage the startup’s employees to stay on during the transition to the new owners. Juniper has promised to honor this stock under the terms of the deal. The deal is expected to close in Juniper’s fiscal fourth quarter subject to normal regulatory review.

Private equity firms can offer enterprise startups a viable exit option

Four years ago, Ping Identity was at a crossroads. A venerable player in the single sign-on market, its product was not a market leader, and after 14 years and $128 million in venture capital, it needed to find a new path.

While the company had once discussed an IPO, by 2016 it began putting out feelers for buyers. Vista Equity Partners made a $600 million offer and promised to keep building the company, something that corporate buyers wouldn’t guarantee. Ping CEO and co-founder Andre Durand accepted Vista’s offer, seeing it as a way to pay off his investors and employees and exit the right way. Even better, his company wasn’t subsumed into a large entity as likely would have happened with a typical M&A transaction.

As it turned out, the IPO-or-acquisition question wasn’t an either/or proposition. Vista continued to invest in the company, using small acquisitions like UnboundID and Elastic Beam to fill in its roadmap, and Ping went public last year. The company’s experience shows that private equity offers a reasonable way for mature enterprise startups with decent but not exceptional growth — like the 100% or more venture firms tend to favor — to exit, pay off investors, reward employees and still keep building the company.

But not everyone that goes this route has a tidy outcome like Ping’s. Some companies get brought into the P/E universe where they replace the executive team, endure big layoffs or sell off profitable pieces and stop investing in the product. But the three private equity firms we spoke to — Vista Equity, Thoma Bravo and Scaleworks — all wanted to see their acquisitions succeed, even if they each go about it differently.

Viable companies with good numbers

Google Analytics update uses machine learning to surface more critical customer data

If you ever doubted the hunger brands have for more and better information about consumers, you only need to look at Twilio buying customer data startup Segment this week for $3.2 billion. Google sees this the same as everyone else, and today it introduced updates to Google Analytics to help companies understand their customers better (especially in conjunction with related Google tools).

Vidhya Srinivasan, vice president of measurement, analytics and buying platforms at Google, wrote in a company blog post introducing the new features that the company sees this changing customer-brand dynamic due to COVID, and it wants to assist by adding new features that help marketers achieve their goals, whatever those may be.

One way to achieve this is by infusing Analytics with machine learning to help highlight data automatically that’s important to marketers using the platform. “[Google Analytics] has machine learning at its core to automatically surface helpful insights and gives you a complete understanding of your customers across devices and platforms,” Srinivasan wrote in the blog post.

The idea behind the update is to give marketers access to more information they care about most by using that machine learning to surface data like which groups of customers are most likely to buy and which are most likely to churn, the very types of information marketing (and sales) teams need to try make proactive moves to keep customers from leaving or conversely turning those ready to buy into sales.

Google_Analytics_predictive_metric predict churn and most likely to convert to sales.

Image Credits: Google

If it works as described, it can give marketers a way to measure their performance with each customer or group of customers across their entire lifecycle, which is especially important during COVID when customer needs are constantly changing.

Of course, this being a Google product it’s designed to play nicely with Google Ads, YouTube and other tools like Gmail and Google Search, along with non-Google channels. As Srinivasan wrote:

The new approach also makes it possible to address longtime advertiser requests. Because the new Analytics can measure app and web interactions together, it can include conversions from YouTube engaged views that occur in-app and on the web in reports. Seeing conversions from YouTube video views alongside conversions from Google and non-Google paid channels, and organic channels like Google Search, social, and email, helps you understand the combined impact of all your marketing efforts.

The company is also trying to futureproof analytics with an eye toward stricter privacy laws like GDPR in Europe or CCPA in California by using modeling to fill in gaps in information when you can’t use cookies or other tracking software.

All of this is designed to help marketers, caught in trying times with a shifting regulatory landscape, to better understand customer needs and deliver them what they want when they want it — when they’re just trying to keep the customers satisfied.

Atlassian Smarts adds machine learning layer across the company’s platform of services

Atlassian has been offering collaboration tools, often favored by developers and IT for some time with such stalwarts as Jira for help desk tickets, Confluence to organize your work and BitBucket to organize your development deliverables, but what it lacked was machine learning layer across the platform to help users work smarter within and across the applications in the Atlassian family.

That changed today, when Atlassian announced it has been building that machine learning layer called Atlassian Smarts, and is releasing several tools that take advantage of it. It’s worth noting that unlike Salesforce, which calls its intelligence layer Einstein or Adobe, which calls its Sensei; Atlassian chose to forgo the cutesy marketing terms and just let the technology stand on its own.

Shihab Hamid, the founder of the Smarts and Machine Learning Team at Atlassian, who has been with the company 14 years, says that they avoided a marketing name by design. “I think one of the things that we’re trying to focus on is actually the user experience and so rather than packaging or branding the technology, we’re really about optimizing teamwork,” Hamid told TechCrunch.

Hamid says that the goal of the machine learning layer is to remove the complexity involved with organizing people and information across the platform.

“Simple tasks like finding the right person or the right document becomes a challenge, or at least they slow down productivity and take time away from the creative high-value work that everyone wants to be doing, and teamwork itself is super messy and collaboration is complicated. These are human challenges that don’t really have one right solution,” he said.

He says that Atlassian has decided to solve these problems using machine learning with the goal of speeding up repetitive, time-intensive tasks. Much like Adobe or Salesforce, Atlassian has built this underlying layer of machine smarts, for lack of a better term, that can be distributed across their platform to deliver this kind of machine learning-based functionality wherever it makes sense for the particular product or service.

“We’ve invested in building this functionality directly into the Atlassian platform to bring together IT and development teams to unify work, so the Atlassian flagship products like JIRA and Confluence sit on top of this common platform and benefit from that common functionality across products. And so the idea is if we can build that common predictive capability at the platform layer we can actually proliferate smarts and benefit from the data that we gather across our products,” Hamid said.

The first pieces fit into this vision. For starters, Atlassian is offering a smart search tool that helps users find content across Atlassian tools faster by understanding who you are and how you work. “So by knowing where users work and what they work on, we’re able to proactively provide access to the right documents and accelerate work,” he said.

The second piece is more about collaboration and building teams with the best personnel for a given task. A new tool called predictive user mentions helps Jira and Confluence users find the right people for the job.

“What we’ve done with the Atlassian platform is actually baked in that intelligence, because we know what you work on and who you collaborate with, so we can predict who should be involved and brought into the conversation,” Hamid explained.

Finally, the company announced a tool specifically for Jira users, which bundles together similar sets of help requests and that should lead to faster resolution over doing them manually one at a time.

“We’re soon launching a feature in JIRA Service Desk that allows users to cluster similar tickets together, and operate on them to accelerate IT workflows, and this is done in the background using ML techniques to calculate the similarity of tickets, based on the summary and description, and so on.”

All of this was made possible by the company’s previous shift  from mostly on-premises to the cloud and the flexibility that gave them to build new tooling that crosses the entire platform.

Today’s announcements are just the start of what Atlassian hopes will be a slew of new machine learning-fueled features being added to the platform in the coming months and years.

Twilio’s $3.2B Segment acquisition is about helping developers build data-fueled apps

The pandemic has forced businesses to change the way they interact with customers. Whether it’s how they deliver goods and services, or how they communicate, there is one common denominator, and that’s that everything is being forced to be digitally driven much faster.

To some extent, that’s what drove Twilio to acquire Segment for $3.2 billion today. (We wrote about the deal over the weekend. Forbes broke the story last Friday night.) When you get down to it, the two companies fit together well, and expand the platform by giving Twilio customers access to valuable customer data. Chee Chew, Twilio’s chief product officer, says while it may feel like the company is pivoting in the direction of customer experience, they don’t necessarily see it that way.

“A lot of people have thought about us as a communications company, but we think of ourselves as a customer engagement company. We really think about how we help businesses communicate more effectively with their customers,” Chew told TechCrunch.

Laurie McCabe, co-founder and partner at SMB Group, sees the move related to the pandemic and the need companies have to serve customers in a more fully digital way. “More customers are realizing that delivering a great customer experience is key to survive through the pandemic, and thriving as the economy recovers — and are willing to spend to do this even in uncertain times,” McCabe said.

Certainly Chew recognized that Segment gives them something they were lacking by providing developers with direct access to customer data, and that could lead to some interesting applications.

“The data capabilities that Segment has are providing a full view of the customer. It really layers across everything we do. I think of it as a horizontal add across the channels and extending beyond. So I think it really helps us advance in a different sort of way […] towards getting the holistic view of the customer and enabling our customers to build intelligence services on top,” he said.

Brent Leary, founder and principal analyst at CRM Essentials, sees Segment helping to provide a powerful data-fueled developer experience. “This move allows Twilio to impact the data-insight-interaction-experience transformation process by removing friction from developers using their platform,” Leary explained. In other words, it gives developers that ability that Chew alluded to, to use data to build more varied applications using Twilio APIs.

Paul Greenberg, author of CRM at the Speed of Light, and founder and principal analyst at 56 Group, agrees, saying, “Segment gives Twilio the ability to use customer data in what is already a powerful unified communications platform and hub. And since it is, in effect, APIs for both, the flexibility [for developers] is enormous,” he said.

That may be so, but Holger Mueller, an analyst at Constellation Research, says the company has to be seeing that the pure communication parts of the platform like SMS are becoming increasingly commoditized, and this deal, along with the SendGrid acquisition in 2018, gives Twilio a place to expand its platform into a much more lucrative data space.

“Twilio needs more growth path and it looks like its strategy is moving up the stack, at least with the acquisition of Segment. Data movement and data residence compliance is a huge headache for enterprises when they build their next generation applications,” Mueller said.

As Chew said, early on the problems were related to building SMS messages into applications and that was the problem that Twilio was trying to solve because that’s what developers needed at the time, but as it moves forward, it wants to provide a more unified customer communications experience, and Segment should help advance that capability in a big way for them.

Twilio is buying customer data startup Segment for between $3B and $4B

Sources have told TechCrunch that Twilio intends to acquire customer data startup Segment for between $3 and $4 billion. Forbes broke the story on Friday night, reporting a price tag of $3.2 billion.

We have heard from a couple of industry sources that the deal is in the works and could be announced as early as Monday.

Twilio and Segment are both API companies. That means they create an easy way for developers to tap into a specific type of functionality without writing a lot of code. As I wrote in a 2017 article on Segment, it provides a set of APIs to pull together customer data from a variety of sources:

Segment has made a name for itself by providing a set of APIs that enable it to gather data about a customer from a variety of sources like your CRM tool, customer service application and website and pull that all together into a single view of the customer, something that is the goal of every company in the customer information business.

While Twilio’s main focus since it launched in 2008 has been on making it easy to embed communications functionality into any app, it signaled a switch in direction when it released the Flex customer service API in March 2018. Later that same year, it bought SendGrid, an email marketing API company for $2 billion.

Twilio’s market cap as of Friday was an impressive $45 billion. You could see how it can afford to flex its financial muscles to combine Twilio’s core API mission, especially Flex, with the ability to pull customer data with Segment and create customized email or ads with SendGrid.

This could enable Twilio to expand beyond pure core communications capabilities and it could come at the cost of around $5 billion for the two companies, a good deal for what could turn out to be a substantial business as more and more companies look for ways to understand and communicate with their customers in more relevant ways across multiple channels.

As Semil Shah from early stage VC firm Haystack wrote in the company blog yesterday, Segment saw a different way to gather customer data, and Twilio was wise to swoop in and buy it.

Segment’s belief was that a traditional CRM wasn’t robust enough for the enterprise to properly manage its pipe. Segment entered to provide customer data infrastructure to offer a more unified experience. Now under the Twilio umbrella, Segment can continue to build key integrations (like they have for Twilio data), which is being used globally inside Fortune 500 companies already.

Segment was founded in 2011 and raised over $283 million, according to Crunchbase data. Its most recent raise was $175 million in April on a $1.5 billion valuation.

Twilio stock closed at $306.24 per share on Friday up $2.39%.

Segment declined to comment on this story. We also sent a request for comment to Twilio, but hadn’t heard back by the time we published.  If that changes, we will update the story.

How Roblox completely transformed its tech stack

Picture yourself in the role of CIO at Roblox in 2017.

At that point, the gaming platform and publishing system that launched in 2005 was growing fast, but its underlying technology was aging, consisting of a single data center in Chicago and a bunch of third-party partners, including AWS, all running bare metal (nonvirtualized) servers. At a time when users have precious little patience for outages, your uptime was just two nines, or less than 99% (five nines is considered optimal).

Unbelievably, Roblox was popular in spite of this, but the company’s leadership knew it couldn’t continue with performance like that, especially as it was rapidly gaining in popularity. The company needed to call in the technology cavalry, which is essentially what it did when it hired Dan Williams in 2017.

Williams has a history of solving these kinds of intractable infrastructure issues, with a background that includes a gig at Facebook between 2007 and 2011, where he worked on the technology to help the young social network scale to millions of users. Later, he worked at Dropbox, where he helped build a new internal network, leading the company’s move away from AWS, a major undertaking involving moving more than 500 petabytes of data.

When Roblox approached him in mid-2017, he jumped at the chance to take on another major infrastructure challenge. While they are still in the midst of the transition to a new modern tech stack today, we sat down with Williams to learn how he put the company on the road to a cloud-native, microservices-focused system with its own network of worldwide edge data centers.

Scoping the problem

Blissfully expands from SaaS management into wider IT services aimed at midmarket

When Blissfully launched in 2016, it was focused on helping companies understand their SaaS usage inside their organizations, but over time the company has seen that there is a wider need, especially in midmarket companies, and today it announced it was expanding into broader IT management.

Company co-founder and CEO Ariel Diaz says that the startup began helping to track SaaS usage, eventually expanding into employee onboarding and exiting, and today they are expanding into a broader set of IT services.

“Our vision when starting a company was really that IT is being redefined in the age of SaaS. So step one was to help with everything around managing SaaS. And step two is what does that mean in terms of the broader IT management vision,” Diaz told TechCrunch.

Blissfully believed that SaaS was going to take a bigger and bigger part of IT in terms of mindshare, spend and how you manage it, and they turned out to be right. Now, they felt the time is right to expand their original idea to encompass more of the IT management function.

That has resulted in a newly expanded platform they are releasing today that not only includes the earlier SaaS management components that it’s been providing all along, but also four other new categories.

For starters they are offering IT asset management. “We are now offering the ability to track not just SaaS applications, but all your IT assets including hardware devices and traditional software,” Diaz said.

Next, they are including help desk management and ticketing capabilities to handle requests that fall outside of their SaaS management workflows. In addition, they are adding role-based access control to allow different people access to various IT management services, which is increasingly essential during the pandemic as people are being forced to troubleshoot and manage various IT issues from home. Finally, the startup is opening up its APIs so that IT can tap into that and build customized functionality or workflows on top of the Blissfully platform.

Diaz believes that the company has reached a point of maturity when it comes to SaaS management, and they saw a need in the midmarket to provide these additional IT services that larger organizations tend to get from a company like ServiceNow.

The new services will be available starting today from Blissfully.

As IBM spins out legacy infrastructure management biz, CEO goes all in on the cloud

When IBM announced this morning that it was spinning out its legacy infrastructure services business, it was a clear signal that new CEO Arvand Krishna, who took the reins in April, was ready to fully commit his company to the cloud.

The move was a continuation of the strategy the company began to put in place when it bought Red Hat in 2018 for the princely sum of $34 billion. That purchase signaled a shift to a hybrid-cloud vision, where some of your infrastructure lives on-premises and some in the cloud — with Red Hat helping to manage it all.

Even as IBM moved deeper into the hybrid cloud strategy, Krishna saw the financial results like everyone else and recognized the need to focus more keenly on that approach. In its most recent earnings report overall IBM revenue was $18.1 billion, down 5.4% compared to the year-ago period. But if you broke out just IBM’s cloud and Red Hat revenue, you saw some more promising results: cloud revenue was up 30 percent to $6.3 billion, while Red Hat-derived revenue was up 17%.

Even more, cloud revenue for the trailing 12 months was $23.5 billion, up 20%.

You don’t need to be a financial genius to see where the company is headed. Krishna clearly saw that it was time to start moving on from the legacy side of IBM’s business, even if there would be some short-term pain involved in doing so. So the executive put his resources into (as they say) where the puck is going. Today’s news is a continuation of that effort.

The managed infrastructure services segment of IBM is a substantial business in its own right, but Krishna was promoted to CEO to clean house, taking over from Ginni Rometti to make hard decisions like this.

While its cloud business is growing, Synergy Research data has IBM public cloud market share mired in single digits with perhaps 4 or 5%. In fact, Alibaba has passed its market share, though both are small compared to the market leaders Amazon, Microsoft and Google.

Like Oracle, another legacy company trying to shift more to the cloud infrastructure business, IBM has a ways to go in its cloud evolution.

As with Oracle, IBM has been chasing the market leaders — Google at 9%, Microsoft 18% and AWS with 33% share of public cloud revenue (according to Synergy) — for years now without much change in its market share. What’s more, IBM competes directly with Microsoft and Google, which are also going after that hybrid cloud business with more success.

While IBM’s cloud revenue is growing, its market share needle is stuck and Krishna understands the need to focus. So, rather than continue to pour resources into the legacy side of IBM’s business, he has decided to spin out that part of the company, allowing more attention for the favored child, the hybrid cloud business.

It’s a sound strategy on paper, but it remains to be seen if it will have a material impact on IBM’s growth profile in the long run. He is betting that it will, but then what choice does he have?